Adaptive Periodic Event‐Triggered Tracking Control for a Class of Switched Nonlinear Systems Based on Dual Sampling Quantization

ABSTRACT This article investigates a periodic event‐triggered control problem for a class of uncertain switched nonlinear systems with nonstrict feedback form. Based on sampling quantized output, a common neural observer is designed to deal with the unavailable states and unknown switching signals. Also by an input logarithmic quantizer, a novel quantized periodic event‐triggered control strategy is proposed to further reduce the communication load between controller and actuator. Compared with the existing quantization control research, the dual sampling quantizer proposed for the first time overcome the disadvantage of continuously monitoring the quantizing conditions. Based on the common Lyapunov function theory and the backstepping technique, a neural event‐triggered output feedback controller and the adaptive laws are obtained to achieve semiglobally uniformly ultimately bounded (SGUUB) stability of the closed‐loop switched systems. The tracking error can converge to a small neighborhood of the origin under arbitrary switching. Finally, the effectiveness and the applicability of the developed control strategy are verified by some simulations of a numerical example and a practical one.