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Abstract: The stochastic finite-time boundedness problem is considered for a class of uncertain Markovian jumping neural networks (MJNNs) that possess partially known transition jumping parameters. The transition of the jumping parameters is governed by a finite-state Markov process. By selecting the appropriate stochastic Lyapunov–Krasovskii functional, sufficient conditions of stochastic finite time boundedness of MJNNs are presented and proved. The boundedness criteria are formulated in the form of linear matrix inequalities and the designed algorithms are described as optimization ones. Simulation results illustrate the effectiveness of the developed approaches. [Copyright &y& Elsevier]

This paper presents an integrated self-triggered control strategy with convergence guarantees for model-free continuous-time systems using reinforcement learning. To consider the control cost and triggering consumption in the self-triggered scheme simultaneously, an integrated cost function is proposed. With this integrated cost function, the trade-off between the triggering occupation and control performance could be adjusted according to different requirements. Then, the actor-critic framework of reinforcement learning is employed to learn the control inputs and triggering intervals by minimizing the corresponding integrated cost function. Considering the divergent characteristics between the control inputs and triggering intervals, two different actors are utilized to learn the triggering strategy and control policy, respectively. Also, the convergence of the developed model-free self-triggered control learning algorithm is proved to ensure the limited learning duration of both the control policy and triggering strategy. The proposed framework can be used to design self-triggered controllers for a wide range of engineering systems with unknow dynamics, including control of aircraft, robots, chemical processes, and other automated systems. Finally, the effectiveness and superiorities of the proposed method are verified by an illustrative example. [ABSTRACT FROM AUTHOR]

In this paper, we study the asynchronous observer-based sliding mode control for a class of discrete-time Markov jump systems with time-delay and conic-type nonlinearities. Firstly, we assume there exists a detector described by the hidden Markov model that provides estimated information of the system mode. Based on the detector, a common sliding surface and an observer-based controller are devised. Secondly, we select the appropriate Lyapunov functional and applying linear matrix inequalities techniques to get sufficient conditions such that the controller and observer gains are obtained, which can guarantee the stochastic stability and strict dissipativity of the sliding mode dynamics. Furthermore, the given H ∞ and passive performance are also discussed. Thirdly, to make the system trajectory get to the predetermined sliding surface, a sliding mode control law is proposed. Finally, an application related to the automotive electronic throttle body control system is given to illustrate the correctness and feasibility of this designed approach. [Display omitted] • For nonlinear MJSs, the observer-based asynchronous SMC scheme is designed. • For the reachability of the sliding surface, a SMC law is given. • By a practical example, the effectiveness of this designed approach is illustrated. [ABSTRACT FROM AUTHOR]