Self-triggering adaptive optimal control for nonlinear systems based on encoding mechanism

This paper deals with self-triggering adaptive optimal control for nonlinear continuous-time systems. We propose a novel self-triggering control structure concerning a special encoding mechanism, which combines the trigger time of control and sampling and reduces both the control time and the sampling time. Such a triggering structure ensures the existence of a maximum triggering time in self-triggering control. When the system expression is known, the encoding mechanism will lead to high quantitative accuracy at a limited channel transmission rate. Moreover, we also provide a new control algorithm and triggering conditions of the proposed structure. Specifically, this algorithm solves the optimal control strategy by using the cost function approximated by neural networks. Besides, the derived closed-loop system is proven to be asymptotically stable. Finally, two examples are provided to illustrate the effectiveness of the proposed control method. (C) 2021 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

Automated summary

This paper introduces a novel self-triggering control structure that combines the trigger time of control and sampling to reduce both the control and sampling time. The proposed algorithm solves the optimal control strategy using a cost function approximated by neural networks, resulting in an asymptotically stable closed-loop system.