Achieving Robust and Optimal Speed Control of DC Motor through Sliding Mode Control Tuned by Genetic and Particle Swarm Optimization Algorithms

This paper presents a robust Sliding Mode Controller (SMC) for the speed control of the Direct Current (DC) motor, addressing the limitations of traditional controllers under parameter variations and disturbances. The performance is compared with a Proportional Integral Derivative (PID) controller, and both controllers' parameters are optimized using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) individually, where Integral Time Absolute Error (ITAE) is used as a cost function. A performance comparison between these two algorithms is also presented for the proposed controller. The robustness of the SMC is investigated under six different cases of parameter variation. To validate the robustness of SMC, the control signal variation for both SMC and PID is also presented in this paper. The frequency response curve and the major margin are given for different controllers to investigate stability. A random disturbance is applied to the DC motor to investigate further robustness. The chattering effect is reduced by implementing a pseudo function. MATLAB/Simulink simulations and experimental results confirm SMC's superiority over PID.