Command Filtered Adaptive Neural Network Synchronization Control of Nonlinear Stochastic Systems With Lévy Noise via Event-Triggered Mechanism

This paper proposes an adaptive neural network (NN) output-feedback synchronization controller for the nonlinear stochastic systems driven by Lévy processes, which consist of Wiener and compensated Poisson process. By employing the generalized It&ohat;’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 Lévy 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.