An Event‐Triggered Predefined Time Adaptive Control for High‐Order Nonlinear MIMO Systems

ABSTRACT

This paper considers the problem of predefined time event‐triggered control for high‐order multi‐input and multi‐output (MIMO) nonlinear systems. Neural networks (NNs) are employed to approximate the unknown nonlinear functions. A filter with predefined time stability is constructed to reduce the computational complexity. To reduce the controller's execution time and conserve communication resources, an event‐triggered mechanism (ETM) with a relative threshold strategy is constructed. Based on the backstepping recursive framework, a neural network adaptive predefined time event‐triggered control scheme is proposed. Using Lyapunov stability theory, it is proved that both the tracking errors and the filtering errors in the closed‐loop system can reach a small neighborhood around zero within the predefined time. Finally, a simulation example is provided to validate the stability of the system and the convergence of the tracking errors.