Keywords: Path Planning and Motion Control
Abstract: Advanced Air Mobility (AAM) including passenger transport and Uncrewed Aircraft Systems (UAS) of all sizes requires autonomy capable of safely managing contingency responses as well as routine flight. This talk will describe pathways from aviation today to a fully autonomous AAM of the future. Research toward comprehensive low-altitude flight environment mapping and urgent landing planning will be summarized with focus on necessary geographical map data curation and processing, real-time perception, and decision algorithms. Dynamic airspace geofencing in support of UAS Traffic Management (UTM) will be defined and compared with traditional fixed airspace corridor solutions. Efforts to effectively present map and decision data to human AAM participants will be summarized.

Keywords: Unmanned Ground and Aerial Vehicles, Robotics, Control Applications
Abstract: In this work, feedback control laws for an omnidirectional screw propelled mobile robots are developed and experimentally implemented in an outdoor environment. This type of robot can navigate a wide range of natural terrain, making them useful in many field applications, such as humanitarian demining and monitoring/maintenance of green stormwater infrastructure (GSI). The contribution of this work lies in the application of nonlinear controllers for position tracking in omnidirectional screw-driven robots in an outdoor environment. This is the first step towards giving these robots the ability to operate autonomously, allowing them to survey outdoor areas. Modeling of the robot dynamics results in nonlinearities, making this system suited for nonlinear control. Model based nonlinear control laws—feedback linearization and sliding mode control laws—were tested and validated experimentally. Indoor experiments implemented using a VICON motion capture system show that both control laws enabled the robot to autonomously reach any desired final position and orientation with absolute error below 0.05m in final position. Outdoor experiments, which employ high precision GPS and IMU, were conducted on a large outdoor grass field. The results show that both control laws enabled the robot to reach any destination with absolute error below 0.5 m. Furthermore, Model adaptive control was combined with each of the control laws to successfully reduce error, leading to improved performance with absolute error below 0.25 m.

Keywords: Motion and Vibration Control, Automotive Systems, Control Applications
Abstract: Ride comfort, road handling, and fuel efficiency of a vehicle have always been key factors during vehicle suspension design. This study investigates parameter sensitivity and control of a novel energy-harvesting hydraulically interconnected suspension (EH-HIS), including 4 energy-harvesting units with each unit coupled with one shock absorber. This EH-HIS system has better performance on ride comfort and road handling than traditional suspension systems while also maintaining extra energy harvesting capability. A parameter variation study showed tradeoffs on performance metrics such as lateral acceleration and roll angle for double lane change maneuvers with fixed external resistance in energy harvesting circuits. Damping tuning via adjusting external resistance plays a critical role in impacting suspension performance and energy harvesting capability. It’s desirable to develop a control strategy for online adjustment of external resistance for further performance improvements on multiple metrics. A skyhook-based control strategy is developed and implemented. Simulation studies show online tuning of external resistance is able to reduce up to 4.2% peak acceleration and 13.8% of roll angle RMS from fixed external resistance cases, and reach an average of 69.7 watts of energy harvesting for double lane change maneuver on a C class road.

Keywords: Manufacturing Systems, Optimal Control, Modeling and Validation
Abstract: There has been increasing research interest in part-scale thermal modeling and thermal control to support the industry’s needs in fabricating sizable parts with Laser Powder Bed Fusion (L-PBF) processes. Interlayer temperature, which is the layer temperature after powder spreading but before scanning a new layer, serves as the initial temperature for scanning a new layer. It plays an important role in the melt-pool morphology and the final build quality. This work shows an experimental evaluation of a model-based part-scale optimal control of laser power to maintain interlayer temperature below a pre-set threshold such that excessive heat buildup during the build process can be mitigated to improve build quality. The optimized part-scale laser power profiles are obtained by solving a convex program with respect to the authors’ prior finite-difference, part-scale thermal model on each super layer consisting of multiple physical layers. The resulting laser power profiles are then programmed on a commercial EOS M280 system for a feedforward control to build a square-canonical part of Inconel 718, as a demonstrating example. The square-canonical geometry, originally from an America Makes Project, is selected in this work due to its complex geometric features that create overheating and interesting thermal-mechanical behavior under the default constant laser power. Real-time in-situ measurements of interlayer temperature for validating the optimal control are obtained through infrared (IR) based thermal imaging during the build process. Post-process optical micrographs are also performed to compare the melt-pool morphology under the optimized laser power profiles versus under the default constant laser power. Research findings from this experimental study confirm the efficacy of the proposed optimal thermal control in reducing potential overheating during the L-PBF build process.

Keywords: Machine Learning in modeling, estimation, and control, Power and Energy Systems
Abstract: This work introduces a physics-informed machine learning methodology that combines physical laws with data-driven approaches for modeling, estimation, and control. This hybrid method is demonstrated on a state estimation problem of Lithium-ion batteries. In particular, we combine a recurrent neural network with a battery model described by differential-algebraic equations for estimating batteries’ state of charge and show how the hybrid model effectively learns the underlining system’s dynamics and ultimately achieves superior estimation accuracy on unseen data.

Keywords: Modelling, Identification and Signal Processing, Estimation, Linear Control Systems
Abstract: In this submission, we present a novel method to separate surface electronic properties from topographic variations in scanning tunneling microscopy (STM). We utilize the closed-loop system identification method to obtain the STM plant. Then we model the STM closed-loop system to implement a Kalman filter to estimate surface conductivity. Experimental verification involves hydrogen depassivation lithography on a Si(100) − 2 × 1: H passivated surface, demonstrating an accurate estimation of surface conductivity.

Keywords: Multi-agent and Networked Systems, Unmanned Ground and Aerial Vehicles, Security and Privacy
Abstract: This research is centered around optimizing the topology of multi-agent systems (MAS) while maintaining the maximum level of trust between agents. Since the nature of the topology will affect or might cause some delay in communication, a method of analyzing and minimizing the effect of communication is applied. The multi-agent system (MAS) is a computer-based system consisting of intelligent agents that interact with each other to accomplish tasks that may be too challenging or unfeasible for a single agent or a centralized system to handle [1]. These agents can cooperate and communicate in various ways to achieve a common objective [2]. Agents in a multi-agent system (MAS) could interact and cooperate in many ways. Effective communication and cooperation among agents are critical factors in achieving consensus within a multi-agent system. However, delays in communication can create synchronization issues among the agents, which requires continuous and consistent communication. Furthermore, the impact of disruptions, such as data loss and system lag, needs to be carefully evaluated [1]. This research proposes a method for optimizing the topology while analyzing the impact of communication delays and mistrust on a multi-agent system using Laplacian matrix [3]. Unlike what Thomas et al did by employing the Lambert W function-based approach to solve delay differential equations, which have an infinite number of roots due to the delay operator, the same method will be applied after every topology optimization to check for communication delay. By computing the eigenvalues or characteristic roots of the system, the method can analytically solve the delay differential equations to analyze stability. The topology of agent interaction determines how the agents control and communicate with one another, what each agent's and the entire system's control and communication capabilities are, and how efficient the control and communications are. This paper provides an overview of different types of MAS topologic models and evaluates their benefits and drawbacks in terms of agent communication and their exposure to risk or other disturbance.

Keywords: Control Design, Linear Control Systems, Multi-agent and Networked Systems
Abstract: We present new design approaches to demonstrate how to use delay-based controllers to improve the speed of response capabilities in single-integrator multi-agent systems at the face of unintentional delays.

