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As a category of the recurrent neural network (RNN), zeroing neural network (ZNN) can effectively handle time-variant optimization issues. Compared with the fixed-parameter ZNN that needs to be adjusted frequently to achieve good performance, the conventional variable-parameter ZNN (VPZNN) does not require frequent adjustment, but its variable parameter will tend to infinity as time grows. Besides, the existing noise-tolerant ZNN model is not good enough to deal with time-varying noise. Therefore, a new-type segmented VPZNN (SVPZNN) for handling the dynamic quadratic minimization issue (DQMI) is presented in this work. Unlike the previous ZNNs, the SVPZNN includes an integral term and a nonlinear activation function, in addition to two specially constructed time-varying piecewise parameters. This structure keeps the time-varying parameters stable and makes the model have strong noise tolerance capability. Besides, theoretical analysis on SVPZNN is proposed to determine the upper bound of convergence time in the absence or presence of noise interference. Numerical simulations verify that SVPZNN has shorter convergence time and better robustness than existing ZNN models when handling DQMI.

The stability analysis problem is considered for multiarea load frequency control (LFC) systems with electric vehicles (EVs) and time delays. The novel linear operator inequality approach is proposed and a less conservative delay-dependent stability condition is achieved. First, the model of the multiarea LFC system with EVs and delays is expressed by the partial integral equation (PIE) at the first time. Then, a complete quadratic Lyapunov-Krasovskii functional is built in the form of an inner product of the linear partial integral (PI) operator. The novel stability criteria with less conservatism are proposed in the form of linear operator inequality. Moreover, the relationships between the delay margins and the controller parameters are shown. Finally, the simulations are conducted on both one-area and two-area LFC systems to show the effectiveness of the proposed approach.

This paper deals with the sliding mode stabilization for chaotic systems. In the system under consideration, the nonlinear function is one-sided Lipschitz with quadratic inner-boundedness. Specifically, a non-fragile sliding mode surface is constructed, and the sufficient condition for the convergence is derived. Then, a new feedback law is proposed to enable the state trajectories of the closed-loop system to reach the sliding mode surface in finite time. Finally, an example in the background of the unified chaos system is simulated to show the validation of the designed controller.

This paper presents design and evaluation of a Quadratic Buck Converter (QBC). A step-by-step procedure to select the converter’s component values and semiconductor device ratings is demonstrated. A prototype QBC with a simple closed loop output voltage control is constructed and experimentally evaluated. It is shown that the converter performs well within the design specification, has good output voltage regulation and fast transient response, and achieves the highest efficiency of 82%.

A control design framework for the coupled stability problem in robotics is presented. The proposed framework fundamentally generalizes the conventional passivity-based approaches to the coupled stability problem. In particular, it allows for stabilization of not necessarily passive robot-environment interaction where both the manipulator and the environment are dissipative systems with quadratic supply rates. In contrast with existing results, the proposed framework can be used in combination with an arbitrary robot’s tracking control algorithm, and its stabilizing action when in contact with environment does not affect the robot’s trajectory tracking performance in free space. The framework is based on the recently developed nonplanar conic systems formalism and generalized scattering-based stabilization methods. A detailed design example is presented which illustrates the capabilities of the proposed method.

In this paper, for a class of continuous-time multivariable linear systems with strong coupling property, a new optimal tracking controller is proposed, which can simultaneously decouple the closed-loop system in its dynamic, and provide optimal performance. Firstly, a new quadratic performance index considering decoupling design is introduced, and then the optimal tracking controller is derived by minimizing the new index according to the minimum principle. Finally, the weighting matrices are provided, by using which decoupling and tracking can both be realized. Experiments on a Coupled-Tank are conducted, whose results show the superiority of the proposed method comparing with the conventional linear quadratic tracking (LQT) controller.

