Abstract
This paper proposes an adaptive neural network (NN) output-feedback synchronization controller for the nonlinear stochastic systems driven by Levy processes, which consist of Wiener and compensated Poisson process. By employing the generalized Ito’s formula combined with Lyapunov function method, it is proved that the proposed controller can ensure that all signals of closed system are bounded in probability. Under the backstepping control framework of stochastic process including Levy noises, the event-triggered control technology is used to reduce the utilization of communication resources. Moreover, the command filtered control technology is introduced into the controller to avoid “explosion of complexity” and obtain the derivatives for virtual control functions continuously. Simulation proves the feasibility of the proposed control method.
Highlights
U P to adaptive synchronization issues with stochastic noises have become a hot topic in nonlinear system studies [1]
It has made some progresses in the field of nonlinear stochastic system control with Lévy noise, such as adaptive control [5], linear matrix inequality (LMI) control [1], [6], sliding mode control (SMC) [3] and so on
In [10], the authors proved that the proposed fuzzy backstepping control approach for a class of uncertain stochastic nonlinear systems with both unmodeled dynamics and unmeasured states can guarantee that all the signals of the VOLUME 4, 2016
Summary
U P to adaptive synchronization issues with stochastic noises have become a hot topic in nonlinear system studies [1]. Based on event-triggered scheme, [18] studied the problem of adaptive fuzzy tracking control for strict-feedback stochastic nonlinear systems. Based on the previous discussion, this paper designs a command filtered adaptive neural network backstepping controller based on event-triggered mechanism to reach nonlinear stochastic systems synchronization with Lévy noise. Compared with the current research, this work has the following contributions: Firstly, a command filtered adaptive neural network backstepping controller is developed in this paper based on eventtriggered mechanism to solve the nonlinear stochastic systems synchronization problem with Lévy noise.
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