Dynamic event-triggered control for discrete-time nonlinear Markov jump systems using policy iteration-based adaptive dynamic programming

This paper investigates a dynamic event-triggered optimal control problem of discrete-time (DT) nonlinear Markov jump systems (MJSs) via exploring policy iteration (PI) adaptive dynamic programming (ADP) algorithms. The performance index function (PIF) defined in each subsystem is updated by utilizing an online PI algorithm, and the corresponding control policy is derived via solving the optimal PIF. Then, we adopt neural network (NN) techniques, including an actor network and a critic network, to estimate the iterative PIF and control policy. Moreover, the designed dynamic event-triggered mechanism (DETM) is employed to avoid wasting additional resources when the estimated iterative control policy is updated. Finally, based on the Lyapunov difference method, it is proved that the system stability and the convergence of all signals can be guaranteed under the developed control scheme. A simulation example for DT nonlinear MJSs with two system modes is presented to demonstrate the feasibility of the control design scheme.     

Adaptive event-triggered dynamic distributed control of switched positive systems with switching faults

This paper designs the dynamic output-feedback controller of switched positive systems subject to switching faults using an improved adaptive event-triggering mechanism. An adaptive event-triggering condition is addressed in the form of 1-norm by virtue of the measurable outputs of distributed sensors and the corresponding error. An error-based closed-loop control system whose dynamic variable relies on a state observer is obtained. A multiple copositive Lyapunov function is constructed to deal with the positivity and stability of the systems. The matrix decomposition and linear programming approaches are used to design and compute the controller and observer gains. An improved average dwell time scheme is proposed to handle the switching faults. The contributions of this paper lie in that: (i) An adaptive event-triggering mechanism is established for switched positive systems, (ii) A framework on the fault of switching signal is constructed, and (iii) A dynamic distributed controller is proposed for the considered systems. Finally, two illustrative examples are given to verify the effectiveness of the obtained results.     

HMM-based quantized dissipative control for 2-D Markov jump systems

In this paper, the dissipative quantized control problem is addressed for Markov jump two-dimensional systems based on Roesser model, in which both asynchronous phenomenon and signal quantization between system modes and controller modes are taken into consideration simultaneously. Moreover, the hidden Markov model (HMM) is adopted to tackle such an asynchronous phenomenon. The principal goal is to devise a state feedback controller, which guarantees that the established closed-loop system achieves asymptotic mean square stability as well as satisfies a prescribed extended dissipative property. Drawing support from Lyapunov function approach and inequality technique, some less conservative criteria ensuring the implementability of the desired controller are derived. Ultimately, the availability and practicability of the developed results are certified through a simulation example.     

Time-triggered and event-triggered control of switched affine systems via a hybrid dynamical approach

This paper focuses on the design of both periodic time- and event-triggered control laws of switched affine systems using a hybrid dynamical system approach. The novelties of this paper rely on the hybrid dynamical representation of this class of systems and on a free-matrix min-projection control, which relaxes the structure of the usual Lyapunov matrix-based min-projection control. This contribution also presents an extension of the usual periodic time-triggered implementation to the event-triggered one, where the control input updates are permitted only when a particular event is detected. Together with the definition of an appropriate optimization problem, a stabilization result is formulated to ensure the uniform global asymptotic stability of an attractor for both types of controllers, which is a neighborhood of the desired operating point. Finally, the proposed method is evaluated through a numerical example.     

Reliable stability and stabilizability for complex-valued memristive neural networks with actuator failures and aperiodic event-triggered sampled-data control

This study addresses the stability and stabilizability problems for complex-valued memristive neural networks (CVMNNs) with actuator failures via reliable aperiodic event-triggered sampled-data control. Different from the traditional control methods with time-triggered mechanism, an aperiodic event-triggered sampled-data control scheme is first proposed for CVMNNs. Taking the influence of actuator failures into account, a reliable controller is designed. In comparison with the existing control approaches, the one here is not only more applicable but effective to save the communication resources for CVMNNs. Then, a new Lyapunov–Krasovskii functional (LKF) is introduced, which can fully capture the information of sampling and complex-valued activation functions. Based on the LKF and some new estimation techniques, novel stability and stabilizability criteria are established, and the desired reliable aperiodic event-triggered sampled-data controller gains are obtained simultaneously. Finally, numerical simulations are provided to verify the effectiveness of the obtained theoretical results.     

