Regular Papers

International Journal of Control, Automation, and Systems 2024; 22(2): 581-592

https://doi.org/10.1007/s12555-022-1127-z

© The International Journal of Control, Automation, and Systems

Neuro-adaptive Event-triggered Optimal Control for Power Battery Systems With State Constraints

Xiaoxuan Pei, Kewen Li, and Yongming Li*

Liaoning University of Technology

Abstract

This paper investigates an adaptive neural networks (NNs) event-triggered optimal control method for the second-order resistance capacitance (RC) equivalent circuit system with state constraints. The NNs are used to estimate the unknown nonlinear functions. In order to constrain the states within the designed boundary in optimal control strategy, the barrier Lyapunov function (BLF) method is taken into account. Furthermore, to economic the transmission resources, the adaptive NNs event-triggered optimizing control strategy is developed by employing the relative threshold strategy. The proposed optimal control strategy is not only able to satisfy the stability of closedloop system, but also can guarantee the performance index functions minimized when all states remain within the given boundaries. Finally, the effectiveness of the suggested control method is demonstrated by simulation.

Keywords Barrier Lyapunov function, event-triggered control, neural networks, optimal control, second-order RC equivalent circuit model.

Article

Regular Papers

International Journal of Control, Automation, and Systems 2024; 22(2): 581-592

Published online February 1, 2024 https://doi.org/10.1007/s12555-022-1127-z

Copyright © The International Journal of Control, Automation, and Systems.

Neuro-adaptive Event-triggered Optimal Control for Power Battery Systems With State Constraints

Xiaoxuan Pei, Kewen Li, and Yongming Li*

Liaoning University of Technology

Abstract

This paper investigates an adaptive neural networks (NNs) event-triggered optimal control method for the second-order resistance capacitance (RC) equivalent circuit system with state constraints. The NNs are used to estimate the unknown nonlinear functions. In order to constrain the states within the designed boundary in optimal control strategy, the barrier Lyapunov function (BLF) method is taken into account. Furthermore, to economic the transmission resources, the adaptive NNs event-triggered optimizing control strategy is developed by employing the relative threshold strategy. The proposed optimal control strategy is not only able to satisfy the stability of closedloop system, but also can guarantee the performance index functions minimized when all states remain within the given boundaries. Finally, the effectiveness of the suggested control method is demonstrated by simulation.

Keywords: Barrier Lyapunov function, event-triggered control, neural networks, optimal control, second-order RC equivalent circuit model.

IJCAS
June 2024

Vol. 22, No. 6, pp. 1761~2054

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