Multi-degree-of-freedom mechanical arm trajectory tracking control method and system based on decomposition type Q learning

By using the multi-game Stackelberg control method for multi-degree-of-freedom robotic arms through Q-decomposition learning, the problems of dependence on precise models and high computational complexity are solved, achieving model-free control, improving the trajectory tracking accuracy and real-time performance of the robotic arm, and making it suitable for complex industrial environments.

CN121870772APending Publication Date: 2026-04-17CHINA JILIANG UNIV
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Patent Information

Application Number
CN202610296819.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-12
Publication Date
2026-04-17

AI Technical Summary

Technical Problem

Existing Stackelberg game-based optimal control methods for nonlinear systems heavily rely on accurate system dynamics models, making it difficult to achieve high-precision trajectory tracking when the model is unknown or has significant uncertainties. Furthermore, their high computational complexity makes them unsuitable for real-time control requirements.

Method used

A multi-degree-of-freedom robotic arm employs a multi-game Stackelberg control method based on decomposed Q-learning. By decomposing the Q-function into state utility terms, linear coupling terms, and quadratic cost terms, an Actor-Critic neural network architecture is constructed to achieve hierarchical game-based optimal control without prior system information.

Benefits of technology

It completely eliminates the reliance on precise dynamic models of the system, reduces computational complexity, improves real-time convergence speed, and possesses excellent transient response capability and anti-interference robustness, ensuring system stability and smooth motion.

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Abstract

The invention discloses a multi-degree-of-freedom mechanical arm trajectory tracking control method and system based on decomposition type Q learning. According to the method, a drifting dynamic matrix and an input dynamic matrix of the mechanical arm system do not need to be known in advance, online learning and optimization can be carried out by directly utilizing input and output data through the Q learning algorithm, and the dependence of a traditional adaptive dynamic programming (ADP) method on an accurate mathematical model is effectively overcome. According to the method, a Q function is innovatively decomposed into three independent components, and three sub neural network channels are used for approximation respectively. Compared with a direct approximation integral Q function, the distributed design has the advantages that the complexity of a single network is reduced, the fitting capability of the neural network to a nonlinear system is fully utilized, and the convergence speed and learning efficiency of the algorithm are improved. In conclusion, high-precision trajectory tracking can be achieved, the error curve convergence is rapid and stable, the speed and position change of each joint is smooth, no obvious jitter exists, and the method has good engineering application value.
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