Keywords: Uncertain Systems - LPVS, Nonlinear Systems
Abstract: This paper presents an overview of research on data-driven modeling and learningbased control of nonlinear systems in linear parameter-varying (LPV) framework. Data-driven methods have been increasingly employed for modeling complex systems to compensate for the incomplete knowledge/uncertainties of the system process. Since the form of models affects the performance of the model identification and controller synthesis, LPV models have attracted increasing attention by virtue of capturing nonlinearities and time-varying behavior in a system using a linear structure. We presents an overview of data-driven identification of LPV models and place particular emphasis on kernelized machine learning and neural networks (NNs) approaches, as NNs have proven to be advantageous and facilitate online learning and uncertainty quantification of the data-driven models. While the system and environment dynamics can be learned from data to improve control performance and constraint satisfaction, the statistical nature of learning-based approaches introduces important challenges in guaranteeing robust constraint satisfaction. We point out the specific challenges and advantages of learning-based control in LPV framework besides the control of LPV systems and provide an overview of learning-based control of LPV systems, especially learning-based model predictive control (MPC) and model-based reinforcement learning (MBRL). Furthermore, we provide perspectives on future research in both data-driven modeling and control in LPV framework.

Keywords: Delay Estimation, Modeling and Identification, Sampled-Data and Delays
Abstract: A novel way of using neural networks to learn the dynamics of time delay systems from sequential data is proposed. A neural network with trainable delays is used to approximate the right hand side of a delay differential equation. We relate the delay differential equation to an ordinary differential equation by discretizing the time history and train the corresponding neural ordinary differential equation (NODE) to learn the dynamics. An example on learning the dynamics of the Mackey-Glass equation using data from chaotic behavior is given. After learning both the nonlinearity and the time delay, we demonstrate that the bifurcation diagram of the neural network matches that of the original system.

Keywords: Stabilization, Approximation Methods
Abstract: We propose a deep unfolding-based approach for stabilization of time-delay linear systems. Deep unfolding is an emerging framework for design and improvement of iterative algorithms and attracting significant attentions in signal processing. In this paper, we propose an algorithm to design a static output feedback gain for stabilizing time-delay linear systems via deep unfolding. Within the algorithm, the learning part is driven by NeuralODE developed in the community of machine learning, while the gain verification is performed with linear matrix inequalities developed in the systems and control theory. The effectiveness of the proposed algorithm is illustrated with numerical simulations.

Keywords: State-dependent Delays, Hybrid Systems and Delays, Control Design
Abstract: In this article, we investigate the optimal control problem of hybrid time-delayed systems. The time-delayed system has switched state-dependent dynamics and/or a switched performance index (PI). Conditions are given for controlling such hybrid time-delayed systems optimally. In particular, it is shown that the Euler-Lagrange equation is impulsive, and its integration provides relevant information for solving the problem directly without the necessity of parameter optimization. Simulation results confirm the interpretation of this generalized Euler-Lagrange equation.

Keywords: Robust Control, Control Design, Predictor-based Control
Abstract: The Partial Integral Equation (PIE) framework provides a unified algebraic representation for use in analysis, control, and estimation of infinite-dimensional systems. However, the presence of input delays results in a PIE representation with dependence on the derivative of the control input, dot u. This dependence complicates the problem of optimal state-feedback control for systems with input delay -- resulting in a bilinear optimization problem. In this paper, we present two strategies for convexification of the optimal state-feedback control problem for systems with input delay. In the first strategy, we use a generalization of Young's inequality to formulate a convex optimization problem, albeit with some conservatism. In the second strategy, we filter the actuator signal -- introducing additional dynamics, but resulting in a convex optimization problem without conservatism. We compare these two optimal control strategies on four example problems, solving the optimization problem using the latest release of the PIETOOLS software package for analysis, control and simulation of PIEs.