In this paper, we formulate a common design methodology to derive an adaptive cruise control algorithm in order to compensate the effect of the lower level time-lag on the stability. Here the dynamics of the vehicle is approximated by a first order system. The lower level time-lag is referred to the lag associated with the vehicle dynamics to attain the desired condition computed by the adaptive cruise controller. Inclusion of such dynamics reduces the performance of an adaptive cruise control system, both locally and collectively. The solutions presented here can be applied to design a new adaptive cruise control model or to extend any model with velocity dependent spacing function. The derived design method fulfills the over-damped string stability condition in presence of a time-lag in the plant, irrespective of the choice of the equilibrium operating point of linearization. The over-damped characteristic improves both safety and performance. To demonstrate the application of the presented method, two existing models are extended using the derived strategy, viz. the variable time gap and the quadratic range policy. Later the extended models are simulated with rigorous driving conditions, in order to illustrate the improvements in comparison with the respective original models as well as with the extended models using an existing compensation strategy.

This brief mainly deals with the issue of guaranteed cost impulsive synchronization of uncertain multi-agent systems from the multiplex network prospective, where the uncertain time-varying system parameters are supposed to be norm-bounded. By means of a group of linear matrix inequalities, the effective guaranteed cost impulsive control is designed to not only ensure that the trajectories of all followers synchronize with the leader, but also guarantee that the control cost is no higher than an upper bound of the quadratic guaranteed cost function. Numerical example demonstrates the feasibility of theoretical results.

This article focuses on the sampled-data output-feedback control problem for nonlinear systems represented by Takagi-Sugeno fuzzy affine models. An input delay approach is adopted to describe the sample-and-hold behavior of the measurement output. Via augmenting the system states with the control input, the resulting closed-loop system is converted into a singular system first. Based on the piecewise quadratic Lyapunov-Krasovskii functionals, some novel results on the sampled-data piecewise affine output-feedback controller design are attained by employing some convexification techniques. The simulation studies are presented to illustrate the effectiveness of the proposed scheme.

Focusing on improving the operation efficiency and riding comfort of metro rail lines in the peak hours, this article investigates the real-time train regulation and passenger load control problem with respect to frequent disturbances. To better illustrate the relationship between the train timetable and the on-board passengers, the variations of the departure time and the passenger load are elaborated in the form of a state-space model. Based on the Lyapunov stability theory, the problem of minimizing an upper bound on the quadratic performance function is transformed to a dynamic optimization problem with a set of linear matrix inequalities (LMIs), and a predictive control strategy is designed to guarantee the actual train schedule and number of in-vehicle passengers track the nominal timetable and the expected passenger load with a given disturbance attenuation level. With the objective to reduce the computational workloads and cut down the utilization of wireless transmitting resources, an event-triggered strategy is developed to implement the proposed stabilizing feedback controller only when the measurement error exceeds certain threshold, which has better adaptability to the application in large-scale metro networks. Some numerical examples based on the Beijing Yizhuang Metrol Line are provided for illustration of the effectiveness of the proposed scheme.

Recent decades have witnessed a trend that control-theoretical techniques are widely leveraged in various areas, e.g., design and analysis of computational models. Computational methods can be modeled as a controller and searching the equilibrium point of a dynamical system is identical to solving an algebraic equation. Thus, absorbing mature technologies in control theory and integrating it with neural dynamics models can lead to new achievements. This work makes progress along this direction by applying control-theoretical techniques to construct new recurrent neural dynamics for manipulating a perturbed nonstationary quadratic program (QP) with time-varying parameters considered. Specifically, to break the limitations of existing continuous-time models in handling nonstationary problems, a discrete recurrent neural dynamics model is proposed to robustly deal with noise. This work shows how iterative computational methods for solving nonstationary QP can be revisited, designed, and analyzed in a control framework. A modified Newton iteration model and an improved gradient-based neural dynamics are established by referring to the superior structural technology of the presented recurrent neural dynamics, where the chief breakthrough is their excellent convergence and robustness over the traditional models. Numerical experiments are conducted to show the eminence of the proposed models in solving perturbed nonstationary QP.

