Event-Triggered Constrained H∞ Control Using Concurrent Learning and ADP

Xue，Shan1; Luo，Biao2; Liu，Derong3; Guo，Dongsheng1

10.1007/978-981-99-8132-8_26

2024

1865-0929

1865-0937

Communications in Computer and Information Science

1962 CCIS

341-352

In this paper, an optimal control algorithm based on concurrent learning and adaptive dynamic programming for event-triggered constrained H control is developed. First, the H control system under consideration is based on event-triggered constrained input and time-triggered external disturbance, which saves resources and reduces the network bandwidth burden. Second, in the implementation of the control scheme, a critic neural network is designed to approximate unknown value function. Moreover, concurrent learning techniques participate in weight training, making the implementation process simple and effective. Lastly, the stability of the system and the effectiveness of the algorithm are demonstrated through theorem proofs and simulation results.

Adaptive dynamic programming concurrent learning event-triggered control H∞ control neural networks

其他

英语

2-s2.0-85178555204

Scopus

1.School of Information and Communication Engineering,Hainan University,Haikou,570100,China
2.School of Automation,Central South University,Changsha,410083,China
3.School of System Design and Intelligent Manufacturing,Southern University of Science and Technology,Shenzhen,518055,China

Xue，Shan,Luo，Biao,Liu，Derong,et al. Event-Triggered Constrained H∞ Control Using Concurrent Learning and ADP[C],2024:341-352.