Keywords: Infinite-dimensional Systems and Delays, Time-domain Methods
Abstract: Inspired by the recently proposed Partial Integral Equality(PIE) representation for linear delay systems, this paper proposes a fuzzy-PIE representation for T-S fuzzy systems with delays for the first time. Inspired by the free-weighting matrix technique, this paper introduces the free-weighting Partial Integral (PI) operators. Based on the alternative representation and free-weighting PI operators, the stability issue is investigated for the T-S fuzzy systems with delays. The corresponding conditions are given as Linear Partial Inequality (LPI) and can be solved by the MATLAB toolbox PIETOOLS. Compared with the existing results, our method has no need of the bounding technique and a large amount of matrix operation. The numerical examples show the superiority of our method. This paper adds to the expanding field of LPI approach to fuzzy systems with delays.

Keywords: Time-domain Methods
Abstract: This paper describes: 1) the use of feed rate scheduling software to predict the radial depth of cut variation for three-axis milling toolpaths and; 2) the use of this radial depth profile in a time-domain simulation to predict dynamic cutting forces. The time-domain simulation, which also includes the tool tip frequency response functions and force model (which relates the cutting force components to the chip geometry) as inputs, enables dynamic force profiles to be predicted and parameter combinations that cause chatter to be identified. A ramp geometry is selected that provides constantly varying radial depth and force predictions are completed at multiple axial depths for comparison to measured forces.

Keywords: Delays and Vibration Control, Frequency-domain Methods, Stabilization
Abstract: The ZOA or zeroth order algorithm is the most well-known and basic reference of stability models in milling processes. This model has shown its effectiveness when dealing with large immersion milling operations. However, it presents several pitfalls when interrupted milling processes are assessed, due to the presence of period doubling chatter and mode coupling effects. This work enhances ZOA method including flip type lobes calculation. Thus, the accuracy of the prediction is increased while maintaining the fast and analytical nature of the method.

Keywords: Time-domain Methods, Delays and Vibration Control, Infinite-dimensional Systems and Delays
Abstract: Stability analysis of classical milling operations considering simple tool geometries has been presented in the past, where it is assumed that the forced vibrations do not affect the stability of the stationary solution. Tool runout is an asymmetry, which is always present in realistic operations and can change the ideal cutter-workpiece engagement. Forced vibrations can elevate the symmetry breaking effect until some cutting edges completely fly-over the material and the classical stability boundaries change drastically. In this presentation we study the effect of tool runout in numerical time-domain simulations, periodic solution iterations and experiments. The changes between regular models and the most recent models presented by the literature are verified.

Keywords: Delays and Vibration Control, Stabilization, Parameter-based Methods
Abstract: This article proposes an active control strategy to suppress self-excited coupled axial-torsional vibrations of a distributed drill-string system while the coupling takes place through the bit-rock interaction. The drill-string model is expressed as Neutral-type Delay Differential Equations (NDDEs) with constant and state-dependent state delays and constant input delays. As a first step in the novel controller design, an implementable input transformation is introduced, resulting in the elimination of the neutral terms from the equations of motion. This supports a simplified next step of stabilizing controller design. In the second step, a new analytic method named the “Eigenvector Contradiction Method” is proposed to provide sufficient conditions to ensure that all eigenvalues have real parts less than a prescribed value. Based on this criterion, an automated parametric feedback control law is designed. A case study simulation is presented to illustrate the effectiveness of the proposed control strategy.

Keywords: Delays and Vibration Control, Frequency-domain Methods, Time-domain Methods
Abstract: Chatter is one of the major limitations in milling operations. Milling tools with special geometries such as variable pitch, serrated or crest-cut forms can be very effective in suppressing chatter and increasing productivity. The paper will present various aspects related to these tools such as their design and performance in a comparative manner.

Keywords: Aerospace Dynamics, Structural Analysis, Nonlinear Systems
Abstract: Obtaining models that can be used for flight control is of outmost importance to ensure reliable guidance and navigation of spacecrafts, like a Generic Parafoil Return Vehicle (GPRV). In this paper, we convert an existing, high-fidelity nonlinear model of the atmospheric flight dynamics of a GPRV to a Linear Parameter-Varying (LPV) form that enables high-performance navigation control design. Application of existing systematic conversion methods for such complicated nonlinear models often result in complex LPV representations, which are not suitable for controller synthesis. We apply and compare state-of-the-art conversion techniques on the GPRV model, including learning based approaches, to optimize the complexity and conservatism of the resulting LPV embedding. The results show that we can obtain an LPV embedding that approximates the complex nonlinear dynamics sufficiently well, where the balance between complexity, conservatism and model performance is efficiently chosen.

