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Elliptical Region Partition-Based Explicit Model Predictive Position Control for Planar Motors
ID：118 View Protection：ATTENDEE Updated Time：2025-10-13 11:26:46 Hits：230 Poster Presentation

Start Time：2025-11-09 09:08（Asia/Shanghai）

Duration：1min

Session：P Poster presentation » P77.Electric Machine Design and Control

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Abstract
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.
Keywords
Explicit model predictive control, elliptical region partition, planar motor, position control.
Speaker
Yuan Miao
Mr. Shenzhen University

Submission Author
Yuan Miao Shenzhen University
Guangzhong Cao Shenzhen University
Hong Qiu Shenzhen University
Sudan Huang Shenzhen University
Junqi Xu Tongji University
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Important Date

  • Conference Date

    Nov 07

    2025

    to

    Nov 09

    2025

  • Oct 30 2025

    Draft paper submission deadline

  • Nov 10 2025

    Registration deadline

Sponsored By
IEEE西南交通大学IAS学生分会
Organized By
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
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