Keywords: Power and Energy Systems, Control of Smart Buildings and Microgrids, Machine Learning in modeling, estimation, and control
Abstract: Based on abundance, free fuel, minimum amount of greenhouse gases emissions, minimum use of water in power generation, highest safety, access to all, and ultra-low cost, photovoltaics (PV) and wind turbines are proving to be the source of generating sustainable, green, and equitable electric power for all. However, the major issue with the existing legacy alternating current (AC)-based power grid is to extend the same power flow control techniques to the new direct current (DC) based renewable generation and loads. Without affecting the existing grid, an innovative concept of the end-to-end DC power-based architecture for existing and new loads is introduced in this poster. Compared to the existing AC grid, which involves multiple AC-to-DC and DC-to-AC conversion stages to connect solar and wind farms and DC loads to the grid, our proposed architecture will save more than 25% of energy and cost. In this work, we will also demonstrate that using Artificial Intelligence based dynamic control can be implemented efficiently in local DC microgrids. We propose a hierarchical control at local, medium voltage DC distribution, and high voltage DC transmission stages to optimize power flows in an end-to-end DC grid. The purpose of the extended abstract is to show that with the proper choice of energy source, the future generation, transmission, and distribution of electrical power should be based on artificial intelligence-based direct current (DC) power networks.

Keywords: Unmanned Ground and Aerial Vehicles, Robotics, Automotive Systems
Abstract: This research poster introduces an open-source robot operating system (ROS) dataset containing recorded sensor data from field experiments conducted using a custom unmanned ground vehicle (UGV). The dataset serves as a valuable resource for testing and developing localization, navigation, and, in general, autonomy algorithms in autonomous robotics. The UGV integrates multiple sensors, including RGB video, lidar, IMU, GPS, and motor current sensors. Using the dataset, a state-of-the-art lidar-based odometry algorithm was implemented. Results show improved UGV localization performance.

Keywords: Electromechanical systems, Manufacturing Systems, Modeling and Validation

Keywords: Automotive Systems, Motion and Vibration Control, Adaptive and Learning Systems
Abstract: The streamlined powertrain of electric cars compared to ICE vehicles is one of the contributors to EVs' better energy efficiency. The EV's simple powertrain significantly reduces friction with a motor and a reduction gear set as its main components. However, this reduced friction can lead to a loss of damping in the car's dynamics, which can cause unpleasant oscillation and vibration to the passenger. One of the excitation sources in the electric car is the cogging torque of the permanent magnet synchronous motor (PMSM). This cogging torque depends not only on the design but also on external factors such as temperature and loading torque. There is room for improvement when we use adaptive control to compensate for the uncertain cogging torque. We present an adaptive control method that estimates unknown cogging torque given the rotor position sensor signals and compensates for the motor torque ripple. The control method is validated with an EV powertrain simulation that assumes uncertain motor torque ripple.

Keywords: Motion and Vibration Control, Marine Systems, Modeling and Validation
Abstract: Over the past decade, offshore wind has positioned itself as one of the most promising renewable energy markets. While this field is currently dominated by fixed-bottom wind turbines located within a limited depth range, floating turbines are showing promise as a way to capture the more developed wind profiles available in deeper waters. Currently, the main challenge with floating offshore wind is that the systems experience larger ultimate loads compared to fixed bottom turbines. These larger loads are caused by the increased motion inherent with floating structures. This study looks to analyze the effects that traditional and inerter based structural control methods can have on reducing the motion of floating offshore wind turbines. Models are developed adding tuned mass dampers (TMD) and tuned inerter dampers (TID) into the three main columns of the semisubmersible platform. Results showed that for free decay tests, heave and pitch RMS values were reduced significantly by the addition of passive structural control. The inerter based structural control consistently outperformed traditional TMD and allowed for similar performance with significantly reduced physical mass values.

Keywords: Motion and Vibration Control, Modelling, Identification and Signal Processing, Machine Learning in modeling, estimation, and control
Abstract: Vibration-based fault diagnostics in rotating machinery when combined with deep learning approaches have yielded promising results. Nevertheless, standard deep learning methods can be unreliable when faced with new data from previously unseen faults or unusual operating conditions. To overcome this challenge, a Bayesian convolutional neural network is explored in this study to diagnose faults in wind turbine gearboxes. This Bayesian statistics framework provides accurate results with low uncertainty by evaluating test data with the same training data distribution. When presented with data that it has not seen before, the network signals its uncertainty and recommends human intervention. This helps to reduce the likelihood of incorrect diagnoses resulting from wrong overconfident classifications. The study compares the performance of Bayesian and standard neural networks using a simulation-based database of acceleration signals. These signals are represented as 2D cyclic spectral coherence maps generated from a multi-body dynamic model of a five-Megawatt wind turbine. By incorporating uncertainty into the prediction results, this approach has the potential to significantly reduce false positives and improve maintenance operations' efficiency and effectiveness.

Keywords: Motion and Vibration Control, Estimation, Electromechanical systems
Abstract: This paper develops a position estimation system for a robot moving over a two-dimensional plane with three degrees of freedom. The position estimation system is based on an external rotating platform containing a permanent magnet and a monocular camera. The robot is equipped with a two-axes magnetic sensor. The rotation of the external platform is controlled using the monocular camera so as to always point at the robot as it moves over the 2D plane. The radial distance to the robot can then be obtained using a one degree of freedom nonlinear magnetic field model and a nonlinear observer. Extensive experimental results are presented on the performance of the developed system. Results show that the position of the robot can be estimated with sub-mm accuracy over a radial distance range of +/- 60 cm from the magnet.

Keywords: Motion and Vibration Control, Modeling and Validation
Abstract: We propose a new theory for fluid-structure interactions of cantilever microbeams undergoing small amplitude vibrations in viscous fluids. The method is based on the concept of nonlocal modal hydrodynamic functions that accurately capture 3D fluid loading on the structure. For short beams for which 3D effects become prominent, existing local theories based on 2D fluid approximations are inadequate to predict the dynamic response. We discuss and compare model predictions in terms of frequency response functions, modal shapes, quality factors, and added mass ratios with the predictions of the local theory, and validate our new model with experimental results.

Keywords: Motion and Vibration Control, Multi-agent and Networked Systems, Robotics
Abstract: The paper introduces the novel problem of the synchronization of a pair of identical mobile nonholonomic oscillators, the so-called Chaplygin sleighs, moving on a movable platform with springs. Each Chaplygin sleigh is actuated by a periodic torques of the same amplitude and frequency,, resulting in a limit cycle in a reduced velocity space. The frictional constraint forces couple the motion of the two Chaplygin sleighs and the platform. The limit cycles of the coupled oscillators are dependent on the relative phase of actuation on the sleighs. We show that the coupled limit cycles become identical but with anti-phase synchronization, where the amplitude and frequency of oscillations and the average translational speeds of the two sleighs become equal. Moreover, in such anti-phase synchronization, the heading angle of both sleighs converge, producing motion in a formation.

Keywords: Automotive Systems, Adaptive and Learning Systems
Abstract: This study presents an approach to decision making for autonomous driving based on reinforcement learning and shared autonomy. Game theory is used to model the interaction between the human driver and the machine, while model pre- dictive control (MPC) is used in the actual control task. The decision model used by the machine is based on Reinforcement Learning (DRL) based on Deep Q Network (DQN) derived from naturalistic driving. To verify the performance of RL based decision model, its performance is compared with performances of other decision models, namely the conventional human driver behavior model, the rule based decision model and the data- driven decision model. The mean velocities and moving distances from highway driving simulations are compared and analyzed to compare their performance. It is shown that the RL based decision model demonstrates the best performance among the decision models considered.

