Research on Output Feedback Control Based on Reinforcement Learning for Overhead Crane

Authors

• Minghui Li, School of Technology, Beijing Forestry University, Beijing 100083, China
• Daoxiang Gao, School of Technology, Beijing Forestry University, Beijing 100083, China

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.

Recommended Citation

Li, Minghui and Gao, Daoxiang (2026) "Research on Output Feedback Control Based on Reinforcement Learning for Overhead Crane," Journal of System Simulation: Vol. 38: Iss. 6, Article 22.
DOI: 10.16182/j.issn1004731x.joss.25-0625
Available at: https://dc-china-simulation.researchcommons.org/journal/vol38/iss6/22

First Page

1771

Last Page

1781

CLC

TP391.9

Recommended Citation

Li Minghui, Gao Daoxiang. Research on Output Feedback Control Based on Reinforcement Learning for Overhead Crane[J]. Journal of System Simulation, 2026, 38(6): 1771-1781.

Corresponding Author

Gao Daoxiang

DOI

10.16182/j.issn1004731x.joss.25-0625