Sliding mode control based on multi-node transmission hybrid scheduling for Markovian jump systems with constrained bandwidth

The sliding mode control (SMC) problem is investigated for Markovian jump systems (MJSs) under constrained communication bandwidth. A multi-node hybrid transmission strategy composed of an event-triggered protocol and the weight try-once-discard (WTOD) protocol is introduced into the sensor-to-controller (S/C) channel. Its key feature is that by using two dynamic thresholds, the number of the transmitted components may be dynamically regulated, not just the one with the largest difference as in the conventional WTOD protocol. That may greatly increase the flexibility of transmission under limited bandwidth, meanwhile, it is also beneficial to balance system performance and network burden. Then, a compensating strategy is proposed via the previous transmitted signals, and a scheduling signal-dependent sliding mode controller is designed. By using mode-dependent Lyapunov function, both the stochastic stability and the reachability are analyzed under different transmission cases, respectively. Moreover, an optimization problem on convergent domain is formulated and the binary-encoded genetic algorithm (GA) is utilized to search a desirable sliding gain. Finally, the proposed multi-node hybrid scheduling-based SMC scheme is illustrated via simulation results.     

Multiple delay-dependent guaranteed cost control for uncertain switched random nonlinear systems against intermittent sensor and actuator faults

In this paper, guaranteed cost control is investigated for switched random nonlinear systems against multiple state delays, model uncertainties, intermittent sensor and actuator faults. Other factors containing nonlinear dynamics, external disturbances as well as measurement noise are also considered. This is the first try to realize guaranteed cost control for uncertain switched random nonlinear systems against multiple time delays. In practice, color noise is more common than white noise in some specific situations. Thus, this paper considers random systems with color noise. In contrast to the previous study works, the suggested system can be applied to a wider range. First, a dynamic full-order output feedback controller is established to make the system stable. And an entire closed-loop system is got to achieve guaranteed cost control. Then, the multiple delay-dependent sufficient conditions are acquired through the piecewise Lyapunov function in the framework of linear matrix inequalities (LMIs). In the meantime, controller gain matrices are obtained. At last, two simulation examples are presented to verify the availability of the suggested approach.     

Reliable controller for nonlinear multiagent system with additive time varying delay and nonlinear actuator faults

In this paper, the problem of nonlinear multiagent system with reliable control is taken into account. The prescribed system consists of additive time-varying delay, actuator faults with both linear and nonlinear functions. The main focus of this paper is to design a reliable control which guarantees the stability and consensus condition of the proposed system. Actuator faults with linear and nonlinear functions are considered in the control input. From the implementation of integral inequality, the linear matrix inequality format is derived by constructing the suitable Lyapunov Krasovskii functional for the specified system. Terminally numerical examples are furnished for the efficiency of the specified method.     

Robust reliable sampled‐data H∞ control for uncertain stochastic systems with random delay

In this article, the problem of robust reliable sampled‐data control for a class of uncertain nonlinear stochastic system with random delay control input against actuator failures has been studied. In the considered system, the parameter uncertainty satisfies the norm bounded condition and the involved time delay in control input are assumed to be randomly time‐varying which is modeled by introducing Bernoulli distributed sequences. By constructing a novel Lyapunov–Krasovskii functional involving with the lower and upper bounds of the delay, a new set of sufficient conditions are derived in terms of linear matrix inequalities (LMIs) for ensuring the robust asymptotic stability of the uncertain nonlinear stochastic system with random delay and disturbance attenuation level about its equilibrium point for all possible actuator failures. In particular, Schur complement together with Jenson's integral inequality is utilized to substantially simplify the derivation in the main results. The derived analytic results are applied to design robust reliable sampled‐data controller for hanging crane structure model and simulation results are provided to demonstrate the effectiveness of the proposed control law. © 2014 Wiley Periodicals, Inc. Complexity 21: 42–58, 2015     

Stochastic adaptive control for continuous-time linear systems with quadratic cost

An adaptive control problem is formulated and solved for a completely observed, continuous-time, linear stochastic system with an ergodic quadratic cost criterion. The linear transformationsA of the state,B of the control, andC of the noise are assumed to be unknown. Assuming only thatA is stable and that the pair (A, C) is controllable and using a diminishing excitation control that is asymptotically negligible for an ergodic, quadratic cost criterion it is shown that a family of least-squares estimates is strongly consistent. Furthermore, an adaptive control is given using switchings that is self-optimizing for an ergodic, quadratic cost criterion.This research was partially supported b y NSF Grants ECS-9102714, ECS-9113029, and DMS-9305936.     