Keywords: Intelligent Autonomous Vehicles, Automotive Systems, Path Planning and Motion Control
Abstract: Flocking control for multi-agent automated vehicles has attracted more research interest recently. However, one significant challenge is that the common use of point-shaped virtual leaders giving uniform navigations is unsuitable for vehicle motions with varying relative positions and orientations on multi-lane roads, particularly on curved sections. Considering the practical movements of multi-agent ground vehicles, this paper proposes a novel type of polyline-shaped leader(s) that aligns with multi-lane roads. Specifically, the polyline-shaped leader is composed of line segments that consider road curvatures, different lanes, and the flocking lattice configuration. Moreover, an artificial flow guidance method is applied to provide the direction of velocity references to ensure vehicles move within their respective lanes during the formed flocking. Simulation results demonstrate that the proposed approach can successfully regulate vehicles to drive in their lanes in coordinated motion, which gives fewer structural deviations on curved roads compared to the case with the point-shaped leader.

Keywords: Automotive Systems, Adaptive and Learning Systems, Control Design
Abstract: Automated vehicles must be equipped with the ability to plan and execute trajectories in the presence of uncertainties for safe and effective navigation in both on- and off-road environments. Model predictive control (MPC) is a powerful and popular optimal control solution for both planning and control tasks, but even robust MPC solutions still suffer from decreased performance and the curse of dimensionality in the optimization problem when subject to model errors. We propose an adaptive control scheme that combines a fast, nominal MPC with our previously developed Model Reference Adaptive Control strategy in a cascaded architecture. The effectiveness of the resulting framework is demonstrated via robust and computationally efficient trajectory tracking under severe uncertainties. This study includes detailed tuning considerations and numerical evaluation with different fidelity vehicle models.

Keywords: Adaptive and Learning Systems, Optimal Control, Machine Learning in modeling, estimation, and control
Abstract: Model predictive control (MPC) is a powerful tool for planning and controlling dynamical systems due to its capacity for handling constraints and taking advantage of preview information. Nevertheless, MPC performance is highly dependent on the choice of cost function tuning parameters. In this work, we demonstrate an approach for online automatic tuning of an MPC controller with an example application to an ecological cruise control system that saves fuel by using a preview of road grade. We solve the global fuel consumption minimization problem offline using dynamic programming and find the corresponding MPC cost function by solving the inverse optimization problem. A neural network fitted to these offline results is used to generate the desired MPC cost function weight during online operation. The effectiveness of the proposed approach is verified in simulation for different road geometries.

Keywords: Path Planning and Motion Control, Intelligent Autonomous Vehicles, Transportation Systems
Abstract: In this work, we develop an eco-driving algorithm for Connected and Automated Vehicles (CAVs) to reduce their energy consumption for urban corridor scenarios. The proposed algorithm considers the uncertainty from both the adaptive traffic signals and the surrounding traffic, thereby increasing its suitability for real-world deployment. In this hierarchical approach, the higher level makes global passing decisions and computes speed targets for further intersections, while the lower level generates smooth and safe speed trajectories. The performance of the algorithm is then demonstrated for an electric vehicle model through extensive (traffic) simulations. Across these experiments, 8-12% energy savings is observed compared to the baseline algorithm.

Keywords: Intelligent Autonomous Vehicles, Robotics, Cyber physical systems
Abstract: The engineering community currently encounters significant challenges in the development of intelligent transportation algorithms that can be transferred from simulation to reality with minimal effort. This can be achieved by robustifying the algorithms using domain adaptation methods and/or by adopting cutting-edge tools that help support this objective seamlessly. This work presents AutoDRIVE, an openly accessible digital twin ecosystem designed to facilitate synergistic development, simulation and deployment of cyber-physical solutions pertaining to autonomous driving technology; and focuses on bridging the autonomy-oriented simulation-to-reality (sim2real) gap using the proposed ecosystem. In this paper, we extensively explore the modeling and simulation aspects of the ecosystem and substantiate its efficacy by demonstrating the successful transition of two candidate autonomy algorithms from simulation to reality to help support our claims: (i) autonomous parking using probabilistic robotics approach; (ii) behavioral cloning using deep imitation learning. The outcomes of these case studies further strengthen the credibility of AutoDRIVE as an invaluable tool for advancing the state-of-the-art in autonomous driving technology.

Keywords: Healthcare systems, Estimation, Biomechanical Systems
Abstract: This paper develops and evaluates a wearable sensor-based system for the estimation of three-dimensional thoracoabdominal displacements. Such estimation is useful for a number of respiratory diagnosis applications, including detection of paradoxical breathing, quantification of tidal volume, and determining efficiency of mechanical ventilation. The use of inertial sensors on the body to estimate respiratory displacements is challenging due to the tilting of the body that occurs with breathing, causing continuous changes in the gravity component at the same frequency as the breathing frequency. A method to estimate the front-to-back and side-to-side tilt angles is developed using a nonlinear observer. The global stability of the observer is analyzed using Lyapunov analysis. Example experimental results are presented using data from a supine subject at various breathing frequencies and tidal volumes. Displacement estimates are found to be typically within ±1 mm compared to a gold standard reference for most data sets.

Keywords: Assistive and Rehabilitation Robotics, Healthcare systems
Abstract: This paper introduces the design of a Parallel Exoskeleton for Wrist Tremor Suppression (PEWTS). The design features dual six degree-of-freedom (DOF) subsystems that aim to be compact and suppress tremors in both radial/ulnar deviation (RUD) and flexion/extension (FE) motions of the wrist. A linear Series Elastic Actuator is employed, to provide compactness and back drivability to the system which would make the orthoses more human-compatible. The presented study focuses on providing a fundamental understanding of the working of PEWTS and investigating its feasibility through kinematic workspace analysis.

Keywords: Human-Machine and Human-Robot Systems, Control Design, Nonlinear Control Systems
Abstract: This paper presents the design, kinematic, and nonlinear control algorithm of a 3 degrees of freedom (DOF) pneumatic haptic finger developed for elastographic imaging virtual palpation. This haptic interface allows for precise tracking of the index finger position and can provide directional force perception at the fingertip. Of the 4~DOF of the human finger, two are kinematically coupled (distal and proximal interphalangeal joints), one DOF of the metacarpophalangeal (MCP) joint is controlled independently, while the remaining MCP joint DOF is only measured. Relationships between the joint angles and actuator positions, as well as fingertip to actuator forces are derived in closed form. A sliding mode nonlinear pneumatic controller is implemented and experimental results are presented.

Keywords: Modelling and Control of Biomedical Systems, Modeling and Control of Biotechnological Systems, Nonlinear Control Systems
Abstract: This paper examines the control of abdominal perfusion during medical interventions. The goal is to avoid safety risks such as excessive retained fluid volume, tissue damage/trauma, and intra-abdominal hypertension. The paper presents a perfusion system with peristaltic pumps dictating fluid inflow/outflow rates, and uses model-based control to avoid multiple safety risks simultaneously. Specifically, the paper: (i) introduces a discharge efficiency metric reflecting the risk of tissue trauma and/or outflow cavitation; (ii) identifies a dynamic model of discharge efficiency from animal test data; and, (iii) develops a safe perfusion control algorithm based on this model. The algorithm receives three user inputs: a desired inflow rate, a desired perfused volume, and a safety bound on discharge efficiency. The algorithm minimizes the deviation of the commanded inflow/outflow rates from a nonlinear model reference controller, subject to a barrier constraint on discharge efficiency. This furnishes a switching control structure providing convergence to the desired perfusion settings in the absence of outflow occlusion, and operating safely when outflow is occluded, as shown in simulation-based validation studies.

