Optimal tracking control method, device and equipment based on adaptive dynamic programming
By using an adaptive dynamic programming method and optimizing the control input using the HJI equation and neural network, the problems of high resource consumption and low robustness in existing technologies are solved, and efficient tracking control under disturbance environments is achieved.
CN120406112APending Publication Date: 2025-08-01SOUTHERN UNIVERSITY OF SCIENCE AND TECHNOLOGY
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Patent Information
- Application Number
- CN202510328520.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-19
- Publication Date
- 2025-08-01
AI Technical Summary
Technical Problem
Existing technologies consume a lot of resources and have low applicability and robustness of control inputs when facing some unknown controlled systems and external disturbances.
Method used
An adaptive dynamic programming method is adopted to obtain control and disturbance input data through the HJI equation. The data is updated using the execution network and the disturbance network until the optimal value function is obtained. Finally, the control input is optimized by combining the evaluation network.
Benefits of technology
It improves resource utilization efficiency, enhances the robustness of control inputs, and enables the controlled system to accurately track the reference trajectory in the presence of disturbances.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention relates to the technical field of optimal tracking control, and provides an optimal tracking control method, device and equipment based on adaptive dynamic programming. Constructing an augmented system according to a tracking error and a reference trajectory of the system (the system is constrained by part of unknown disturbance), and designing a corresponding value function; based on the designed value function, the H infinity tracking problem is converted into a zero sum game, and a corresponding Hamilton-Jacobi-Isaacs (Hamilton-Jacobi-Isaacs, HJI) equation is constructed; and the evaluation-execution-disturbance network approximates the HJI equation, the optimal control strategy and the worst disturbance strategy are solved, and the optimal control strategy is applied to the controller. According to the method, the control input and the disturbance input during neural network training are updated by using a double-event triggering mechanism, and the resource utilization efficiency is improved. According to the optimal control input data, external disturbance factors suffered by the controlled system are considered, so that the robust performance of the obtained optimal control input data is improved.
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