Speed Control of Induction Motor Drives Based on Combining Slime Mould Optimization Algorithm and Sliding Mode Theory

This study addresses the speed control problem of an induction motor (IM) under the field-oriented control (FOC) architecture by proposing a robust controller design that combines the slime mould algorithm (SMA) with sliding mode theory (SMT). Distinct from traditional controller designs with fixed gains, the proposed theory defines the ranges of three gain parameters of the exponential reaching law sliding mode controller—namely, the sliding mode dynamic trajectory control gain, the exponential reaching gain, and the constant speed reaching gain—as the search space for the SMA. An adaptive fitness function is constructed using speed error and the rate of change of speed error to continuously evaluate and update these three gain parameters, thereby determining the optimal gains for the current state. This method allows the system to increase gain values to accelerate reaching when far from the sliding surface, and reduce gains to suppress chattering and minimize overshoot when approaching the sliding surface. Finally, Matlab/Simulink simulation software is used to simulate the proposed robust controller applied to an IM drive system. Its performance is compared with three other controllers: constant speed reaching law, exponential reaching law, and zebra optimization algorithm (ZOA) combined with exponential reaching law. Simulation results confirm that the proposed novel controller demonstrates control performance superior to the other three controllers in both speed command tracking and load regulation response.