Observer‐based adaptive event‐triggered neural tracking control for nonlinear cyber‐physical systems with incomplete measurements

Abstract

Summary In this paper, an adaptive event‐triggered neural networks (NNs) tracking control problem is investigated for cyber‐physical Systems (CPSs) with incomplete measurements. The state variables can get unavailable or distorted in incomplete measurements because of data transmission problems, which can degrade the performance of the system. To solve these problems, the radial basis function neural networks (RBF NNs) control is used to approximate the unknown nonlinear function in CPSs, and the Butterworth Low‐pass Filter (LPF) is used to construct the NNs observer, which can estimate the immeasurable states. By using the Lyapunov function, the tracking error of the controller has limited to a small boundary. Based on backstepping control theory and event‐triggered theory, the control signal of the fixed threshold strategy is obtained and two adaptive controllers for CPSs are established, it can ensure that all the closed‐loop signals are uniformly ultimately bounded (UUB) in mean square and avoid the Zeno‐behavior. The simulation results confirm the feasibility and effectiveness of the controller.