Keywords: Aerospace Dynamics, Nonlinear Systems
Abstract: In this paper, we develop a reliable Linear Parameter-Varying (LPV) model of the pitch channel dynamics of a fin-stabilized projectile. Among the available LPV design approaches, the State Transformation is analyzed, being particularly suitable for a class of systems defined as output nonlinear, and compatible with the projectile dynamics formulation. The State Transformation provides a quasi-LPV representation, which corresponds to an exact transformation of the original nonlinear model, preserving relevant couplings that are usually lost through the classical approximation methods. Some important considerations regarding the limitations of this approach are also discussed and verified in the missile dynamics. The accuracy of the obtained quasi-LPV model is assessed by means of open-loop simulations, comparing the performance to the original nonlinear model at selected flight conditions.

Keywords: Nonlinear Systems
Abstract: A popular technique used to obtain linear representations of nonlinear systems is the so-called Koopman approach, where the nonlinear dynamics are lifted to a (possibly infinite dimensional) linear space through nonlinear functions called observables. In the lifted space, the dynamics are linear and represented by a so-called Koopman operator. While the Koopman theory was originally introduced for autonomous systems, it has been widely used to derive linear time-invariant (LTI) models for nonlinear systems with inputs through various approximation schemes such as the extended dynamics mode decomposition (EDMD). However, recent extensions of the Koopman theory show that the lifting process for such systems results in a linear parameter-varying (LPV) model instead of an LTI form. As LTI Koopman model based control has been successfully used in practice and it is generally temping to use such LTI descriptions of nonlinear systems, due to the simplicity of the associated control tool chain, a systematic approach is needed to synthesise optimal LTI approximations of LPV Koopman models compared to the ad-hoc schemes such as EDMD, which is based on least-squares regression. In this work, we introduce optimal LTI Koopman approximations of exact Koopman models of nonlinear systems with inputs by using l2-gain and generalized H2 norm performance measures. We demonstrate the advantages of the proposed Koopman modelling procedure compared to EDMD.

Keywords: Predictive Control, Nonlinear Systems, Stability and Stabilization
Abstract: This paper presents a novel Model Predictive Control (MPC) algorithm for Linear Parameter Varying (LPV) systems represented in the Input-Output (IO) form. The proposed MPC is derived using estimates for the future scheduling parameter trajectory, made viable through a recursive Taylor-based extrapolation law. The method also includes explicit integral action, which, coupled with quadratic terminal ingredients, enables offset-free reference tracking and asymptotic IO stability. A numeric benchmark example is used to illustrate the advantages of the proposed method, as well as its real-time capabilities

Keywords: Fault Tolerant Control, Aerospace Dynamics
Abstract: In this article several robust design techniques are compared via their application to the fault tolerant control problem for the lateral/directional motion of JAXA's research aircraft MuPAL-alpha. The techniques used include: (i) a single, passive-FTC, robust structured Hinfinity design, (ii) single, active-FTC, robust standard and structured Hinfinity designs, (iii) manual scheduling schemes from the previous designs, (iv) a self-scheduled structured Hinfinity design, and (v) a linear parameter varying design. All the designs were implemented in the onboard computer and validated in the so-called Aircraft-In-the-Loop configuration, which entails the operation of the full aircraft in fly-by-wire mode in the hangar. The results show that all the approaches provided acceptable solutions, but with the last two techniques resulting in a more homogeneous performance throughout the fault and command scenarios tested.

Keywords: Uncertain Systems - LPVS, Sampled-Data Systems, Structural Analysis
Abstract: Predicting the exact future of a Linear Parameter-Varying (LPV) system with its parameter exclusively known in real time is by definition an impossible task. In particular, it is difficult to quantify the error induced by a Zero-Order Hold (ZOH) discretization of the parameter which is not verified in practice (e.g. when the parameter depends on the states, inputs or outputs of the continuous-time system). Under a Lipschitz assumption, this paper upper bounds — in terms of uncertain matrices — the greatest possible discrepancy between the real future of the system and its estimate based on the last known values of the input and parameter. This not only upper bounds the error due to the ZOH discretization, but also provides sufficient conditions for controllability and observability of the system in the near future by bounding its Gramians.

