Speed Controller Design for a Brushless DC Motor Drive System Integrating Coati Optimization Algorithm and Composite Sliding Mode Theory

This study proposes an intelligent speed-loop controller for a brushless DC motor (BLDCM) drive implemented under a field-oriented control (FOC) scheme. The controller is constructed by embedding the coati optimization algorithm (COA) into a composite sliding mode theory (CSMT) control structure. In sliding mode control (SMC), the use of only one reaching law normally produces a design trade off: increasing the reaching speed tends to aggravate overshoot or chattering, whereas reducing switching activity often slows convergence. To mitigate this compromise, the proposed controller adopts a composite reaching law (CRL) formed by an exponential component and a power component. When the system trajectory is distant from the sliding surface, the exponential component strengthens the reaching action and shortens the transient interval. When the trajectory moves close to the sliding surface, the power component decreases the effective switching intensity, thereby attenuating high-frequency chattering and reducing the overshoot associated with an aggressive exponential action. For adaptive gain selection, the COA search variables are chosen as four controller parameters: the sliding mode gain, the exponential reaching gain, the power reaching gain, and the power exponent. The fitness index is established from the rotor-speed tracking error and the time variation in that error. By imitating the hunting and predator-avoidance behaviors of coatis, the optimization process updates candidate solutions and selects the parameter combination that best matches the current operating condition. The resulting gains are supplied to the composite sliding mode controller (CSMC) so that the BLDCM can follow speed commands rapidly while preserving stable regulation. Because the proposed method performs online optimization of controller gains rather than data-driven training, it can be realized without a large training dataset. MATLAB/Simulink simulations are carried out to examine the effectiveness of the proposed strategy. The controller is compared with four benchmark methods, namely power reaching law (PRL)-based SMC, exponential reaching law (ERL)-based SMC, non-optimized composite reaching law SMC, and zebra optimization algorithm (ZOA)-assisted ERL-based SMC. The simulation results demonstrate that the proposed COA-based composite sliding mode controller improves both speed command-tracking and load-disturbance rejection relative to the comparative controllers.