【摘要】This paper investigates the adaptive event-triggered control problem for a class of nonlinear systems subject to periodic disturbances. To reduce the communication burden, a reliable relative threshold strategy is proposed. Fourier series expansion and radial basis function neural network are combined into a function approximator to model suitable time-varying disturbed function of known periods in strict-feedback systems. By combining the Lyapunov stability theory and the backstepping technique, the proposed adaptive control approach ensures that all the signals in the closed-loop system are bounded, and the tracking error can be regulated to a compact set around zero in finite time. Finally, simulation results are presented to verify the effectiveness of the theoretical results.
【作者单位】School of Automation and Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control Guangdong University of Technology Guangzhou 510006 China; Science Program Texas A&M University at Qatar Doha 23874 Qatar;