Keywords: Networks and Networked Systems
Abstract: We present some recent progress in developing a data-driven high dimensional data clustering algorithm using delay adaptation and discrete Lyapunov functions. In our dynamical system approach towards data clustering, we aim to develop a feedforward and feedback neural network that self-organizes high dimensional data into clusters characterized by the network’s local attractors and their domains of attraction whose boundaries describe the clustering criteria. For a high dimensional data, clusters (and local attractors) are formed in lower dimensional subspaces not pre-prescribed, and delay adaptation governed by the dissimilarity between features of emerging clusters and input signals is the critical feedback mechanism for the convergence of the subspaces where clusters are formed. These subspaces can be skewed, so the principal component analysis must be incorporated into the delay adaptation. We will illustrate the global convergence theory and algorithm with some real-life applications.

Keywords: Structural Properties
Abstract: Problems of locomotion typically encounter switching between gaits. In many applications, it is desirable to make such transitions between modes or gaits inconspicuous and graceful. This is achieved by keeping the typical behavior of the system as persistent as possible. It is shown that this problem can be defined in a more abstract sense, and therefore generalized to a new class of problems, called “maximum persistence of behavior.” In this presentation, I will give a precise definition of this concept, for signals and for systems, and illustrate some applications from thermodynamics to signal study and robotics. I will also introduce the dual problem of filtering in the context of “maximal persistence of behavior.” The results stem from joint work with Deryck Yeung (Trinity University, San Antonio, TX), Basit Memon (Habib University, Karachi, PK), and Vishal Murali (now at NVIDIA) while at Georgia Tech. The paradigm starts by defining typical behaviors in the framework initiated by J.C. Willems. Gluing two typical signals or typical system behaviors requires connections (raccordations) that belong to a larger class of behaviors, but are locally close to the typical behaviors. Solutions with persistent behavior are then sought via optimization for a kernel or an image problem. This solution encapsulates the controllability problem of Willems, morphing theory in image analysis, object shaping, quasi-stationary transitions from thermodynamics, and orbit transfer problems, and can be characterized in a geometric way. In the robotics framework, obvious applications are in gait transitions for biomimetic robots as when switching from walking (e.g., while inspecting) to running (evasion), or a gait transition necessitated by a change in terrain (solid, muddy, or granular). In particular, I will discuss a method of smoothly connecting periodic orbits of systems. We first deal with the case when the periodic signals to be connected are of the same period and then move on to discuss the case of different periods, requiring in addition a frequency warping.

Keywords: Diagnosis and Control
Abstract: The design of feedback controllers that are asked to satisfy structural constraints remains as challenging a problem today as it was decades ago. Whereas there has been significant advance in the techniques to analyze stability of feedback systems, with polynomial relaxations extending and unifying many robustness and stability results and pushing the envelope of the class of problems that could be considered solved, design problems continue to challenge systems and control researchers and practitioners.

In this talk, we will revisit various problems in robust, gain scheduling and structured control. Along the way, we will establish connections among various forms of constraints and associated notions of stabilities. We will conclude with the introduction of some new tools that hold the promise of unifying two of the competing frameworks for controller design by reformulating the Youla parametrization of all stabilizing controllers in the form of linear matrix inequalities.

Keywords: Distributed Delays, Networks and Networked Systems, Delays in Network Controlled Systems
Abstract: This paper studies the higher-order cluster consensus problem for linear multi-agent systems with fixed input delay. It is proved that there is always a non-empty set of control parameters that makes a delayed third-order system stable. In light of this result, a control parameter selection procedure is proposed for higher-order systems under fixed input delay. Theoretical results are verified by numerical examples.

