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

Abstract

SummaryThis 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.