Abstract:

An output feedback control algorithm is designed based on reinforcement learning for the optimal control problem of overhead crane system. A high gain observer (HGO) is designed using output data to estimate the unmeasurable states of the overhead crane system. Based on the estimated states from the high-gain observer, a policy iteration (PI) method is designed with integral reinforcement learning, which uses Critic and Actor neural networks to approximate the optimal value function and control strategy, and adjusts the neural network weights in real time through online adaptive algorithms. According to the Lyapunov stability theory, the uniform ultimate boundedness of the system state, state observation error, and neural network weight estimation error is demonstrated, thereby ensuring the stability of the closed-loop system and obtaining a suboptimal control policy. The simulation results demonstrate that the proposed control algorithm achieves accurate trolley positioning and minor payload swings despite incomplete system state measurement.

Key words: overhead crane, reinforcement learning, high gain observer, output feedback, PI