1. Southeast UniversitySoutheast University, NO.2 Sipailou Rd. , NanjingNanjing ChinaChina 210096210096
2. Southeast University, No.2 Sipailou Rd. , Nanjing Jiangsu China 210096
In this paper, we propose a novel event-triggered near-optimal control for nonlinear continuous-time systems. The receding horizon principle is utilized to improve the system robustness and obtain better dynamic control performance. In the proposed structure, we first decompose the infinite horizon optimal control into a series of finite horizon optimal problems. Then a learning strategy is adopted, in which an actor network is employed to approximate the cost function and an critic network is used to learn the optimal control law in each finite horizon. Furthermore, in order to reduce the computational cost and transmission cost, an event-triggered strategy is applied. We design an adaptive trigger condition, so that the signal transmissions and controller updates are conducted in an aperiodic way. Detailed stability analysis shows that the nonlinear system with the developed event-triggered optimal control policy is asymptotically stable. Simulation results on a single link robot arm with different noise types have demonstrated the effectiveness of the proposed method.
National Natural Science Foundation of China Grant 61803085 National Key R&D Program of China Grant 2018AAA0101400 National Natural Science Foundation of China Grant 61921004 National Natural Science Foundation of China Grant 61931020
Copyright 2020 Science China Press Co., Ltd. 《中国科学》杂志社有限责任公司 版权所有