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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 is devoted to the stabilization via adaptive feedback for a class of uncertain coupled reaction–diffusion dynamics in the actuation path of an ordinary differential equation (ODE) system. The system under investigation, a class of coupled parabolic partial differential equation (PDE)–ODE systems, is more representative since the dynamics in actuation path (i.e., the PDE subsystem) are coupled rather than uncoupled parabolic equations and hence includes those in the related literature as special cases. Moreover, serious parametric uncertainties are present in both PDE and ODE subsystems, which bring obstacles to the traditional methods on this issue. To solve the control problem, an infinite-dimensional backstepping transformation with time-varying matrix-valued kernel functions is adopted to change the original system into a new one. Then, an adaptive state-feedback controller is designed for the new system, which guarantees that all the closed-loop system states are bounded while regulating the original system states to zero. Finally, a simulation example is provided to illustrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper considers the control design with prescribed performance for uncertain nonlinear systems. Different from the existing literature, the systems under investigation possess unknown control directions and nonparametric uncertainties. Even so, the control objective to be achieved is still refined, not only to ensure the basic performance on stabilization, but also to guarantee certain prescribed performance required in real applications. Motivated by the funnel control method, a time-varying stabilizing scheme is developed for the systems, to effectively handle the serious uncertainties and to successfully guarantee the states to converge to zero with a prescribed rate. [ABSTRACT FROM AUTHOR]