Optimization of a backstepping controller for a nonlinear permanent magnet synchronous motor model using marine predator algorithm

This study addresses the problem of tuning a backstepping controller for the speed control of a Permanent Magnet Synchronous Motor (PMSM) that includes nonlinear and multivariable characteristics with coupling between electrical and mechanical variables. The following metaheuristic algorithms were evaluated and compared: the slime mould algorithm (SMA), marine predators algorithm (MPA), LSHADE, hippopotamus optimization algorithm (HO), golden search optimization algorithm (GSO), and coati optimization algorithm (COA). Among these, MPA showed the best performance in minimizing the mean squared error while optimizing the PI controller. The performance of the optimized backstepping controller for the PMSM was improved using MPA. The algorithm adjusted the controller parameters to achieve precise speed trajectory tracking of the rotor under different operating conditions, including parametric variations and external disturbances. The results indicated that the optimized backstepping controller using the MPA provides a robust and efficient response, significantly improving the performance compared to the optimized PI controller and the adaptive backstepping controller, with an integral of time-weighted absolute error of 662.949006. • Parametric-robust optimized backstepping controller. • Comparison of six metaheuristics for tuning a PMSM backstepping speed controller • Comparison of proposed method vs PI, NNBCS, and SMC control approaches • Optimized nonlinear backstepping controller tested in MATLAB/Simulink under varied conditions.