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

A robust speed controller integrating the slime mold algorithm (SMA) with sliding mode theory (SMT) is proposed for induction motor (IM) drives operating under field-oriented control (FOC). Unlike conventional controllers with fixed gain parameters, the proposed exponential reaching law sliding mode controller (ERLSMC) defines the sliding mode dynamic trajectory control gain, exponential reaching gain, and constant-speed reaching gain as the search space for the SMA. An adaptive fitness function based on the speed error and its rate of change is constructed to continuously evaluate and update these gain parameters, thereby determining the optimal controller gains according to the current operating state. Consequently, larger gain values are assigned when the system state is far from the sliding mode dynamic trajectory to accelerate the reaching process, whereas smaller gain values are adopted near the sliding mode dynamic trajectory to suppress chattering and reduce overshoot. Matlab/Simulink (2024b version) simulations are conducted to evaluate the proposed controller in an IM drive system and compare its performance with constant-speed reaching law sliding mode control, exponential reaching law sliding mode control, and zebra optimization algorithm (ZOA)-based ERLSMC methods. The simulation results demonstrate that the proposed controller achieves superior performance in both speed command tracking and load regulation response.