Keywords: Frequency-domain Methods, Control Design
Abstract: This paper focuses on the consensus tracking problem for a group of single-integrator agents with heterogeneous communication and input delays, with the goal of achieving the maximum exponential convergence rate. Existing research discusses a tracking protocol consisting of a consensus-based estimator and a tracking controller for each agent, as well as stability analysis. In contrast, we examine the performance evolution of multi-agent systems for such tracking protocols in terms of their exponential convergence rate. We present a systematic frequency domain method for analyzing the rightmost eigenvalue in the open left half complex plane, which determines the exponential decay rate of the tracking error. The spectral characteristics of the linear time-invariant retarded delay system are investigated, leading to the optimization of the tracking controller and estimator gains. Simulations are given to demonstrate how the system performs when the design parameters are optimized.

Keywords: Delays in Network Controlled Systems, Distributed Delays, Control Design
Abstract: This paper studies the leader-tracking problem of high-order Multi-Agents Systems under a periodic time-varying communication topology, without requiring the connectivity of the network for all t ≥ 0. The case of both state and communication delays is considered. A fully distributed control protocol, along with the constructive time-delay approach to periodic averaging, are combined in order to solve the problem, thus ensuring that a time-dependent switching control rule preserves the input to-state stability (ISS) of the entire network, despite the presence of disconnected topologies, state and communication delays. The original closed-loop error systems is transformed into a neutral-type system with discrete and distributed delays. ISS analysis of the neutral system employs appropriate Lyapunov Krasovskii functionals leading to simple ISS conditions in terms of Linear Matrix Inequalities (LMIs), whose solution allows finding upper bounds on small parameter, state and communication delays that preserve ISS. Numerical simulations illustrate the effectiveness of the theoretical results.

Keywords: Distributed Delays, Time-varying Delays, Networks and Networked Systems
Abstract: This paper analyzes the consensus problem in a group of networked agents with nonuniform time-varying delays communicating over an uncertain communication network. Time varying delays are assumed to be associated with each link in a directed communication graph topology. The network topology for information exchange is modeled with communication links subject to bounded multiplicative uncertainties. By using the the Lyapunov–Krasovskii method, delay-dependent consensus conditions are derived in terms of linear matrix inequalities. A numerical simulation is presented to illustrate the accuracy of the proposed theoretical approach.

Keywords: Traffic Flow and Transportation Systems, Time-varying Delays, Control Design
Abstract: In this paper we introduce a novel fully distributed finite-time cooperative control law able to solve the platoon formation control problem for a network of autonomous vehicles, whose dynamics is represented via the third-order drivetrain linear model. Internet of Vehicle (IoV) communication latencies have been considered and modeled via heterogeneous time-varying delays. The resulting control protocol is computed via outdated information and embeds vehicles acceleration error, which allows improving control reactivity during sudden acceleration/deceleration maneuvers. The finite-time stability of the vehicular network has been analyzed and proved by leveraging Multi-Agent Systems (MASs) paradigm along with Lyapunov-Krasovskii method. This latter leads to stability conditions in the forms of Linear Matrix Inequalities, whose solution allows finding control gains and state trajectories threshold. Simulation results confirm the effectiveness of the theoretical derivation in guaranteeing the cooperative driving despite the presence of IoV communication latencies.

Keywords: Delays in Network Controlled Systems, Control Design, Stabilization
Abstract: The stabilization of consensus or balanced configurations on the circle for a single integrator multi-agent system with heterogeneous input delays and fixed undirected ring communication topology is addressed with globally continuous control using a feedback reshaping strategy to destabilize locally stable nonconsensus configurations in the Kuramoto model while ensuring the input delay is below the Hopf instability threshold. Simulations show the desired configuration is almost globally stabilized for a maximum number N of agents while the region of attraction is enlarged for higher N. Consensus stabilization of natural Kuramoto dynamics using delayed nonlinear feedback control is also proposed.

Keywords: Infinite-dimensional Systems and Delays, Approximation Methods, Delays in Biological Systems
Abstract: We extend the use of piecewise orthogonal collocation to computing periodic solutions of coupled systems of retarded functional differential equations and renewal equations, motivated by their vast presence in models of population dynamics. In view of proving convergence through a rigorous error analysis, extending the corresponding proofs obtained for RFDEs and REs separately, we validate the theoretical results by means of numerical experiments.

