Multi-objective optimization of speed-sensorless sliding mode vector control for electric motors based on HSMO and MOGA

Purpose Speed-sensorless induction-motor drives are limited by low-speed flux-estimation errors, parameter mismatch, and sliding-mode chattering. This study proposes a hybrid sliding-mode observer-based optimized control (HSMO-OC) framework to improve speed estimation, dynamic response, and electromagnetic-output smoothness. Methods A hybrid sliding-mode observer (HSMO) combines voltage-model and current-model flux estimates using speed-dependent weighting and continuous sliding-mode correction. A multi-objective genetic algorithm (MOGA) jointly optimizes observer and sliding-mode speed-controller parameters under stability and engineering constraints. MATLAB/Simulink simulations of a 2.2 kW induction-motor drive compare four control configurations under startup, speed transitions, load disturbances, low-speed operation, and three uncertainty conditions. Results Compared with the conventional SMO-SMC baseline, HSMO-OC reduced speed root-mean-square error from 28.9 to 9.6 r/min, settling time from 0.204 to 0.109 s, and load-induced speed drop from 64.8 to 21.5 r/min. Torque-ripple and current-ripple RMS values decreased from 0.96 to 0.41 N·m and from 1.07 to 0.48 A, respectively. At 100 r/min, speed RMSE was 2.8 r/min. Under resistance mismatch, measurement noise, and inverter dead time, speed RMSE values were 13.4, 12.7, and 14.2 r/min, respectively. Conclusion The results demonstrate the complementary contributions of hybrid flux observation and joint multi-objective parameter optimization to estimation accuracy, disturbance rejection, and electromagnetic-output smoothness under the tested simulation conditions.