报告详情
Elliptical Region Partition-Based Explicit Model Predictive Position Control for Planar Motors
编号：118
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更新：2025-10-13 11:26:46 浏览：5次
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摘要
An explicit model predictive control (EMPC) method using error state-based elliptical region partition strategy is proposed for high-performance positioning of planar motors under physical constraints. By partitioning the error state space into several elliptical regions, a piecewise affine explicit control law of the planar motor is formulated. Compared to conventional EMPC method, the elliptical region partition effectively reduces the number of partitions while preserving constraint satisfaction, lowers memory storage requirements, and enhances real-time performance. Two elliptical region partitioning strategies are developed to evaluate their influence on the control performance of the EMPC. Simulation results demonstrate that an appropriate elliptical region partitioning strategy can significantly improve positioning tracking accuracy of the planar motor, maintaining the steady-state position error within the micrometer level; the proposed method provides an efficient and feasible solution for high-performance position control of planar motors under physical constraints.
关键词
Explicit model predictive control, elliptical region partition, planar motor, position control.
报告人
Yuan Miao
Mr. Shenzhen University稿件作者
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重要日期
-
会议日期
11月07日
2025
至11月09日
2025
-
10月12日 2025
初稿截稿日期
-
10月30日 2025
注册截止日期
主办单位
IEEE西南交通大学IAS学生分会
承办单位
西南交通大学电气工程学院
SPACI车网关系研究室
四川大学电力系统稳定与高压直流输电研究团队
SPACI车网关系研究室
四川大学电力系统稳定与高压直流输电研究团队
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