A New Approach for Designing of PID Controller for a Linear Brushless DC Motor with Using Ant Colony Search Algorithm

This paper presents a ant colony search algorithm (ACSA) method for determining the optimal proportional-integral derivative (PID) controller parameters, for speed control of a linear brushless DC motor. The proposed approach has superior features, including easy implementation, stable convergence characteristic and good computational efficiency. The brushless DC motor is modeled in Simulink and the ACSA is implemented in MATLAB. Comparing with genetic algorithm (GA) and linear quadratic regulator (LQR) method, the proposed method was more efficient in improving the step response characteristics such as, reducing the steady-states error; rise time, settling time and maximum overshoot in speed control of a linear brushless DC motor.