Enhanced Parameter Identification of Fractional-Order PMSM in Robotic Joints Using Lévy Crayfish Optimization Algorithm

The accuracy and response speed of torque in robotic joint models are crucial for enhancing the compliance of robot interactions. Consequently, parameter identification of the permanent magnet synchronous motor (PMSM) model in robotic joints has garnered significant attention from researchers world-wide. To further improve model accuracy, this study focuses on the parameter identification of a fractional-order model of PMSM. This paper introduces the Lévy Crayfish Optimization Algorithm (LCOA), an enhancement of the crayfish optimization algorithm (COA), designed to overcome COA's tendency to become trapped in local optima. The proposed algorithm employs the Lévy flights (LF) strategy to optimize the foraging stage, improving global search capabilities. As a result, the new algorithm exhibits superior stability and computational accuracy. Simulations were conducted to discuss the accuracy of parameter identification for the fractional-order PMSM.