For the conservatism issue of state-feedback control for Takagi-Sugeno (T-S) fuzzy systems, a novel local controller design method and stability analysis approach are proposed in this article. First, based on a membership function piecewise linearization technique, a local linear controller is proposed to form a closed-loop system. Then, a series of system stability conditions are derived using quadratic and piecewise function approaches. Further, with a designed fuzzy local controller and fuzzy Lyapunov function, less conservative stability conditions are obtained. Finally, a simulation example is adopted to make comparisons with existing and proposed methods. The results clearly show the conservatism reduction and excellent performance of the proposed approach.

This article investigates a fault-tolerant control problem for heterogeneous vehicular platoons with actuator faults and saturation. The occurrence of actuator faults may yield large control signals to avoid performance loss, which can potentially lead to saturation of the actuator that may cause further performance deterioration or even instability. To compensate for the effects of actuator faults and saturation, an adaptive fault-tolerant control method is proposed based on nonlinear vehicle dynamics and a new quadratic spacing policy. The improved quadratic spacing policy is introduced to remove the assumption of zero initial spacing errors. The nonlinear vehicle dynamics is approximated by a radial basis function neural network (RBFNN). The adaptive fault-tolerant platoon control method is developed in the context of PID-type sliding mode control technique, and proved to be capable of guaranteeing individual vehicle stability, string stability and traffic flow stability. The effectiveness of the method is verified through comparison simulation studies.

A guidance law for intercepting a stationary target is derived based on solving the minimum effort optimal control problem with fixed terminal time and a quadratic approximation of the kinematic eq...

To address the low computational efficiency and accuracy for reliability optimization of flexible mechanisms, the mean-probability decomposition-coordination-based extreme support vector machine regression (MPDC-ESVR) method based on the dynamics and uncertainty is proposed. Firstly, a dynamical model is built to calculate the maximum deformation. Then, the ESVR is used as a surrogate model to study the reliability of the flexible mechanisms. Finally, the MPDC are adopted to optimize the weight of the flexible mechanisms considering multiple failure modes. It is proved that the computational efficiency and accuracy of the present methodology are superior to those of Monte Carlo simulation (MCS) and quadratic polynomial-Monte Carlo simulation (QP-MCS) through exemplification with a two-link flexible manipulator. The results indicate the two-link flexible manipulator optimized by current MPDC is lighter than that by the equal distribution probabilistic method and the original manipulator, which is essential for low cost-effectiveness without compromise to safety.

This paper presents a control design method that achieves safety for systems with unmodeled dynamics at the plant input. The proposed method combines control barrier functions (CBFs) and integral quadratic constraints (IQCs). Simplified, low-order models are often used in the design of the controller. Parasitic, unmodeled dynamics (e.g. actuator dynamics, time delays, etc) can lead to safety violations. The proposed method bounds the input-output behavior of these unmodeled dynamics in the time-domain using an alpha-IQC. The alpha-IQC is then incorporated into the CBF constraint to ensure safety. The approach is demonstrated with a simple example.

In this article, we propose a new method to ob-tain feedback controllers of an unknown dynamical systemdirectly from noisy input/state data. The key ingredient ofour design is a new matrix S-lemma that will be proven inthis article. We provide both strict and nonstrict versionsof this S-lemma, which are of interest in their own right.Thereafter, we will apply these results to data-driven con-trol. In particular, we will derive nonconservative designmethods for quadratic stabilization, H2 and H∞ control, allin terms of data-based linear matrix inequalities. In contrastto previous work, the dimensions of our decision variablesare independent of the time horizon of the experiment. Ourapproach, thus, enables control design from large datasets.

