一种无人系统姿态控制PID参数整定动态多目标优化方法
By constructing a dynamic model and multidimensional variable optimization space for unmanned systems, and combining it with a dynamic multi-objective optimization algorithm based on kernel mean matching, the problems of overshoot, settling time, and control error in the dynamic attitude control of unmanned systems are solved. This achieves efficient PID parameter tuning and meets the optimization requirements in dynamic environments.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- XIAMEN UNIV
- Filing Date
- 2023-07-13
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies in the dynamic attitude control of unmanned systems cannot simultaneously meet the requirements of small maximum overshoot, short settling time, and small absolute control error, and the control process is lengthy and ineffective in dynamic environments.
An optimization space is constructed based on the dynamic model of the unmanned system, and a multi-dimensional variable optimization objective function is built. A dynamic multi-objective optimization algorithm based on kernel mean matching is used, and PID parameters are tuned through matrix description method. Combined with environmental detection and scoring selection mechanism, highly adaptable individuals are selected for transfer learning to optimize and solve the PID control parameters.
It accelerates the optimization speed of multi-objective optimization algorithms in dynamic environments, improves the efficiency of attitude control of unmanned systems, meets the optimization objectives under different time scales, and achieves control effects with small overshoot, short settling time and small control error.
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Figure CN116774573B_ABST