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

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 closed-loop 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.