Search

This article aims to enlarge the applicability of event-triggered control for stochastic systems, and is particularly concerned with event-triggered stabilization for stochastic systems with control-dependent diffusion term. Roughly treating stochastic noises as unfavorable factors for system stability, the related works are based on Lyapunov-type conditions for ISS property with respect to sampling error as those in the nonstochastic context. The conditions imply the existence of a twice-differentiable Lyapunov function with negative-definite infinitesimal under continuous-time control, and indeed exclude many familiar stochastic systems with control-dependent diffusion term which are continuously stabilizable while no such Lyapunov function exists. As the main contribution of this article, an enlarged framework of event-triggered stabilization is established for stochastic systems with control-dependent diffusion term, starting from the system stability under continuous-time control, rather than from Lyapunov-type conditions for ISS property. Specifically, with moment exponential stability assumed under continuous-time control, elementary event-triggering mechanisms are proposed with an enforced minimum inter-execution interval, under which the event-triggered controller can render the system exponentially stable both in the sense of expectation and in the almost sure sense. Particularly, a distinctive analysis of closed-loop stability is performed via delicate comparison between the solutions under the event-triggered, and continuous-time controls. Within the framework, inclusive Lyapunov-type conditions incorporating the underlying positive effects of stochastic noises are presented for event-triggered exponential stabilization of the stochastic systems, where the candidate Lyapunov function involved is weakened to be nondifferentiable at the origin and, even, to allow non-negative infinitesimal under continuous-time control. [ABSTRACT FROM AUTHOR]

This article addresses the event-triggered stabilization for continuous-time stochastic systems. Due to stochastic effects, the system state is hard to predict and dominate, and the system behavior would vary with every trial (i.e., sample path) even for the same initial condition. This gives rise to substantial challenges, especially, in determining the execution/sampling times and assessing the closed-loop performance, which urges us to develop powerful and sophisticated tools/methods for the analysis and design of stochastic event-triggered control. In this article, basic theorems, particularly a stochastic convergence theorem, are first proposed for stochastic event-triggered controlled systems. Then, a framework of event-triggered stabilization is established for the stochastic systems without applying the well-known Lyapunov theorems. Specifically, we present conditions under which event-triggered stabilization is feasible for the systems. Accordingly, static and dynamic event-triggering mechanisms are proposed with enforcing a fixed positive lower bound for the interexecution times. While avoiding infinitely fast execution/sampling, both asymptotic stabilization and exponential stabilization are achieved for the systems by the proposed stochastic convergence theorem. The involved analysis is helpful to form a pattern for stochastic event-triggered controlled systems. As the direct application of the established framework, the constructive design of event-triggered controller is realized, respectively, for two representative classes of stochastic systems. [ABSTRACT FROM AUTHOR]

This paper addresses the event-triggered tracking for a class of uncertain nonlinear systems. Essentially different from the related works, the nonlinear systems allow serious (parametric) uncertainties, while no further a priori information, excepting some coarse information, is required on the reference signal to be tracked. This makes event-triggered control more challenging, partially because the sampling error cannot be precisely bounded any more. As main contributions of this paper, new adaptive event-triggered tracking schemes are proposed for the systems in two event-triggering architectures with different roles of the event-triggering mechanism on the information transmitting and control computing/updating. Much importantly, a dynamic gain is incorporated in either scheme not only to counteract the serious uncertainties, but also to overcome the bad influence of the sampling error. Based on the dynamic gain, two adaptive event-triggered controllers are designed with distinct event-triggering mechanisms, respectively. Particularly, to ensure application flexibility, one of the triggering mechanisms is rendered relatively independent of the controller signal. The designed event-triggered controllers are shown to achieve the practical tracking for the systems, that is, the prespecified arbitrary tracking accuracy can be guaranteed (a comparable objective to that via continuous-time control), while avoiding infinitely fast sampling/execution. [ABSTRACT FROM AUTHOR]