IPSO algorithm-based spherical motor driving strategy via full factorial design method

Abstract

Spherical motors represent advanced multi-degree-of-freedom motion devices where precise current calculation is critical for generating target torque outputs. Achieving close-loop control in permanent magnet spherical motors (PM-SpMs) with non-circular symmetric magnetic configuration presents significant technical challenges. This paper proposes an improved particle swarm optimization (IPSO) algorithm enhanced by the full factorial design (FFD) method and applies it to the driving strategy of a PMSpM. First, we implement a bilinear interpolation technique for real-time torque estimation using pre-characterized torque maps, significantly improving computational accuracy and response speed. Following population size determination through conventional trial-and-error analysis, we enhance the classical PSO framework by incorporating adaptive inertia weights and dual adaptive learning factors. Subsequently, the FFD approach systematically optimizes critical IPSO parameters. Comprehensive validation through MATLAB simulations and Minitab statistical analysis demonstrates the efficacy of our FFD- enhanced IPSO algorithm. The experimental results prove the effectiveness and practicability of the proposed driving strategy for the PMSpM.