Observer‐based adaptive neural output‐feedback event‐triggered control for discrete‐time nonlinear systems using variable substitution - Wang, Min; Huang, Longwang; Zhao, Zhijia; Yang, Chenguang (International journal of robust and nonlinear control, 2021)
In this paper, an event‐triggered adaptive neural output feedback control scheme is developed for a class of uncertain discrete‐time strict‐feedback nonlinear systems subject to immeasurable system states and network resource limitation. An i‐step ahead predictor is synthesized to obtain the future signal of the reference orbit. By combining the neural observer and the variable substitution technology, an event‐triggered adaptive neural control scheme is developed, thereby estimating the immeasurable system states and avoiding the n‐step delays of the existing controller. To promote the transient system performance, an improved triggering condition is designed to increase the number of triggering events in the transient process. The stability analysis of the closed‐loop system is divided into two parts to deal with the challenge from the simultaneous presence of state estimations, unknown system dynamics, and aperiodical controller weight updating laws. The proposed scheme achieves the state estimation, guarantees the output tracking performance with the improved transient control performance, and reduces the communication resource. Simulation studies on a numerical example and a networked robot manipulator are, respectively, implemented to show the validity of the proposed scheme.