This paper investigates an adaptive fault-tolerant pseudo-proportional-integral-derivative sliding-mode control (pseudo-PID-SMC) scheme for a high-speed train (HST) subject to actuator faults, asymmetric nonlinear actuator saturation (ANAS), and integral quadratic constraints (IQCs). It is worth mentioning that a pseudo-PID-SMC surface is proposed in this paper and the scheme based on this surface does not require acceleration measurement. An adaptive saturation compensation system that makes no assumption, as in existing works where nonlinear functions are used to describe the unsaturated region of ANAS as known and strictly monotonous, is developed to attenuate the adverse effects of ANAS. For the saturation-free and ANAS cases, two adaptive fault-tolerant pseudo-PID-SMC schemes with no chattering, a simple structure, and inexpensive computation are developed to guarantee the exponential convergence of all signals in the closed-loop systems. Finally, simulation results based on a real train dynamic model are presented to show the proposed schemes' effectiveness and feasibility.

This article deals with the robust control design for linear systems with input and state delays, subject to nonvanishing matched disturbances. The controller is synthesized with the Lyapunov redesign technique in two stages: first, an optimal predictor compensates the input delay of the nominal system while a quadratic performance index is minimized. Then, the robust component guarantees that the solutions are ultimately bounded. The theoretical results are experimentally validated through a dehydration process, with the controller digital implementation on an industrial programmable automation controller cDAQ-9132.

This article proposes a systematic synthesis method to identify the optimum converter topology for a specified voltage conversion ratio. This gain-oriented converter synthesis is formulated as an inverse problem around the inductor flux balance equations. The proposed synthesis method consists of two processes. A set of distinct converter topologies is first synthesized from the specified voltage conversion ratio by obtaining the solutions for a set of six inverse equations in twelve unknowns. The optimal topology for any intended application can be identified based on the desired performance metrics. The complete synthesis method is illustrated with quadratic buck–boost gain as an example. In total, 25 distinct topologies are synthesized using the first process, and their feasibility is validated by PLECS simulation. In total, three case studies are reported to explain the second process. Ground-referenced output and the minimum number of switches are used as the first two selection criteria in all the case studies. Minimum peak inductor current, minimum component stress factor, and minimum voltage and current stress are the third selection criterion in Case Studies-1, 2, and 3, respectively. The steady-state operations of two optimal converters selected in Case Study-1 were verified experimentally.

This paper is concerned with event-triggered and guaranteed cost finite-time control for switched systems with dynamic quantized output feedback and exogenous disturbances. First, one common strategy is superseded by one mode-dependent event-triggered strategy, which means each subsystem possesses its own event-triggered substrategy. Second, considering the hardship of obtained system state information, a dynamic quantized output feedback controller is used to stabilize the switched system. Third, by using the methods of multiple Lyapunov functionals, free-weighting matrices and the average dwell time for the switched system, the sufficient condition of finite-time boundedness (FTB) is obtained for the switched system; and a synthesis procedure is provided to obtain the dynamic output feedback controller gain, event-triggered strategy parameters and an upper bound of the quadratic performance function. Finally, a numerical simulation is given to demonstrate the effectiveness of the proposed method.

The distributed fault detection and isolation of linear discrete time-varying multi-agent systems subject to heterogeneous dynamics and norm bounded model uncertainties is investigated. By combining the model uncertainties and external disturbances into a new generalized disturbance, a distributed closed-loop residual generator is constructed based on the estimate of the generalized disturbance. Then, the concerned fault detection is transformed into an indefinite quadratic minimum problem by using the finite-horizon robust H ∞ filtering method, for which necessary and sufficient minimum conditions are provided by the Krein-space theory. Afterwards, a computationally efficient recursive algorithm is further developed to determine the residual for individual agent. Based on the resulting residuals, the fault occurrence can be alerted by devising a novel distributed fault detection scheme , which is then applied for the fault isolation by some appropriate transformation of the output measurements. Finally, both numerical and practical simulations are proposed to verify the effectiveness of the proposed algorithm.

