Event-triggered adaptive dynamic programming for decentralized tracking control of input constrained unknown nonlinear interconnected systems

Wu, Qiuye1; Zhao, Bo2; Liu, Derong3,4; Polycarpou, Marios M.5,6

2023

10.1016/j.neunet.2022.10.025

NEURAL NETWORKS   影响因子和分区

0893-6080

1879-2782

This paper addresses decentralized tracking control (DTC) problems for input constrained unknown nonlinear interconnected systems via event-triggered adaptive dynamic programming. To reconstruct the system dynamics, a neural-network-based local observer is established by using local input-output data and the desired trajectories of all other subsystems. By employing a nonquadratic value function, the DTC problem of the input constrained nonlinear interconnected system is transformed into an optimal control problem. By using the observer-critic architecture, the DTC policy is obtained by solving the local Hamilton-Jacobi-Bellman equation through the local critic neural network, whose weights are tuned by the experience replay technique to relax the persistence of excitation condition. Under the event-triggering mechanism, the DTC policy is updated at the event-triggering instants only. Then, the computational resource and the communication bandwidth are saved. The stability of the closed -loop system is guaranteed by implementing event-triggered DTC policy via Lyapunov's direct method. Finally, simulation examples are provided to demonstrate the effectiveness of the proposed scheme.(c) 2022 Elsevier Ltd. All rights reserved.

Adaptive dynamic programming Event-triggered control Decentralized tracking control Input constraints Experience replay Neural networks

SCI ; EI

英语

其他

National Natural Science Foundation of China["62073085","61973330"] ; Beijing Natural Science Foundation[4212038] ; Open Research Project of the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences[20210108]

Computer Science ; Neurosciences & Neurology

Computer Science, Artificial Intelligence ; Neurosciences

WOS:000926142600008

PERGAMON-ELSEVIER SCIENCE LTD

20230213352285

Adaptive control systems ; Closed loop control systems ; Large scale systems ; Navigation ; Neural networks ; Optimal control systems

Control Systems:731.1 ; Optimization Techniques:921.5 ; Systems Science:961

COMPUTER SCIENCE

Web of Science

1.Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Peoples R China
2.Beijing Normal Univ, Sch Syst Sci, Beijing 100875, Peoples R China
3.Southern Univ Sci & Technol, Dept Mech & Energy Engn, Shenzhen 518055, Peoples R China
4.Univ Illinois, Dept Elect & Comp Engn, Chicago, IL 60607 USA
5.Univ Cyprus, Dept Elect & Comp Engn, CY-2109 Nicosia, Cyprus
6.Univ Cyprus, KIOS Res Ctr Excellence, CY-2109 Nicosia, Cyprus

Wu, Qiuye,Zhao, Bo,Liu, Derong,et al. Event-triggered adaptive dynamic programming for decentralized tracking control of input constrained unknown nonlinear interconnected systems[J]. NEURAL NETWORKS,2023,157:336-349.

Wu, Qiuye,Zhao, Bo,Liu, Derong,&Polycarpou, Marios M..(2023).Event-triggered adaptive dynamic programming for decentralized tracking control of input constrained unknown nonlinear interconnected systems.NEURAL NETWORKS,157,336-349.

Wu, Qiuye,et al."Event-triggered adaptive dynamic programming for decentralized tracking control of input constrained unknown nonlinear interconnected systems".NEURAL NETWORKS 157(2023):336-349.