Keywords: Healthcare systems, Electromechanical systems, Control Applications
Abstract: In this work, we use the theory of time-scale separation to analyze motion of magnetic needles powered by electromagnetic coils for surgical tasks. In developing model-based controllers for such systems, it is often assumed that the magnetic needle orientation is aligned with the magnetic field direction. This assumption has enabled use of open-loop control techniques for orientation control of the magnetic agent. We provide a framework to show how this assumption might be highly inaccurate for certain system parameters. We show numerical simulations to depict this and validate results with experiments on a real magnetic control system. The results of this work can help ascertain whether the orientation alignment assumption is valid for a given set of parameters for more accurate control design and accurate implementation of surgical tasks using magnetic needles in clinical settings.

Keywords: Optimal Control, Nonlinear Control Systems, Path Planning and Motion Control
Abstract: This paper studies the combined maneuver of flying and sailing for a robotic system which is referred to as a flying+sailing drone. Due to the emergence of hybrid systems behavior in tasks which involve both the flying and sailing modes, a hybrid systems formulation of the robotic system is presented. Key characteristics of the system are (i) changes in the dimension of the state space as the system switches from flying to sailing and vice versa and (ii) the presence of autonomous switchings triggered only upon the landing of the drone on the water surface. For the scenario in which the drone's initial state is given in the flying mode and a fixed terminal state is specified in the sailing mode, the associated optimal control problems are studied within the vertical plane passing through the given points, hence the dynamics of the drone in the flying mode are represented in a five-dimensional state space (associated with three degrees of freedom) and in a three-dimensional state space in the sailing mode (associated with two degrees of freedom). In particular, the optimal control problems for the minimization of time and the minimization of the control effort are formulated, the associated necessary optimality conditions are obtained from the Hybrid Minimum Principle (HMP), and the associated numerical simulations are presented.

Keywords: Stochastic Systems, Modelling and Control of Environmental Systems, Optimal Control
Abstract: The optimal operation of water reservoir systems is a challenging task involving multiple conflicting objectives. The main source of complexity is the presence of the water inflow, which acts as an exogenous, highly uncertain, disturbance on the system. When model predictive control (MPC) is employed, the optimal water release is usually computed based on the (predicted) trajectory of the inflow. This choice may jeopardize the closed-loop performance when the real inflow differs from its forecast. In this work, we consider - for the first time - a stochastic MPC approach for water reservoirs, in which the control is optimized based on a set of plausible future inflows, directly generated from past data. Such a scenario-based MPC strategy allows the controller to be more cautious, counteracting droughty periods (e.g. the lake level going below the dry limit), while at the same time guaranteeing that the agricultural water demand is satisfied. The effectiveness of the method is validated through extensive Monte Carlo tests, using real inflow data taken from lake Como, Italy.

Keywords: Stochastic Systems, Optimal Control, Control Design
Abstract: To solve the problem caused by limited communication and ubiquitous noise, we formulate the multi-player stochastic differential pursuit-evasion games based on directed graphs. This paper proposes a novel Riccati equation concerning multi-player stochastic differential games based on the linear-quadratic exponential cost function. We define capture and escape conditions for pursuers and evaders, respectively. Subsequently, the optimal strategies for pursuers and evaders are obtained based on the communication topologies and a direct method using the completion square and Radon-Nikodym derivative. Under the constraints of directed topology, the strategy presented in this article is distributed, and players do not require any global information. Besides, we provide sufficient theoretical evidence that the proposed strategy is a Nash equilibrium. Numerical simulations verify the effectiveness of the strategies in the capture scenarios and escape scenarios.

Keywords: Optimal Control, Automotive Systems, Machine Learning in modeling, estimation, and control
Abstract: Anti-locking Braking systems are crucial safety systems in modern vehicles. In this work, we investigate the possibility to use Model Predictive Control (MPC) for braking systems by considering three different models identified from data. Specifically, we consider two models, whose structure and the identification procedure are driven by physics principles, and a third black-box modeling approach that relies on Koopman theory. By comparing the effectiveness of the three resulting MPC schemes in a high-fidelity simulation environment, we show that Koopman-based MPC can generally be a viable solution for the design of braking controllers, which might not be the case of nonlinear MPC or approximated scheme like the second one we test.

Keywords: Optimal Control, Nonlinear Control Systems, Control Applications
Abstract: Hydraulic pumps are a key component in manufacturing industry and off-highway vehicles. Paired with diesel engines or electric motors, they provide hydraulic flow that can conveniently be used to power a variety of actuators. Hydraulic power transmission has numerous advantages, unfortunately energy efficiency is usually not one of those. The use of Variable Speed Variable Displacement (VSVD) pumps has been proven to be advantageous with respect to constant speed or constant displacement solutions: It allows to achieve higher efficiency and faster flow tracking dynamics. This paper presents the development of a Model Predictive Control (MPC) for this system, considering the nonlinearities and look-up-tables that characterize the system dynamics. The MPC is then compared both in simulation and on test bench with a reference controller for such system, showing potential both regarding efficiency and flow tracking dynamics.

Keywords: Assistive and Rehabilitation Robotics, Optimal Control
Abstract: This paper focuses on the application of model-based predictive control (MPC) for a full wrist exoskeleton designed for the alleviation of tremors in patients suffering from Parkinson’s Disease and Essential Tremor. The main motivation for using MPC here relies on its ability to incorporate state and input constraints, which are crucial for the user’s safety. The forearm-exoskeleton model is successively linearized at each time sample to obtain a linear state space model. The optimal input is then generated by minimizing a convex quadratic cost function. Finally, simulation cases are provided to demonstrate the effectiveness of the control scheme.

Keywords: Power and Energy Systems, Machine Learning in modeling, estimation, and control
Abstract: Accurate estimation of the state of charge (SOC) is crucial for ensuring the safe and efficient operation of lithium-ion batteries. Machine learning (ML) models can achieve high SOC estimation accuracy, but typically require large training datasets that may not always be accessible in practical applications. To address this issue, this paper proposes a hybrid model of a Transformer neural network and the physics-based Extended Single Particle Model (ESPM) for SOC estimation under limited data scenarios. The Transformer can leverage the internal battery states estimated by the ESPM when necessary and learn to use information from multiple sources (i.e., experimental data and ESPM). Two limited data scenarios, partially available cycles and varying temperatures, are evaluated with experimental battery discharge cycles to identify the conditions under which the proposed model outperforms traditional ML models. Despite being highly dependent on the ESPM's performance, the hybrid model demonstrated improved SOC estimation over the baseline models, with less than 2% error for most scenarios.

Keywords: Estimation, Modeling and Validation, Control Design
Abstract: An efficient state of charge (SOC) estimation for LiFePO4 batteries in electric vehicles (EVs) has been an open problem so far, largely due to its non-measurable nature. This paper tackles this problem by presenting a fractional-order (FO) dynamical framework to unravel and understand the inherent dynamics of the LiFePO4 battery which leads to an improved estimation of SOC. First, a FO model (FOM) is proposed where the parameters are introduced as nonlinear functionalities of SOC. It has been observed that the FO defined as a nonlinear function of SOC is crucial in identifying its progression during the weakly measurable flat, open circuit curve of the battery; a property the integer order models (IOMs) fail to capture. Second, a fractional order estimator (FOE) is designed incorporating the SOC based nonlinearities of the model parameters. The FO derivative being a memory-based operator improves estimation as it can store historical information of the speed profiles of the EV. The proposed framework of nonlinear FOM and FOE design is validated through both simulation and experimental results. Precise estimation of the battery parameters using the proposed framework can be applied to protect the battery management system, mitigate overcharge or discharge, prevent hazardous accidents, and enhance battery life, eventually leading to an energy-efficient mode of green transportation.