PURPOSE Thermal dose delivery in microwave hyperthermia for cancer treatment is expected to benefit from the introduction of ultra-wideband (UWB)-phased array applicators. A full exploitation of the combination of different frequencies to improve the deposition pattern is, however, a nontrivial problem. It is unclear whether the cost functions used for hyperthermia treatment planning (HTP) optimization in the single-frequency setting can be meaningfully extended to the UWB case. METHOD We discuss the ability of the eigenvalue (EV) and a novel implementation of iterative-EV (i-EV) beam-forming methods to fully exploit the available frequency spectrum when a discrete set of simultaneous operating frequencies is available for treatment. We show that the quadratic power deposition ratio solved by the methods can be maximized by only one frequency in the set, therefore rendering EV inadequate for UWB treatment planning. We further investigate whether this represents a limitation in two realistic test cases, comparing the thermal distributions resulting from EV and i-EV to those obtained by optimizing for other nonlinear cost functions that allow for multi-frequency. RESULTS The classical EV-based single-frequency HTP yields systematically lower target SAR deposition and temperature values than nonlinear HTP. In a larynx target, the proposed single-frequency i-EV scheme is able to compensate for this and reach temperatures comparable to those given by global nonlinear optimization. In a meninges target, the multi-frequency setting outperforms the single-frequency one, achieving better target coverage and 0.5∘C higher T90 in the tumor than single-frequency-based HTP. CONCLUSIONS Classical EV performs poorly in terms of resulting target temperatures. The proposed single-frequency i-EV scheme can be a viable option depending on the patient and tumor to be treated, as long as the proper operating frequency can be selected across a UWB range. Multi-frequency HTP can bring a considerable benefit in regions typically difficult to treat such as the brain.

The ever-growing higher penetration of distributed energy resources (DERs) in low-voltage (LV) distribution systems brings both opportunities and challenges to voltage support and regulation. This paper proposes a deep reinforcement learning (DRL)-based scheduling scheme of energy storage systems (ESSs) to mitigate system voltage deviations in unbalanced LV distribution networks. The ESS-based voltage regulation problem is formulated as a multi-stage quadratic stochastic program, with the objective of minimizing the expected total daily voltage regulation cost while satisfying operational constraints. While existing voltage regulation methods are mostly focused on one-time-step control, this paper explores a day-horizon system-wide voltage regulation problem. In other words, the size of action and state spaces are extremely high-dimensional and need to be delicately handled. Furthermore, in order to overcome the difficulty of modeling uncertainties and develop a real-time solution, a learn-to-schedule feedback control framework is proposed by adapting the problem to a model-free DRL setting. The proposed algorithm is tested on a customized 6-bus system and a modified IEEE 34-bus system. Simulation results validate the effectiveness and near-optimality of voltage regulation by ESS in comparison with a deterministic quadratic program solution.

This article proposes a robust model predictive control (MPC) algorithm for the tracking problem of wheeled mobile robots. The robots are subject to bounded disturbances and various practical constraints. Particularly, the incremental input constraint is introduced in the consideration of the safety and comfortability needs in real life. Conditions on the acceleration of the leader robot are derived to guarantee the satisfaction of the incremental input constraint of follower robot. To compensate for the effect of disturbances, a disturbance observer is designed to obtain the estimation of the disturbances, which together with the optimal control input of MPC optimization is contained in the actual control input. Also, a novel quadratic robustness constraint is developed to handle the disturbance estimation error, which allows the designer to balance the initial feasible region and control performance. The proposed algorithm can ensure recursive feasibility, robust constraint satisfaction, and closed-loop stability. Finally, both simulation and experiment results are provided to verify the theoretical properties.

In this paper, the output reachable set synthesis problem for periodic piecewise time-varying systems has been addressed for the first time. Generalized periodic time-varying conditions of the closed-loop system to guarantee the system output bounded by a collection of finite ellipsoids are developed. The decoupling of time-varying observer and controller gains is achieved by a technical lemma . Moreover, by using time-varying matrix functions in multiple linear-interpolative forms and the relaxed quadratic matrix polynomial definiteness results, sufficient condition is developed to determine the periodic time-varying observer and controller with reduced conservatism. A tractable iterative algorithm is proposed to obtain the periodic time-varying observer and controller gains. A numerical example is presented to illustrate the validity of proposed methods.

