Elliptical Region Partition-Based Explicit Model Predictive Position Control for Planar Motors
ID：118
Submission ID：123 View Protection：ATTENDEE
Updated Time：2025-10-13 11:26:46 Hits：58
Poster Presentation
Start Time：2025-11-09 09:08 （Asia/Shanghai）
Duration：1min
Session：[P] Poster presentation » [P7] 7.Electric Machine Design and Control
No files
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
Comment submit