Event-triggered adaptive neural-network control of nonlinear MIMO systems

Abstract

This article investigates an adaptive neural networks (NNs) tracking control design issue for nonlinear multi-input and multi-output (MIMO) systems involving the sensor-to-controller event-triggered mechanism (ETM). In the design, NNs are utilized to approximate the unknown nonlinear functions. A sensor-to-controller ETM is designed to save unnecessary transmission and communication resources. Subsequently, a first-order filter technique is presented to solve the problem that the virtual control function is not differentiable. Furthermore, an event-triggered adaptive NNs control strategy is presented by constructing Lyapunov functions and using adaptive backstepping recursive design. It is demonstrated that the presented scheme can ensure the whole closed-loop signals are uniformly ultimately bounded without exhibiting the Zeno behavior. Finally, a numerical simulation example confirms the effectiveness of the presented adaptive event-triggered control (ETC) approach.