This paper is concerned with the forecasting aided state estimation (FASE) problem for power distribution networks (PDNs) subject to nonlinear measurements and limited communication resources. For the sake of using bandwidth more effectively, a special closed-loop event triggering condition is first designed with the hope to reduce unnecessary data transmission from the measurement side to the remote estimator. And an event-based robust filtering algorithm is proposed to improve the estimation performance of estimator with the nonlinear intermittent observations. In this algorithm, the measurement output function with high-order nonlinearity is reasonably reduced to a quadratic alternative form, in order to avoid the computation burden caused by the use of the second-order extended Kalman filter (SOEKF), the quadratic nonlinear function is further linearized by Taylor series with quadratic norm bound. Furthermore, an upper bound of the filtering error covariance containing some uncertainties (includes quadratic term and non-triggering errors) is derived and subsequently minimized at each time step. The desired filter gain is obtained by recursively solving a set of Riccati-like matrix equations, which is easy to implement via online computation. Finally, the effectiveness of the proposed algorithm is demonstrated using the standard IEEE 13-bus and 123-bus distribution test feeders with both phasor measurement unit (PMU) and conventional measurements.

This paper presents the observer-based control methodology for the one-sided Lipschitz (OSL) nonlinear systems over measurement delays. A controller design method, based on the estimated states, has been provided by applying the Lyapunov-Krasovskii functional for the delayed dynamics and by inserting the OSL constraint and quadratic inner-boundedness condition. The stability of the resultant delayed dynamics is achieved through the delay-range-dependent approach, and derivative of Lyapunov functional is exploited through the Wirtinger's integral inequality approach to reduce the conservatism of the conventional Jensen's inequality scheme. Further, a necessary and sufficient solution for the main design method has been provided by employing a tedious decoupling technique to render the observer and controller gains, simultaneously, by using the recursive optimization tools. Furthermore, the solution of matrix inequality-oriented results is handled via the cone complementary linearization technique to validate the controller and observer gains through convex optimization. The effectiveness of the resultant observer-oriented control formulation for the OSL nonlinear systems under measurement delays is validated via numerical simulation examples.

This paper considers the problem of designing a shifting state-feedback controller via quadratic parameter-dependent Lyapunov functions (QPDLFs) for systems subject to symmetric time-varying saturations. By means of the linear parameter varying (LPV) framework and the use of the shifting paradigm and the ellipsoidal invariant theory, it is shown that the solution to this problem can be expressed with linear matrix inequalities (LMIs) which can efficiently be solved via available solvers. Specifically, three hyper-ellipsoidal regions are defined in the state-space domain for ensuring that the control action remains in the linearity region of the actuators where saturation does not occur. Furthermore, the closed-loop convergence speed is regulated online according to the instantaneous saturation limit values through the shifting paradigm concept. The main characteristics of the proposed approach are validated by means of two illustrative examples. This work has been partially funded by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the project SaCoAV (ref. PID2020-114244RB-I00). This work has also been partially funded by AGAUR of Generalitat de Catalunya, Spain through the Advanced Control Systems (SAC) group grant (2017 SGR 482) and by the University of Stavanger, Norway through the project IN-12267. A. Ruiz is also supported by the Secretaria d’Universitats i Recerca de la Generalitat de Catalunya, Spain, the European Social Fund (ESF) and AGAUR under a FI SDUR grant (ref. 2020 FI-SDUR 00097).

International audience; Adaptive control using the -modification provides an easily implementable way to stabilize systems with uncertain or fluctuating parameters. Motivated by a specific application from neuroscience, we extend here this methodology to nonlinear time-delay systems ruled by globally Lipschitz dynamics. In order to make the result more handy in practice, we provide an explicit construction of a Lyapunov–Krasovskii functional (LKF) with linear bounds and strict dissipation rate based on the knowledge of an LKF with quadratic bounds and point-wise dissipation rate. When applied to a model of neuronal populations involved in Parkinson’s disease, the benefits with respect to a pure proportional stabilization scheme are discussed through numerical simulations.

