Dynamic event-based cooperative control of virtually coupled high-speed trains: an ADP-based optimal control scheme

Abstract

This paper is concerned with the cooperative control issue of virtually coupled high-speed trains (VCHSTs) under an adaptive dynamic programming (ADP) framework, where a dynamic event-triggered mechanism is employed to govern the communication between sensors and observers. A learning-based observer, equipped with a robust term, is exploited to mitigate the effect of the unknown nonlinear dynamics of the VCHSTs. Furthermore, to realise the distributed design, an auxiliary control signal is employed to simply the closed-loop system and also integrated into the cost function to handle the coupled nature of the investigated systems. The Hamilton–Jacobi–Bellman (HJB) equation is approximated by actor and critic networks in the ADP framework, which iteratively acquires the desired control strategy. By resorting to the common Lyapunov stability, some sufficient conditions are derived to achieve the desired observer gain and learning rates of NNs. Ultimately, the control scheme's efficiency is confirmed by simulations of VCHSTs.

Keywords:

• Neural networks
• optimisation control
• dynamic event-triggered mechanisms
• adaptive dynamic programming

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 62303253.