Keywords: Robust Stability and Performance Analysis
Abstract: Natural systems across biology, ecology and epidemiology can be seen as dynamical networks, namely dynamical systems that are naturally endowed with an underlying network structure, because they are composed of several subsystems that interact according to an interconnection topology. Despite their large scale and complexity, most systems in nature exhibit an extraordinary robustness, which can be found at all scales, from protein interactions to gene regulatory networks, from single cells to whole organisms and species: fundamental properties and qualitative behaviours that are crucial for survival are preserved even in the presence of huge parameter variations and environmental fluctuations. With a special focus on biochemical reaction networks, the first part of the talk discusses systems-and-control approaches to unravel the key features of these systems and identify the roots of the amazing robustness that often characterises them, by identifying properties and emerging behaviours that exclusively depend on the system structure (the graph topology along with qualitative information), regardless of parameter values. The BDC-decomposition is introduced to capture the system structure and enable the parameter-free assessment of important properties, including the stability of equilibria and the sign of steady-state input-output influences, thus allowing structural model falsification and structural comparison of alternative mechanisms proposed to explain the same phenomenon. The second part of the talk is inspired by the COVID-19 pandemic and the observation that compartmental models for epidemics can be seen as a special class of chemical reaction networks. Epidemiological systems describing the spread of infectious diseases within a population are considered along with control approaches to curb the contagion. Strategies relying on optimal and robust control theory are proposed to cope with the deep uncertainty affecting parameter values and optimally control the epidemic.

Keywords: H-Infinity Control, Identification, and Estimation, Automotive, Robust Control
Abstract: This paper presents an efficient design for a lateral control synthesis of an off-road vehicle. The considered vehicles have two steering axles and are intended to move on a slippery soil with important lateral and longitudinal slopes. The proposed design relies on an extended bicycle model that accounts for the slopes. The lateral control is designed based on a feedforward/feedback architecture. The feedforward takes advantage of the knowledge of the path characteristics (curvature and slopes), and the feedback ensures robust reference-trajectory tracking. Through an H2/Hinf multi-objective synthesis, the robustness of the lateral controller is ensured with regard to the model uncertainties, path, and soil features. This is important because off-road vehicles’ dynamics are by nature highly variable. To cope with the difficulty caused by uncertain knowledge of key parameters, the proposed robust approach is a practical compromise that maximizes performance under constraints of robustness. The results obtained on a realistic non-linear simulator support this assertion.

Keywords: Robust Control, H-Infinity Control, Identification, and Estimation
Abstract: This paper is concerned with the design of robust controllers with observers. In general, the nominal model is selected as the state-space model for the observer to estimate the states even if the plant has uncertainties. However, the properness of this strategy is ambiguous. This paper evaluates the properness of selecting the nominal model for state estimation of the plant with uncertainties. Sato proposed a new type of controller so-called observer-structured controller. The controller introduces additional freedom for selecting the state-space model used for the state estimation. With this type of controller, this paper demonstrates that the adequate selection of estimation model improves the closed-loop control performance through numerical examples. This paper also shows an example which clearly shows that the optimized observer-based controller has an optimized state-transition matrix which is different from the nominal state-transition matrix of the controlled plant.

Keywords: H-Infinity Control, Identification, and Estimation
Abstract: This paper deals with the dynamic modeling and the mathcal{H}_{infty} control of a cable-driven parallel robot with flexible cables. The modeling of planar 3-DOF (two translation and one rotation) robot pulled by four-cables has been performed using the assumed modes approach and the Euler-Lagrange equation, leading to a differential-algebraic equations model (DAE model). This model has been then transformed into an ordinary-differential equations model (ODE model) whose linearization is observable and controllable. An mathcal{H}_{infty} controller has been synthesized, considering a four-bloc synthesis scheme, in order to control the translation and the rotation of the platform and the mean tension of the cables. An original tuning strategy of the weighting functions is proposed, in order to ensure different performances on the different DOF, that suits to the physical nature of the process. The approach is evaluated in simulation on the nonlinear model and shows good disturbance rejection and reference tracking.

Keywords: H-Infinity Control, Identification, and Estimation, Robust Nonlinear Control, Mechatronics
Abstract: The synthesis of a global nonlinear H-infinity position regulator and the L_2-gain analysis are studied for robot manipulators. For the global solution, one needs to verify the Hamilton-Jacobi-Isaacs inequality for all initial conditions. The stability analysis of the unperturbed closed-loop system is made in the framework of Lyapunov function, while the robustness analysis is stated by the L_2-gain approach. Simulation results, made for an experimental robot, corroborate the developed analysis.