Keywords: Power and Energy Systems, Healthcare systems, Modelling and Control of Biomedical Systems
Abstract: This article studies the intricate relationship between implantable cardioverter defibrillators (ICDs) power consumption and their lithium battery power sources, employing a coupled electro-thermal dynamic model simulation. ICDs play a crucial role in preserving lives by addressing fatal arrhythmia disorders through precise electrical shocks. These life-saving devices rely on lithium battery power to deliver timely interventions. In this investigation, we analyze the terminal voltage, depth of discharge (DOD), and temperature dynamics of the implantable lithium battery, incorporating a combined cathode material, namely lithium/silver vanadium oxide-carbon mono fluoride (Li/SVO-CFx). Although modeling implantable battery characteristics is well-explored on previous literature, the impact of high-energy defibrillation shocks and low-energy device power supply (housekeeping) on battery performance remains an intriguing yet relatively unexplored aspect. Our analysis unravels an interesting finding: the battery terminal voltage is primarily influenced by the subtle but persistent housekeeping discharge currents in the microamp range, rather than the more intermittent high defibrillation currents in the several amp range. The implications of these findings hold promise for refining device design, control, and operation, paving the way for extended service life for patients and mitigating the necessity for invasive replacement surgeries.

Keywords: Power and Energy Systems, Machine Learning in modeling, estimation, and control, Modeling and Validation
Abstract: State estimation is essential for enabling battery management systems (BMSs) to monitor, control, and optimize battery performance. The first step toward this is the ability to predict a battery’s behavior given its input current and present state. In the early stages of new battery chemistry development, prior to commercialization, lab-fabricated battery cells might be used for characterization and BMS development. Such custom-fabricated batteries are often more prone to anomalies in their cycling behavior, including loss of connectivity and instantaneous internal shorts. The use of battery cycling data containing such anomalies can negatively affect modeling accuracy and system predictability. This paper uses the K-means clustering method to detect outlier patterns in battery cycling, thereby enabling the extraction of sanitized cycling data. A feedforward neural network is then trained to predict battery voltage one step ahead, given the input current and prior voltage history. The paper demonstrates this machine learning approach for cycling data from laboratory-fabricated lithium-sulfur (Li-S) cells. This demonstration highlights both the accuracy of the proposed voltage prediction algorithm and the degree to which the proposed outlier detection algorithm helps improve this accuracy.

Keywords: Automotive Systems, Machine Learning in modeling, estimation, and control, Estimation
Abstract: This paper aims to address the gap in the literature by proposing a machine learning-based approach to predict the battery state of charge (SOC) and the driving range of electric vehicles (EVs) in rural applications. While the literature revealed a lack of a comprehensive model that considers all major factors influencing EV range prediction, the proposed approach considers the three major classes of factors that influence EV range and SOC: vehicle parameters, driver behavior, and exploitation environment. The real-world driving cycle (RDC) data used in this study were collected from an On-Board Diagnostics (OBD) device connected to the driver's vehicle. The results of our study showed that the proposed machine learning approach was able to achieve an average accuracy of 95% in predicting the SOC of lithium-ion batteries. This high level of accuracy was achieved despite the large variations in the RDC data and the presence of noise in the measurements. The proposed machine learning approach was also able to accurately predict the Remaining Driving Range (RDR) of EVs using the predicted SOC values, with an average error of less than 2%.

Keywords: Automotive Systems, Human-Machine and Human-Robot Systems, Optimal Control
Abstract: In the past decade, the number of battery electric vehicles (BEV) on the road has been growing rapidly in response to global climate change and cyclic gasoline shortages. Due to the limited driving range of most commercial BEVs, individuals who use BEVs for long-distance travel tend to spend much more time on the road than owners of traditional internal combustion engine vehicles. To reduce travel time in long-distance trips, a social-aware trip planner is necessary to coordinate driving speed, vehicle charging, and social activities (e.g., dining, visit of places of interest). This paper formulates this travel time minimization problem into a mixed-integer programming model and utilizes Genetic Algorithm (GA) to solve for the optimal driving speed, vehicle charging, and the schedule of dining. The proposed planner is tested both in numerical simulations and in real-world experiments. Monte Carlo Simulations also are performed to give a thorough analysis on the performance of the proposed planner. The simulation results show that the proposed method outperforms the baseline on both routes. Additionally, real-world test results further validate the accuracy of the mixed-integer programming model. The proposed social-aware trip planner can be potentially instrumental in reducing the long-distance trip time for BEV users.

Keywords: Transportation Systems, Intelligent Autonomous Vehicles, Linear Control Systems
Abstract: This abstract presents the results from (Wang and Sipahi, 2023) also accepted at the MECC 2023. It addresses how the Delay Margin (DM) known as the largest delay less than which a dynamical system preserves linear stability, can be fragile against the size of the platoon of a network of vehicle dynamics. This work utilizes a MATLAB toolbox called parDMF to efficiently calculate the DM of these network dynamics over multiple trials. Here, we model the dynamics of connected human-driven vehicles (CHVs) and connected autonomous vehicles (CAVs), inspired by Bando et al. (1998); Gomez et al. (2014). Next, we define three connection policies describing how these vehicles should be wired so that they can exchange information. These policies lead to different network topologies of vehicles. The results show that the DM of the linearized dynamics of this network under certain policies is sensitive against the platoon size, i.e., the number of vehicles in the network. We reveal that to maintain a DM value robustly against the platoon size, the number of connections in the network should increase quadratically with respect to platoon size. This presents a trade-off since too many connections are not desirable as they cause information queuing up. To address this issue, we optimize the network such that the connection sparsity can be increased by weakening this quadratic relationship and without sacrificing the robustness of DM. The results show that by removing certain connections from the network, it is possible to generate sparse networks with 60% less connections and still achieve relatively large DM values. Overall, we find that connections established with farther CAV(s) that belong to different CAV clusters, that is, having CHVs in between the connected CAV pairs, is a crucial parameter that promotes larger DM. It also appears that human reaction connections alone may prevent information flow between upstream and downstream. This creates a virtual bottleneck causing information to flow only in one direction between two CHVs. Network design to some degree should focus on removing this weakness, mainly by re-wiring the vehicles with additional communication links.

Keywords: Automotive Systems, Adaptive and Learning Systems, Control Applications
Abstract: This paper proposes a personalized ground-vehicle lane-keeping assist system (LKA). To begin with, a control-oriented model unifying nonlinear vehicle-road dynamics and a linear driver lane-keeping steering angle model is derived. Subsequently, a novel LKA control strategy, alloying an L^(1+α)-reachable adaptive sliding-mode controller and a shifted-logistic-function-based smooth parameter projection scheme, is formulated. Through a non-quadratic Lyapunov redesign, the sliding manifold is asymptotically reached, throughout which the trajectory of the switching function’s value is confined inside the L^(1+α) signal space. In addition, the suggested smooth projection operator can prevent the unbounded control parameter drift whilst maintaining the control command’s smoothness. A simulation study employing the CarSim-Simulink joint platform and a cyber driver model is performed to evaluate the proposed personalized LKA adaptive controller and compare it with a linear-robust-control-based solution.