International audience; This paper addresses the design of a sampled-data model predictive control (MPC) strategy for linear parameter-varying (LPV) systems. A continuous-time prediction model, which takes into account that the samples are not necessarily periodic and that plant parameters continuously vary with time, is considered. Moreover, it is explicitly assumed that the value of the parameters used to compute the optimal control sequence is measured only at the sampling instants. The MPC approach proposed by Kothare et al. [1], where the basic idea consists in solving an infinite horizon guaranteed cost control problem at each sampling time using linear matrix inequalities (LMI) based formulations, is adopted. In this context, conditions for computing a sampled-data stabilizing LPV control law that provides a guaranteed cost for a quadratic performance criterion under input saturation are derived. These conditions are obtained from a parameter-dependent looped-functional and a parameter-dependent generalized sector condition. A strategy that consists in solving convex optimization problems in a receding horizon policy is therefore proposed. It is shown that the proposed strategy guarantees the feasibility of the optimization problem at each step and leads to the asymptotic stability of the origin. The conservatism reduction provided by the proposed results, with respect to similar ones in the literature, is illustrated through numerical examples.

Controller design for nonlinear systems with Control Lyapunov Function (CLF) based quadratic programs has recently been successfully applied to a diverse set of difficult control tasks. These existing formulations do not address the gap between design with continuous time models and the discrete time sampled implementation of the resulting controllers, often leading to poor performance on hardware platforms. We propose an approach to close this gap by synthesizing sampled-data counterparts to these CLF-based controllers, specified as quadratically constrained quadratic programs (QCQPs). Assuming feedback linearizability and stable zero-dynamics of a system's continuous time model, we derive practical stability guarantees for the resulting sampled-data system. We demonstrate improved performance of the proposed approach over continuous time counterparts in simulation., 6 pages, 1 figure, submitted to IEEE L-CSS / CDC 2021

This paper studies the performance of a feedback control loop closed via an error-free digital communication channel with transmission delay. The system comprises a discrete-time noisy linear time-invariant (LTI) plant whose single measurement output is mapped into its single control input by a causal, but otherwise arbitrary, coding and control scheme. We consider a single-input multiple-output (SIMO) channel between the encoder-controller and the decoder-controller which is lossless and imposes random time delay. We derive a lower bound on the minimum average feedback data rate that guarantees achieving a certain level of average quadratic performance over all possible realizations of the random delay. For the special case of a constant channel delay, we obtain an upper bound by proposing linear source-coding schemes that attain desired performance levels with rates that are at most 1.254 bits per sample greater than the lower bound. We supplement our results with a numerical experiment demonstrating that the obtained bounds and operational rates are increasing functions of the constant delay.

This paper deals with the design of output feedback for multivariable (MIMO) linear discrete-time systems subject to actuator saturation. The saturation is included in a convex hull and a saturation-dependent Lyapunov function is used to ensure stability of the closed-loop. The guaranteed domain of attraction is enlarged by an iterative algorithm using linear matrix inequalities (LMIs). In numerical examples, the new approach is compared to the state of the art methods. Therefore, quadratic and saturation-dependent Lyapunov functions are used. It is shown that the new approach leads to less conservative results in both cases. Even with quadratic Lyapunov functions, the new approach provides less conservative results than existing methods with saturation-dependent Lyapunov functions.

We recently developed the quadratic integration- exploited model predictive current control (QIMPCC) and the binary capacitor voltage control-based modular multilevel converter (BCVC-MMC) with a new hybrid arm design to effectively enhance the DC-AC power-conversion quality-and- efficiency and the closed-loop control performance of MPCC- based multilevel converters in low nominal DC-voltage applications. Due to the technical limits of its predicted cost- function optimization method utilizing a finite three-phase switching combination set, QIMPCC cannot be directly applied to BCVC-MMC; moreover, there is room for improved closed-loop control performance. To address these drawbacks, this paper proposes the voltage-control reference estimation (VCRE)-based QIMPCC exploiting the QI method’s superior accuracy. The proposed algorithm substantially improves the closed-loop control performance of this class of converters. Finally, simulation results demonstrate and quantify the claimed technical advantages.

