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

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.

Automated summary

This paper proposes an event-triggered adaptive dynamic programming method to solve the decentralized tracking control problem for input constrained unknown nonlinear interconnected systems. A neural-network-based local observer is established to reconstruct the system dynamics using local input-output data and desired trajectories. The DTC problem is transformed into an optimal control problem using a nonquadratic value function. The DTC policy is obtained by solving the local Hamilton-Jacobi-Bellman equation through the observer-critic architecture, with weights tuned by the experience replay technique. Simulation examples demonstrate the effectiveness of the proposed scheme.