Keywords: Modelling, Identification and Signal Processing, Multi-agent and Networked Systems, Stochastic Systems
Abstract: As with other complex dynamic systems, modeling traffic systems requires an accurate understanding of how the individual components are coupled, which can be challenging without prior knowledge. Due to a lack of understanding of the nature of interactions between traffic entities, existing models rely on assumptions. The present study evaluates two information-theoretic measures and their ability to quantify and determine the nature of interactions using synthetic data generated from two structurally different traffic simulation models. These measures uncover relationships that describe these interactions without knowing the underlying model, which suggests these measures can be useful in data-driven model discovery.

Keywords: Path Planning and Motion Control, Intelligent Autonomous Vehicles, Transportation Systems
Abstract: Rapidly Exploring Random Trees (RRT) is one of the most widely used algorithms for motion planning in the field of robotics. To reduce the exploration time, RRT-Connect was introduced where two trees are simultaneously formed and eventually connected. Probabilistic RRT used the concept of position probability map to introduce goal biasing for faster convergence. In this paper, we propose a modified method to combine the pRRT and RRT-Connect techniques and obtain a feasible trajectory around the obstacles quickly. Instead of forming a single tree from the start point to the destination point, intermediate goal points are selected around the obstacles. Multiple trees are formed to connect the start, destination, and intermediate goal points. These partial trees are eventually connected to form an overall safe path around the obstacles. The obtained path is tracked using an MPC+Stanley controller which results in a trajectory with control commands at each time step. The trajectories generated by the proposed methods are more optimal and in accordance with human intuition. The algorithm is compared with the standard RRT and pRRT for studying its relative performance.

Keywords: Multi-agent and Networked Systems, Robotics, Nonlinear Control Systems
Abstract: This work presents a verifiable condition for the selection of a sufficient number of pursuers to capture a faster evader. The condition is based on the tracking performance of a multi-agent control scheme. Trajectory tracking results are provided for both the effects of the multi-agent control topology and its execution by the pursuers in the context of input saturation. To that end, nonlinear contraction theory is leveraged because it provides a unifying framework for the analysis of systems subject to bounded disturbances. Monte Carlo simulations are performed to validate the proposed condition for sufficient pursuers selection.

Keywords: Machine Learning in modeling, estimation, and control, Control Applications, Robotics
Abstract: This work improves the positioning precision of lightweight robots with series elastic actuators (SEAs). Lightweight SEA robots, along with low-impedance control, can maneuver without causing damage in uncertain, confined spaces such as inside an aircraft wing during aircraft assembly. Nevertheless, substantial modeling uncertainties in SEA robots reduce the precision achieved by model-based approaches such as inversion-based feedforward. Therefore, this article improves the precision of SEA robots around specified operating points, through a multi-input multi-output (MIMO), iterative learning control (ILC) approach. The main contributions of this article are to (i) introduce an input-weighted complex kernel to estimate local MIMO models using complex Gaussian process regression (c-GPR); (ii) develop Geršgorin-theorem-based conditions on the iteration gains for ensuring ILC convergence to precision within noise-related limits, even with errors in the estimated model; and (iii) demonstrate precision positioning with an experimental SEA robot. Comparative experimental results, with and without ILC, show around 90% improvement in the positioning precision (close to the repeatability limit of the robot) and a 10-times increase in the SEA robot’s operating speed with the use of the MIMO ILC.

Keywords: Automotive Systems, Control Applications, Machine Learning in modeling, estimation, and control
Abstract: The tire-road interaction generates vehicle driving forces, which affect vehicle performance such as maximum acceleration and stability. Sequential extended Kalman filter (S-EKF) integrated with a slope method has been used for tire-road friction coefficient estimation with its own limitations, along with several "cause-based" and "effect-based" methods. This research proposes a new stochastic-based evaluation criterion using existing vehicle sensor signals with the help of data-driven Kriging model. The proposed estimation method is validated by both CarSim simulation and experimental studies, respectively, under different road conditions. The results show that the proposed novel criterion has a strong correlation with the road friction coefficient and provide an improved tire-road friction coefficient estimation. A signal fusion estimation scheme based on both S-EKF and proposed evaluations is developed to improve estimation robustness.

Keywords: Machine Learning in modeling, estimation, and control, Multi-agent and Networked Systems, Optimal Control
Abstract: We propose a novel deep learning-based approach for the problem of continuous-time leader synchronization in graphical games on large networks. The problem setup is to deploy a distributed and coordinated swarm to track the trajectory of a leader while minimizing local neighborhood tracking error and control cost for each agent. The goal of our work is to develop optimal control policies for continuous-time leader synchronization in graphical games using deep neural networks. We discretize the agent’s model using sampling to facilitate the modification of gradient descent methods for learning optimal control policies. The distributed swarm is deployed for a certain amount of time while keeping the control input of each agent constant during each sampling period. After collecting state and input data at each sampling time during one iteration, we update the weights of a deep neural network for each agent using collected data to minimize a loss function that characterizes the agents’ local neighborhood tracking error and the control cost. A modified gradient descent method is presented to overcome existing limitations. The performance of the proposed method is compared with two reinforcement learning-based methods in terms of robustness to initial neural network weights and initial local neighborhood tracking errors, and the scalability to networks with a large number of agents. Our approach has been shown to achieve superior performance compared with the other two methods.

Keywords: Machine Learning in modeling, estimation, and control, Sensors and Actuators, Mechatronic Systems
Abstract: Structural Health Monitoring plays a crucial role in ensuring the safety and reliability of critical infrastructure, including pressure vessels involved in various applications. This research reports the damage detection of a pressure box employed in space habitat that operates in harsh environment where both structural failure and bolt joint loosening may occur. These failure modes are extremely hard to model based on first principles. We explore proper sensing mechanism and the associated inverse analysis algorithm that can elucidate the health condition of the pressure box. It is identified that piezoelectric impedance based active interrogation can provide necessary information for damage detection in such a system. Concurrently, deep learning technique leveraging spatial convolutional neural network is synthesized to analyze the acquired raw data and identify different types of damage. By training the deep learning model on a dataset of healthy and various damage scenarios, we can achieve high accuracy in identifying the presence of damage and its type. This research provides a data-driven methodology for structural damage detection using deep learning and has the potential to be extended to various systems with different failure modes.

Keywords: Automotive Systems, Machine Learning in modeling, estimation, and control, Power and Energy Systems
Abstract: Reinforcement learning (RL) can improve control performance by seeking to learn optimal control policies in the end-use environment for vehicles and other systems. To accomplish this, RL algorithms need to sufficiently explore the state and action spaces. This presents inherent safety risks, and applying RL on safety-critical systems like vehicle powertrain control requires safety enforcement approaches. In this paper, we seek control-barrier function (CBF)-based safety certificates that demarcate safe regions where the RL agent could optimize the control performance. In particular, we derive optimal high-order CBFs that avoid conservatism while ensuring safety for a vehicle in traffic. We demonstrate the workings of the high-order CBF with an RL agent which uses a deep actor-critic architecture to learn to optimize fuel economy and other driver accommodation metrics. We find that the optimized high-order CBF allows the RL-based powertrain control agent to achieve higher total rewards without any crashes in training and evaluation while achieving better accommodation of driver demands compared to previously proposed exponential barrier function filters and model-based baseline controllers.

