An event‐triggered integer‐mixed adaptive dynamic programming for switched nonlinear systems with bounded inputs. (8th July 2021)

• Record Type: Journal Article
• Authors: Han, Xiumei; Qin, Haiqin; Wang, Zhitao; Xu, Ning; Zhao, Xudong; Zhao, Jinfeng
• Is Part Of: International journal of robust and nonlinear control. Volume 31:Number 15(2021)
• Page Start: 7280
• Page End: 7297
• Publication Date: 2021-07-08
• Subjects: constrained control input -- event‐triggered control -- integer‐mixed adaptive dynamic programming -- neural networks -- optimal control -- switched nonlinear systems; Automatic control -- Periodicals; Control theory -- Periodicals; Nonlinear systems -- Periodicals; 629.836
• DOI: 10.1002/rnc.5668
• Languages: English
• ISSNs: 1049-8923

Abstract

Abstract: This paper studies the optimal event‐triggered control for constrained‐input discrete‐time switched nonlinear systems. Firstly, the optimal time‐triggered control problem is analyzed based on the necessary optimality condition. Secondly, the optimal event‐triggered control problem is presented, and the optimal results are presented based on the periodic time‐triggered ones, and an event‐triggered integer‐mixed adaptive dynamic programming algorithm is put forward to obtain the optimal results. The proposed algorithm is only executed at trigger instants, which decreases the execution times compared with the periodic time‐triggered algorithm, and the neural networks are applied to approximate the control input and costate vector functions of each subsystem based on the triggered states. Thirdly, an event‐triggered condition is designed to make the closed‐loop switched system asymptotically stable. Fourthly, it is shown that during the iteration process, the value function sequence corresponding to the costate vector converges to the optimal value function. Finally, the simulation results demonstrate the effectiveness of the presented algorithm.