Keywords: Robust Control
Abstract: In this talk we discuss a class of (large-scale) delay eigenvalue problems that admit a spectrum similar to that of a Hamiltonian matrix, in the sense that the spectrum is symmetric with respect to both the real and imaginary axis. Such eigenvalue problems are typically encountered when computing the H-infinity norm of time-delay systems. We will present a method to iteratively approximate the eigenvalues closest to a given purely imaginary point, while preserving the symmetries of the spectrum. To this end, the presented method exploits the equivalence between the solutions of the considered delay eigenvalue problem and the eigenvalues of a linear but infinite-dimensional operator. To compute the eigenvalues closest to the given point, we apply a specifically chosen shift-invert transformation to this operator. The advantage of the chosen shift-invert transformation is that the spectrum of the transformed operator has a “real skew-Hamiltonian”-like structure. Furthermore, it can be proven that the Krylov subspace constructed by applying this operator, satisfies an orthogonality property in terms of a specifically chosen bilinear form. By taking this property into account during the orthogonalization process, it is ensured that the obtained approximation for a simple, purely imaginary eigenvalue is simple and purely imaginary, even in the presence of rounding errors.

Keywords: Infinite-dimensional Systems and Delays, Approximation Methods
Abstract: This work is a preliminary investigation of the use of data-driven techniques to analyze dynamical systems originating from delay differential equations. In particular, we study two alternate approaches. On the one hand, we apply model discovery techniques to recover the governing equations from data. On the other hand, we adopt the following multi-fidelity strategy. We first apply dimensionality reduction methods, like the dynamic mode decomposition, to decompose a time series of snapshots in components which evolve linearly. The most relevant modes are extracted for reconstruction of missing data or future prediction, collecting at low cost a possibly large amount of low-fidelity data. Then we refine them by adding few high-fidelity data from, e.g., highly accurate, possibly expensive, numerical simulations. We present preliminary experimental results on systems with a single constant delay (both linear and nonlinear).

Keywords: Observation and Observer Design, Frequency-domain Methods, Infinite-dimensional Systems and Delays
Abstract: In order to estimate the state of a dynamical system, we address the problem of designing an observer for linear time-invariant (LTI) dynamical systems including a time-delay. Sufficient conditions for the existence of the proposed observer are given using partial placement of the poles for the error. Namely, we exploit the multiplicity-induced-dominance property of the characteristic function corresponding to the system’s error. The effectiveness of the proposed observer design is shown in both state lag and input lag respectively through the problems of Mach number control in a wind tunnel and stabilization of the inverted pendulum on a cart by using the delay.

Keywords: State-dependent Delays, Approximation Methods, Delay Estimation
Abstract: Threshold delays arise naturally in a wide variety of dynamical systems. Standard routines for performing numerical analysis of delay differential equations (DDEs) typically handle discrete constant or state-dependent delays, but are not directly applicable to distributed delay problems. We show how to reformulate the threshold delay problem as a discrete delay DDE, with the delay as an extra variable, by differentiating the threshold condition. This allows initial value problems to be solved in Matlab using ddesd. We also describe two implementations of the threshold delay DDE in DDE-BIFTOOL to perform numerical bifurcation analysis. One approach enables us to determine steady state bifurcations via a correction of characteristic values from a modified but related problem with discrete delays. The second approach involves introducing dummy constant delays which are used to discretize the integral in the threshold condition. This allows the threshold delay to be solved for directly within DDE-BIFTOOL, and enables the full functionality of DDE-BIFTOOL, so we can compute periodic orbits as well as steady states, along with their stability and bifurcations. We illustrate these techniques on a gene regulation model where the delay, driven by a transport process, is implicitly defined by a threshold condition. The speed of the transport process depends on the state of the system making the delay both state-dependent and distributed.