Keywords: Sensors and Actuators, Distributed Parameter Systems, Modeling and Validation
Abstract: Series elastic actuators (SEAs) are increasingly used in robotic applications where tasks require safe human-robot interaction. Motivated by the growing need to develop SEAs with nonlinear stiffness to tradeoff between force tracking bandwidth and low output impedance, this paper presents an analytical approach to model the magnetic force/torque of a magnetic (Mag) SEA using the distributed current source method to provide a basis to derive the noncontact magnetic compliance; both translational and rotational stiffnesses are considered. The Mag-SEA model, which relaxes several common assumptions in the published literature that treat the magnetic lead screw as an ideal transformer, is numerically illustrated. The findings demonstrate that the simplified (ideal transformer) model fails to characterize the displacement-dependent magnetic force/torque and underestimates the mechanical advantage that can be capitalized from the spring-like magnetic energy of the embedded permanent magnets.

Keywords: Machine Learning in modeling, estimation, and control, Intelligent Autonomous Vehicles, Estimation
Abstract: Knowledge of the maximum friction coefficient μ_max between tire and road is necessary for implementing autonomous driving. As this coefficient cannot be measured via existing serial vehicle sensors, μ_max estimation is a challenging field in modern automotive research. In particular, model-based approaches are applied, which are limited in the estimation accuracy by the physical vehicle model. Therefore, this paper presents a data-based μ_max estimation using serial vehicle sensors. For this purpose, recurrent artificial neural networks are trained, validated, and tested based on driving maneuvers carried out with a test vehicle showing improved results compared to the model-based algorithm from previous works.

Keywords: Soft Robotics, Modeling and Validation, Mechatronic Systems
Abstract: Twisted string actuators (TSAs) generate high tendon-based (muscle-like) linear actuation and forces and have wide operational bandwidth. For these properties, TSAs are well-suited for advanced robotic and mechatronic applications, though their inclusion in soft robots has been limited. We recently employed TSAs to drive an anthropomorphic soft gripper which successfully demonstrated dexterous grasping and manipulation. While the gripper showed superior qualitative performance, the lack of modeling and control strategies limited its realization in a fully autonomous system {that could generalize well to new grasping behaviors}. As a first step toward this objective, this paper presents modeling strategies for a monolithic soft silicone finger driven by a TSA. A physics-based kinematic model is developed to predict the bending and velocity kinematics of the finger as a function of the TSA's twisting angle. An average error of 2.06degree was obtained for the kinematic bending model across different actuation behaviors and an average error of 2.95degree/s was measured for the angular velocity model.

Keywords: Modeling and Validation, Modelling, Identification and Signal Processing
Abstract: This paper investigates a suitable model for a power line conductor to explore its free vibration characteristics. For this, we compare the Euler–Bernoulli beam model of the conductor against the string model of the conductor via experimental and analytical vibration analyses. The effects of conductor parameters such as flexural rigidity, diameter, length, and tension on the natural frequencies of different modes are explored through parametric studies. We observe that the Euler–Bernoulli beam model of the conductor is a more realistic approach to examining the conductor's free vibration characteristics as compared to a string model.

Keywords: Underwater Vehicles, Modeling and Validation, Marine Systems
Abstract: This paper deals with modelling of a remotely operated towed vehicle (ROTV) and attached umbilical cable, as a means of exploration and research in the ocean. Fossen’s approach is used for modelling the ROTV using XFLR5 for airfoil analysis. The lumped mass-spring approach with fourth-order Runge-Kutta solver is used as umbilical cable model. It is shown that the mean square error exponentially increases when the segment length exceeds 4 meters, and the computation time is linearly increasing with the number of nodes. The results show that the umbilical cable model combined with the ROTV model can reproduce the output of the real data when taking modelling uncertainties into consideration.

Keywords: Modelling and Control of Environmental Systems, Agricultural Systems, Distributed Parameter Systems
Abstract: Microclimate within the canopy and at the plant surface can have a significant influence on the physiological processes of plants, as well as on the epidemiology of pathogens. Assessing greenhouse climate parameters, canopy microclimate and leaf surface microclimate is essential for developing greenhouse climate control strategies. Considering the canopy as a heterogenous porous medium and using the heat transfer theory, we developed a one-dimensional coupled elliptic system for control purposes to describe the air canopy and crop temperature, and established the existence and uniqueness of the solutions. The corresponding numerical model was derived within the finite difference framework. Simulations were conducted to evaluate the capability of the model to replicate canopy temperature for two arrangements of crops species under different behaviour and environment.

Keywords: Automotive Systems, Machine Learning in modeling, estimation, and control, Power and Energy Systems
Abstract: Low ambient temperatures significantly reduce Lithium ion batteries' (LIBs') charge/discharge power and energy capacity, and cause rapid degradation through lithium plating. These limitations can be addressed by preheating the LIB with an external heat source or by exploiting the internal heat generation through the LIB's internal impedance. Fast external heating generates large temperature gradients across the LIB due to the low thermal conductivity of the cell, while internal impedance heating (usually through AC or pulse charge/discharging) tends to be relatively slow, although it can achieve more uniform temperature distribution. This paper investigates the potential of combining externally sourced resistive film heating with bidirectional pulse heating to achieve fast preheating without causing steep temperature gradients. The LIB is modeled with the Doyle Fuller Newman (DFN) electrochemical model and 1D thermal model, and reinforcement learning (RL) is used to optimize the pulse current amplitude and film voltage concurrently. The results indicate that the optimal policy for maximizing the rate of temperature rise while limiting temperature gradients has the film heating dominate the initial phases and create the ideal conditions for pulse heating to take over. In addition, the pulse component shares the heating load and reduces the energy rating of the auxiliary power source.

Keywords: Electromechanical systems, Estimation, Automotive Systems
Abstract: This paper focuses on sensitivity analysis and optimal experiment design for calendar aging tests to find SEI (solid-electrolyte interface) degradation parameters. This parameterization facilitates predicting the Li-ion battery end-of-life. To this end, a physics-based model of SEI layer is used to develop a closed form solution for loss of Lithium inventory during calendar aging. This model is shown to match experimental data. Further, the developed model is used to find the sensitivity of LLI (loss of Lithium inventory) to SEI parameters in diffusion-limited and kinetically limited SEI patterns. Through this sensitivity analysis, we show that in kinetically limited case, performing calendar aging tests at high SOCs gives the most accurate results. For the diffusion limited case, we use optimal experiment design techniques to find the optimal calendar aging SOCs. It is also shown that, these optimal SOC values result in higher accuracy estimations compared to sub-optimal SOCs.

Keywords: Healthcare systems, Biomechanical Systems, Modelling and Control of Biomedical Systems
Abstract: This paper quantifies the degree to which one can exploit the dependence of battery resistance on state of charge (SOC) for better SOC estimation, and illustrates this improvement for a Lithium-Sulfur (Li-S) battery. This work is motivated by the poor SOC estimation accuracy for batteries with shallow slopes of open-circuit potential versus SOC. This is a well-recognized challenge for a number of batteries, including Li-S batteries in the low plateau region. For such batteries, the dependence of internal resistance on SOC can be exploited to obtain more accurate SOC estimates. Moreover, dithering the battery current to increase its mean square value can be valuable for improving SOC estimation accuracy. The paper examine these insights using Fisher information analysis, then demonstrates them insights using laboratory-fabricated Li-S coin cells. An equivalent circuit model is parameterized from the resulting experimental data, then utilized in a Monte Carlo simulation study supporting the paper’s theoretical insights. The simulation results show a reduction of up to 50% in SOC estimation error.