This article investigates the cooperative output regulation problem for discrete-time heterogeneous multiagent Markov jump systems. Two cases are studied: 1) output regulation quadratic control in the case where the exosystem is accessible to all agents and 2) cooperative output regulation quadratic control in the case where only a part of agents can directly communicate with the exosystem. The hidden Markov models are employed to describe the asynchronous modes of the agents and their corresponding controllers. Via the jumping regulator equation, asynchronous control laws are constructed and the algorithms to obtain control parameters are presented in terms of linear matrix inequalities. For the first case, the optimal synchronous/mode-dependent control law, which is a special case of the asynchronous control protocol, is also given via the stochastic dynamic programming approach. Finally, an example is given to illustrate the effectiveness of the proposed approaches.

The task of determining the end point (DEP) of the transient process (TP) in the measuring circuit (MC) during testing of complex objects (CO) is currently relevant. The real MC has a priori parametric uncertainty (PU). It is PU that determines the significant dynamic error (DE) of the first kind when determining the end of the dynamic test mode. For example, estimates of the relative error of DEP TP are known to arise due to the improper application of the interval type criterion (ITC) for CO with a first-order dynamic model. The importance of solving this problem is emphasized by its inclusion in Russian and foreign standards on methods of conducting CO tests in various technologies. Previously, we found the presence of a critical constant criterion (CCC), which is determined by the nominal value of the system parameter (SP) PU. Such a modified interval criterion should ensure acceptable DEP quality. Robust properties are provided due to a specially developed algorithm for the functioning of the ITC. Based on the analysis of the dynamics of the increments of the deterministic TP signal, a criterion for evaluating the quality of automatic monitoring of the duration of the observation interval (DOI) control is developed. Moreover, the evaluation criterion has a quadratic character, which made it possible to apply extreme control methods. A block diagram of the DOI control based on the processing of the TP measurement signal is developed. A digital simulation of the modified interval criterion is performed. The simulation showed the effectiveness of the proposed solution of the DEP TP problem in the possible range of variations of the MC mathematical model. Moreover, the solution is guaranteed to provide DE at the acceptable level.

International audience; We investigate discrete-time closed-loop dynamics consisting of a linear plant, a linear controller and a wireless network that connects the sensors and the actuators to the control unit. The objective, and the main contribution of this work, is the static output feedback control synthesis under given network specifications. Precisely, the network features are formulated in terms of stochastic allowable transmission interval (SATI) which is a concept well-suited for the time-triggered control of wireless network control systems (WNCS). Given SATI parameters, we provide sufficient conditions in terms of linear matrix inequalities (LMIs) under which we can design a static output feedback controller that stabilizes the closed-loop WNCS in mean-square sense. Moreover, we guarantee that a quadratic control cost is less than a given bound. Consequently, the results can be used to ensure not only stability but also desired control performances for the WNCS and its SATI characteristics.

This paper considers the partial quadratic eigenvalue assignment problem (PQEAP) in active vibration control using state feedback. A receptance method is proposed for PQEAP such that the norms of feedback matrices and the condition number of close-loop system are simultaneously minimized. The real form of the proposed method is developed. The proposed method only uses receptance matrices and the unwanted eigenpairs of open-loop system. The system matrices and the unchanged eigenpairs of open-loop system are not required. The efficiency of the proposed method is illustrated by some numerical examples.

This article presents a saturation controller design method for a type of nonlinear time-delay systems. Different from the current works, the nonlinear systems considered herein satisfy an incremental quadratic constraint, which is a more general nonlinearity. Firstly, for the constraint in the actuator, the convex set theory is employed to transform the saturated feedback law into a parameterized linear convex hull set. Next, the convex hull Lyapunov function is constructed to obtain sufficient conditions for exponential stability of the closed-loop system. In addition, by solving an optimization problem, the maximal estimation of domain of attraction is also determined. Finally, numerical examples are provided to verify the proposed method.