Event-triggered adaptive dynamic programming for decentralized tracking control of input constrained unknown nonlinear interconnected systems. (January 2023)

• Record Type: Journal Article
• Authors: Wu, Qiuye; Zhao, Bo; Liu, Derong; Polycarpou, Marios M.
• Is Part Of: Neural networks. Volume 157(2023)
• Page Start: 336
• Page End: 349
• Publication Date: 2023-01
• Subjects: Adaptive dynamic programming -- Event-triggered control -- Decentralized tracking control -- Input constraints -- Experience replay -- Neural networks; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2022.10.025
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: 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.