Keywords: Power and Energy Systems, Modeling and Validation
Abstract: To mitigate the consequences of a Li-ion battery system failure, a battery management system may only have a few minutes to respond. Rapidly discharging a battery cell significantly reduces the likelihood of thermal runaway, but requires currents comparable to an external short circuit (ESC). Ohmic heating accounts for a large part of the initial heat generation, so the discharge current should be regulated to prevent excessive temperatures. Therefore an accurate and fast model-based controller is needed to implement a fast and safe discharge. This work explores the ability of a Single Particle Model with electrolyte (SPMe) to capture the behavior of a cell undergoing an ESC. This work shows that a reduced-order model with concentration-dependent diffusion coefficients, that were parameterized under nominal operating conditions, can capture the mass transport limitations and resulting current and voltage drops. Additionally, the effects of changes that improve the numerical stability and robustness of the model and high-temperature phenomena on model error are discussed.

Keywords: Electromechanical systems, Control Applications, Estimation
Abstract: As high-energy-density solid-state cells approach maturity, battery management systems must be developed to maximize the performance of these cells by monitoring and/or controlling their mechanical stress. This paper describes the design, testing, and control of a lab-scale solid-state battery cell testing system. Solid-state lithium-sulfur cells are characterized under constant volume and constant stress conditions. The testing system also uses axial displacement feedback for mixed volume/stress control and dynamic switching between control modes as a function of state-of-charge and/or charge/discharge conditions.

Keywords: Modeling and Validation
Abstract: Predicting capacity loss and resistance growth is essential to ensure battery cycle life is suitable for a given application. This paper presents an empirical aging model for NMC/graphite cells which is parameterized using data obtained from calendar aging and accelerated cycle aging experiments. The model was trained to predict degradation under stress factors including temperature (-5◦C, 25◦C & 45◦C), charging and discharging C-rates (0.2C, 1.5C & 2C), depth of discharge (50% & 100%) and storage at various states of charge (50% & 100%). The model was validated in complex scenarios such as cycling under varying temperatures and fast charging at 1.5C followed by a realistic daily drive cycle discharge.

Keywords: Stochastic Systems, Sensors and Actuators, Modeling and Validation
Abstract: Atomic Force Microscopy (AFM) serves characterization and actuation in nanoscale applications. We study the stochastic dynamics of an AFM cantilever under tip-sample interactions represented by the Lennard–Jones and Morse potential energy functions. In both cases, we also study the contrasting dynamic effects of additive (external) and multiplicative (internal) noise. Moreover, for multiplicative noise, we study the two sub-cases arising from the Itˆo and Stratonovich interpretations of stochastic integrals. In each case, we also investigate the stochastic stability of the system by tracing the time evolution of the maximal Lyapunov exponent. Additionally, we obtain stationary probability densities for the unforced dynamics using stochastic averaging.

Keywords: Stochastic Systems, Modeling and Validation
Abstract: The COVID-19 pandemic has refocused research on mathematical modeling and analysis of epidemic dynamics. We analytically investigate a partial differential equation (PDE) based, compartmental model of spatiotemporal epidemic spread, marked by strongly nonlinear infection forces representing the infection transmission mechanism. Employing higher-order perturbation analysis and computing the local Lyapunov exponent, we observe the emergence of dynamic instabilities induced by stochastic environmental forces driving the epidemic spread. Notably, the instabilities are uncovered using third-order perturbations whilst they are not observed under second-order perturbations. Moreover, the onset of instability is more likely with increasing noise strength of the stochastic environmental forces.

Keywords: Manufacturing Systems, Modeling and Validation, Uncertain Systems and Robust Control
Abstract: This paper proposes the implementation of the PageRank algorithm as an alternative to the Analytic Network Process (ANP) for prioritizing maintenance activities in water distribution systems. We demonstrate the comparable performance of the PageRank algorithm to the ANP by comparing the results obtained from a previous conference paper that utilized the ANP for decision-making in sustainability-related problems involving water distribution systems feeding manufacturing industries. The ANP is commonly used for decision-making in complex systems, but has limitations such as subjective weighting and handling large datasets. In contrast, the PageRank algorithm, originally designed for web page ranking, offers a scalable and objective approach for analyzing complex systems. To showcase the effectiveness of the PageRank algorithm, we compare the results obtained from the ANP in our previous conference paper with the PageRank algorithm. Our findings reveal that the PageRank algorithm yields identical results to the ANP, while addressing its limitations. The results of this study demonstrate the viability and effectiveness of the PageRank algorithm in achieving identical outcomes as the ANP, with potential advantages in scalability and objectivity. The proposed implementation of the PageRank algorithm as an alternative to the ANP offers a promising approach for prioritizing maintenance activities in water distribution systems, as similar considerations can be extended to any sector of activity.

Keywords: Unmanned Ground and Aerial Vehicles, Machine Learning in modeling, estimation, and control, Intelligent Autonomous Vehicles
Abstract: Deep Reinforcement Learning (DRL) enables cognitive Autonomous Ground Vehicle (AGV) navigation utilizing raw sensor data without a-priori maps or GPS, which is a necessity in hazardous, information poor environments such as regions where natural disasters occur, and extraterrestrial planets. The substantial training time required to learn an optimal DRL policy, which can be days or weeks for complex tasks, is a major hurdle to real-world implementation in AGV applications. Training entails repeated collisions with the surrounding environment over an extended time period, dependent on the complexity of the task, to reinforce positive exploratory, application specific behavior that is expensive, and time consuming in the real-world. Effectively bridging the simulation to real-world gap is a requisite for successful implementation of DRL in complex AGV applications, enabling learning of cost-effective policies. We present AutoVRL, an open-source high fidelity simulator built upon the Bullet physics engine utilizing OpenAI Gym and Stable Baselines3 in PyTorch to train AGV DRL agents for sim-to-real policy transfer. AutoVRL is equipped with sensor implementations of GPS, IMU, LiDAR and camera, actuators for AGV control, and realistic environments, with extensibility for new environments and AGV models. The simulator provides access to state-of-the-art DRL algorithms, utilizing a python interface for simple algorithm and environment customization, and simulation execution.

Keywords: Modelling and Control of Biomedical Systems, Modelling, Identification and Signal Processing, Modeling and Validation
Abstract: This paper presents an experimentally parameterized model of the dynamics of oxygen transport in a laboratory animal that simultaneously experiences: (i) a reduction in inspired oxygen plus (ii) an increase in intra-abdominal pressure. The goal is to model the potential impact of elevated intra-abdominal pressure on oxygen transport dynamics. The model contains three compartments, namely, the animal's lungs, lower body vasculature, and upper body vasculature. The model assumes that intra-abdominal pressure affects the split of cardiac output among the two vasculature compartments and that aerobic metabolism in each compartment diminishes with severe hypoxia. Fitting this model to a laboratory experiment on an adult male Yorkshire swine using a regularized nonlinear least squares approach furnishes both physiologically plausible parameter values plus a reasonable quality of fit.

Keywords: Soft Robotics, Robotics, Modeling and Validation
Abstract: The inherent low stiffness in soft robots makes them preferable for working in close proximity to humans. However, having this low stiffness creates challenges when operating in terms of control and sensitivity to disturbances. To alleviate this issue, soft robots often have built-in stiffness tuning mechanisms that allow for controlled increases in stiffness. Additionally, redundant pneumatic manipulators can utilize antagonistic pressure to achieve identical positions under increased stiffness. In this paper, we develop a model to predict the stiffness and configuration of a pneumatic soft manipulator under different pressure inputs and external forces. The model is developed based on the physical characteristics of a soft manipulator while enabling efficient parameter estimation and computation. The efficacy of the modeling approach is supported via experimental results.