Keywords: Fractional-order Modeling and Identification, Fractional Signal and Imaging Processing
Abstract: Fractional derivatives are non local operators that has compacity property in terms of parameter number for modeling diffusive phenomenon with very few parameters. One of its main properties is its non-local behavior, as it can be exploited to model long-memory phenomena such as heat transfers. However, such non-locality implies a constant knowledge of the full past of the function to be differentiated. In the context of real-time system identification, this may limit the experiences as calculations become slower as time progresses. This study deals with the relationship between frequency content of a signal and its truncation error in order to obtain real-time exploitable algorithms.

Keywords: Fractional-order Modeling and Identification, Fractional Signal and Imaging Processing, Robotics, Mechatronics and Mechanical Systems
Abstract: This paper proposes a method to design a wind turbulence model based on real wind spectral characteristics. It uses models based on Cole-Cole fractional functions to approximate the wind turbulence power spectral density. Von Kármán model is the most commonly used model but originally designed for aircraft in high altitude. Therefore, it is not suitable for systems operating at low altitude, hence the need for more accurate models in these specific conditions. Shaping filters issued from the fractional models are employed to generate realistic random wind speed turbulence from a random white noise input. Then, a grey box model is proposed from physical parameters such as the classic von Kármán ones (mean speed, turbulence intensity and length scale).

Keywords: Infinite-dimensional Systems and PDEs Models, Structural Properties, Fractional-order Modeling and Identification
Abstract: This discussion paper presents some new results, currently under review process in an IFAC journal. It concerns robust stability analysis of LTI systems having irrational transfer functions which cover a wide variety of linear systems including distributed parameter systems that are solution of partial differential equations, time-delay rational systems, and fractional order systems. A robust estimation algorithm, formulated as an interval constraint satisfaction problem, is proposed, for determining stability crossing sets, for which there is at least a pole crossing the imaginary axis. It is applied for assessing stability of a controlled parabolic 1D partial differential equation.

Keywords: Power Systems and Control, Power Systems, Energy and Nuclear Systems
Abstract: This study explores an integral minus tilt integral derivative with a filter (I-TDN) control structure for a two-area interconnected hybrid power system. The investigated system includes reheated thermal power plants with stochastic non-conventional energy sources in one control area and hydropower plants in the other. Non-conventional energy sources include wind, solar thermal, and fuel cells. It needs to mention here that the load employed in both control area have stochastic in nature. The comprehensive simulation study shows that the I-TDN control structure outperforms PID and I-PD controllers in terms of the performance index. Sensitivity analysis has been carried out for variation in the system parameters, such as the speed regulation parameter, nonlinearities associated with a reheated thermal and hydropower plant, and random load perturbation, to demonstrate the robustness of the proposed control structure. The simulation results obtained for sensitivity analysis proves the efficacy of the ‘I-TDN’ control structure as a load frequency controller over others in terms of integral time absolute error and performance index.

Keywords: Process Control and Chemical Processes, Structural Properties, Disturbance Rejection
Abstract: Heat exchanger networks (HENs) are drivers of sustainability in chemical process plants. Due to their interaction with various process units, their operation is frequently subjected to disturbances in inputs and planned changes in targets. This motivates the need for optimal operation and control. This paper addresses optimal operation of a class of HENs involving stream splits. It is shown that these stream splits can be used to direct the energetic effect of a disturbance along a favourable path (e.g. minimum energy consumption) and thus allow for optimal operation. Specifically, a two-step strategy is proposed involving steady state optimisation for redirection of energetic effect of the disturbance based on the structure of the network. This is followed by implementation of a feedback control system to track the targets given by the optimiser. The effectiveness of the proposed framework is illustrated with the help of a benchmark example.

Keywords: Automotive Systems, Feedback Control, Time-varying Systems
Abstract: In order to increase the number of situations in which an intelligent vehicle can operate without human intervention, lateral control is required to accurately guide it in a reference trajectory regardless of the shape of the road or the longitudinal speed. Some studies address this problem by tuning a controller for low and high speeds and including an output adaptation law. In this paper, a strategy framed in the Model-Free Control paradigm is presented to laterally control the vehicle over a wide speed range. Tracking quality, system stability and passenger comfort are thoroughly analyzed and compared to similar control structures. The results obtained both in simulation and with a real vehicle show that the developed strategy tracks a large number of trajectories with high degree of accuracy, safety and comfort.