Keywords: H-Infinity Control, Identification, and Estimation, Robust Stability and Performance Analysis, Mechatronics
Abstract: The paper considers the problem of synthesizing the pitch control system of an autonomous underwater vehicle (AUV), considering the uncertainty of its parameters and underwater disturbances. The μ-synthesis using D-K iteration is applied to solve the problem. For the subsequent implementation of the controller in practice, a method for reducing its order based on the Hankel norm approximation is applied. The proposed approach is tested on the mathematical model of the MAYA AUV with the parameters deviating from the nominal values. The simulation result shows that the proposed robust control scheme effectively compensates for the parametric uncertainty and underwater disturbances.

Keywords: Identification for Robust Control, Robust Adaptive Control
Abstract: A gyroscopic rotor is controlled and studied. A rotor-model-free control which is based on the transform domain least mean squares algorithm is implemented. An algorithm is proposed which compensates the delay of the transformation for quasi-periodic signals. Experiments show that this algorithm has a positive effect on the first resonance and that it is sufficient to compute the transformation only once per revolution. The rotor-model-free control is then used to excite the system during operation for its identification. A non-parametric model is estimated using two run-outs, one with and one without adaptive feedforward control. Transient phenomena occur after passing the first lightly damped resonance causing a distortion in the estimation. This is remedied by fitting a modal model to the estimation.

Keywords: Computational Methods in Control, Networked Systems
Abstract: A distributed welfare game is a non-cooperative game-theoretic model for a resource allocation problem that maximizes welfare. In this paper, two utility function designs for resource allocation problems with infinite resources are considered. One is called wonderful life utility, which was proposed for the problem. The second one employs a solution concept of the cooperative game, Egalitarian Non-Separable Contribution (ENSC). The main contributions of the paper are two holds. The first contribution is to clarify the existence of a pure strategy Nash equilibrium in the games defined via the above utility functions when the welfare function is monotone, continuously twice differentiable, and diminishing return (DR) submodular. The second is to derive lower bounds of the Price of Anarchy (PoA) for both games. The PoA is the worst-case ratio between the optimal value and values at a pure strategy Nash equilibrium.

Keywords: Identification for Robust Control
Abstract: This paper proposes system identification of time delay systems that have a long time constant. It is known that an identified model has an unstable zero with an internal delay. In particular, the distributed parameter system such as the heat conduction system has dynamic characteristics in not only time but space, and the approximate model is often described by the time delay system. It is shown that applying the subspace method to the heat conduction system makes a model with an unstable zero. By confirming the position of zero according to time-shift operation of the input-output data, the time delay can be identified.

Keywords: Model and Controller Reduction
Abstract: Direct data-driven controller design has attracted considerable attention because of the simple design procedure. In this study, we present a direct model-referenced data-driven design for obtaining a sparse controller to prevent overlearning from single-experiment data. In conventional methods, controller parameters are obtained for a controller with the order defined by the user. When there is little plant information, it is difficult to define controller order to achieve model matching. In addition, if a high-order controller is simply set, overlearning may occur. Thus, we propose a sparse controller design method. The proposed method is based on the virtual reference feedback tuning and least absolute shrinkage and selection operator regression. A simulation is performed to verify the proposed method’s effectiveness. The results show that a sparse controller can be obtained for a high-order controller.

Keywords: Robust Control, Robust Nonlinear Control, Robust Stability and Performance Analysis
Abstract: Nonlinear control problems have been the main subjects in control engineering from theoretical and applications aspects. Reinforcement learning shows promising results for solving highly nonlinear control problems. Among many variants of reinforcement learning, Deep Deterministic Policy Gradient (DDPG) considers continuous control signals, which makes it an ideal candidate for solving nonlinear control problems. The training requires frequently, however, a large number of computations. To improve the convergence of DDPG, we present a state-space segmentation method dividing the state-space to expand the target space defined by the best reward. An inverted pendulum control example demonstrates the performance of the proposed segmentation method.

Keywords: Networked Systems, System Biology
Abstract: During the progression of complex diseases caused by qualitative shifts in gene networks, the deteriorations may be abrupt and cause a critical transition from a healthy state to a disease state. We define a pre-disease state as a state just before the imminent critical transition. Medical treatment in the pre-disease state should be more efficient than in the disease state. This paper proposes a data-driven method to design an input assignment and a stabilizing controller to avoid this kind of qualitative shift. The proposed method only requires a small number of data samples on the steady pre-disease state. We show numerical examples to validate the effectiveness of the proposed method.