Stochastic stability for Markovian jump linear systems associated with a finite number of jump times

This paper deals with a stochastic stability concept for discrete-time Markovian jump linear systems. The random jump parameter is associated to changes between the system operation modes due to failures or repairs, which can be well described by an underlying finite-state Markov chain. In the model studied, a fixed number of failures or repairs is allowed, after which, the system is brought to a halt for maintenance or for replacement. The usual concepts of stochastic stability are related to pure infinite horizon problems, and are not appropriate in this scenario. A new stability concept is introduced, named stochastic τ-stability that is tailored to the present setting. Necessary and sufficient conditions to ensure the stochastic τ-stability are provided, and the almost sure stability concept associated with this class of processes is also addressed. The paper also develops equivalences among second order concepts that parallels the results for infinite horizon problems.     

A data-driven switching control approach for braking systems with constraints

Braking control is of paramount importance in guaranteeing driving safety and comfort, but it is a well-known challenging task, due to the highly nonlinear and road condition-dependent behavior of the vehicle. Existing braking controllers typically rely on accurate models of the vehicle dynamics and the vehicle–road interaction, which are quite difficult to be retrieved in practice. In the wake of the data-driven control paradigm, we propose a model-free and fully data-based braking control method. The architecture of our scheme is two-layered, featuring: an inner switching controller, directly designed from data to match a given closed-loop behavior, and an outer predictive reference governor, exploited to enforce constraints and possibly improve the overall braking performance. The effectiveness of the approach is shown in a simulation environment, by providing a sensitivity analysis to the main tuning knobs of the method.     

H∞ robust control based on event‐triggered sampling for hybrid systems with singular Markovian jump

The problem of H_∞ robust control based on event‐triggered sampling for a class of singular hybrid systems with Markovian jump is considered in this paper. The primary object of this paper here is to design the event‐triggered sampling controller for a class of uncertain singular Markovian systems, and two fundamental issues on mean square exponential admissibility and H_∞ robust performance are fully addressed. By making use of a suitable Lyapunov functional, in combination with both infinitesimal operator and linear matrices inequalities(LMIs), the sufficient criteria are derived to guarantee the controlled singular hybrid system with Markovian jump is robustly exponentially mean‐square admissible and has a prescribed H_∞ performance γ. Finally, a typical RLC circuit system is given to show the effectiveness of the proposed control method.     

Robust reliable L2 – L∞ control for continuous‐time systems with nonlinear actuator failures

This article examines the reliable L₂ – L_∞ control design problem for a class of continuous‐time linear systems subject to external disturbances and mixed actuator failures via input delay approach. Also, due to the occurrence of nonlinear circumstances in the control input, a more generalized and practical actuator fault model containing both linear and nonlinear terms is constructed to the addressed control system. Our attention is focused on the design of the robust state feedback reliable sampled‐data controller that guarantees the robust asymptotic stability of the resulting closed‐loop system with an L₂ – L_∞ prescribed performance level γ > 0, for all the possible actuator failure cases. For this purpose, by constructing an appropriate Lyapunov–Krasovskii functional (LKF) and utilizing few integral inequality techniques, some novel sufficient stabilization conditions in terms of linear matrix inequalities (LMIs) are established for the considered system. Moreover, the established stabilizability conditions pave the way for designing the robust reliable sampled‐data controller as the solution to a set of LMIs. Finally, as an example, a wheeled mobile robot trailer model is considered to illustrate the effectiveness of the proposed control design scheme. © 2016 Wiley Periodicals, Inc. Complexity 21: 309–319, 2016     

Fuzzy guaranteed cost output tracking control for fuzzy discrete‐time systems with different premise variables

This article investigates the problem of output tracking control for a class of discrete‐time interval type‐2 (IT2) fuzzy systems subject to mismatched premise variables. Based on the IT2 Takagi–Sugeno (T–S) fuzzy model, the criterion to design the desired controller is obtained, which guarantees the closed‐loop system to be asymptotically stable and satisfies the predefined cost function. Moreover, the controller to be designed does not need to share the same premise variables of the system, which enhances the flexibility of controller design and reduces the conservativeness. Finally, two examples are provided to demonstrate the effectiveness of the method proposed in this article. © 2015 Wiley Periodicals, Inc. Complexity 21: 265–276, 2016