Multi-objective optimization configuration of redundant electromagnetic actuators in fault-tolerant control of active magnetic bearing system

Shuai Deng; Xin Cheng; Huachun Wu; Yefa Hu

doi:10.1016/j.isatra.2023.06.015

Multi-objective optimization configuration of redundant electromagnetic actuators in fault-tolerant control of active magnetic bearing system

ISA Trans. 2023 Sep:140:293-308. doi: 10.1016/j.isatra.2023.06.015. Epub 2023 Jun 20.

Authors

Shuai Deng¹, Xin Cheng², Huachun Wu³, Yefa Hu³

Affiliations

¹ School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, China.
² School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, China; Hubei Provincial Engineering Technology Research Center for Magnetic Suspension, Wuhan, China. Electronic address: chengx@whut.edu.cn.
³ School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, China; Hubei Provincial Engineering Technology Research Center for Magnetic Suspension, Wuhan, China.

PMID: 37414593
DOI: 10.1016/j.isatra.2023.06.015

Abstract

Fault-tolerant control of active magnetic bearing (AMB) systems with redundant electromagnetic actuators (EMAs) based on generalized bias current linearization has become a practical technique to address EMA/amplifier faults. In this method, the configuration of multi-channel EMAs involves solving a high-dimensional and nonlinear problem containing complex constraints offline. This article develops a general framework for the EMAs multi-objective optimization configuration (MOOC) by combining the non-dominated sorting genetic algorithm III (NSGA-III) and the sequential quadratic programming (SQP) with the designing of objectives, handling of constraints, consideration of the iterative efficiency and the diversity of solutions. The numerical simulation results confirm the feasibility of the framework for searching the non-inferior configurations and reveal the function mechanism that intermediate variables of the nonlinear optimization model on AMB performance. Finally, the best configurations identified using the technique for order preference by similarity to an ideal solution (TOPSIS) are applied to the 4-DOF AMB experimental platform. Experiments further indicate that the work in this paper provides a novel way with good performance and high reliability for solving the EMAs MOOC problem in fault-tolerant control of AMB systems.

Keywords: Active magnetic bearing (AMB); Electromagnetic actuators (EMAs); Multi-objective optimization; Non-dominated sorting genetic algorithm.