Keywords: Networked Systems, Robust Stability and Performance Analysis, Optimal Control and Dynamic Optimization
Abstract: This paper investigates closed-loop stability of linear discrete-time plants subject to bounded disturbances when controlled according to packetized predictive control (PPC) policies. In the considered feedback loop, the controller is connected to the actuator via a digital communication channel imposing bounded dropouts. Two PPC strategies are taken into account. In both cases, the control packets are generated by solving sparsity-promoting optimization problems. One is based upon an ell^2-constrained ell^0 optimization problem. Such problem is relaxed by an ell^1-ell^2 optimization problem in the other sparse PPC setting. We show that the ell^2-constrained ell^0 sparse PPC and unconstrained ell^1-ell^2 sparse PPC render system states bounded if the design parameters satisfy certain conditions. The bounds we derive on states are increasing with respect to the disturbance magnitude. We illustrate the results via simulation.

Keywords: Robust Stability and Performance Analysis, Robust Control, Robust Nonlinear Control
Abstract: In the past, a special type of nonlinear delay differential system structure was proposed for gene regulatory networks. For this cyclic dynamical system, stability analysis was done using various tools from systems theory. This paper investigates robust stability of an extended gene regulatory network model with time delayed negative feedback. Specifically, the delay margin analysis is done for this system under multiplicative uncertainty. The effect of uncertainty on the delay margin is determined. It is also shown that for particular higher order extensions of the model it is possible to improve the delay margin.

Keywords: Robust Control, Mechatronics
Abstract: The key idea of this contribution is the partial compensation of non-minimum phase zeros or unstable poles. Therefore the integer-order zero/pole is split into a product of fractional-order pseudo zeros/poles. The amplitude and phase response of these fractional-order terms is derived to include these compensators into the loop-shaping design. Such compensators can be generalized to conjugate complex zeros/poles, and also implicit fractional-order terms can be applied. In the case of the non-minimum phase zero, its compensation leads to a higher phase margin and a steeper open-loop amplitude response around the crossover frequency resulting in a reduced undershooting in the step-response, as illustrated in the numerical example.

Keywords: System Biology, Robust Control
Abstract: This study was aimed at enhancing the path-following control performance of the arm length control mechanism in automatic spinach harvesters. Field experiments conducted in existing studies indicated that the root cutting blade path must be controlled to compensate for motion disturbances in the pitch direction to realize successful harvesting. Therefore, methods of path variation compensation via PID control and feedforward control have been established. Notably, using these strategies, the arm length controller cannot follow the corrected target values with an acceptable response because of uncertainties and disturbances. The insufficient performance of the controller deteriorates the harvesting performance. Consequently, the feedback control performance must be enhanced in events involving uncertainties and disturbances. To enhance the path-following performance of the arm length control mechanism, an advanced controller based on the H-infinity control theory was designed in this study. The tracking performance and effectiveness of the path compensation method were experimentally evaluated.

Keywords: Analysis of Big Data Affected by Uncertainties, Robust Optimization, Model and Controller Reduction
Abstract: This paper presents an approach to optimal routing with resource assignment for traveling among scattered farm fields. To this end, a road network among farm fields is created, which has data on shortest paths of all pairs of fields and its distances. Two types of multiple traveling salesman problem (mTSP) formulations solved by an exact algorithm are presented to perform optimal routing. The proposed approach is demonstrated through a numerical experiment using data from an agricultural corporation considered in this paper. The results show features of the mTSP formulations and the proposed approach can yield the optimal routes within 2.4 seconds. Hence the proposed approach is feasible for use in actual daily work.

Keywords: Optimal Control and Dynamic Optimization, Fault Detection in Uncertain Systems, Mechatronics
Abstract: In this research, we aim to promote digital forest inventory using a multi-rotor drone with LiDAR, which is flying in the target forest. In the measurement by LiDAR mounted on the drone, noise and error occur in the measurement data due to the shaking and vibration of the drone. The accuracy of measurement data for forest inventory is required to be achieved according to the standards of the relevant government agency. Therefore, it is necessary to construct a robust and precise forest inventory system for disturbances caused by such drone-based measurement. To solve the issue, we design a path planning considering the characteristics of LiDAR-based measurement. Our fundamental inventory system provides point clouds corrected based on drone flight records and point cloud processing technologies.

Keywords: H-Infinity Control, Identification, and Estimation
Abstract: Global navigation satellite system (GNSS) and inertial measurement unit (IMU) are often used for the localization of outdoor robots such as agricultural robots. When an agricultural robot travels over unleveled ground such as in a farmland, the GNSS antenna may shake, further degrading the positioning accuracy. In particular, the GNSS observation value may suddenly jump, and the error may persist for some time. This GNSS bias may cause a decrease in the localization accuracy. In this study, we have proposed a localization method that is robust to GNSS bias by integrating two models: one that estimates bias from the difference between GNSS observations and odometry, and the other that does not include bias in GNSS observations, using a particle filter. The proposed method was validated by applying it to the data obtained when an agricultural robot travels over unimproved farmland.