Modelling and Analysis of Linear Brushless DC Motor using PSO-PID Controller

Abstract

This Paper presents a particle swarm optimization
(PSO) 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 PSO algorithm 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. In the analysis and design of Control Systems, it is often necessary to simplify A high order system. The use of a reduced order model makes it easier to implement analysis, simulations and control system design. To establish a a transfer function of lower order numerous methods have
been proposed. Inspite of the significant number of methods
available, no approach always gives the best result for all systems. Almost all methods, however, aim at accurate reduced models for a low computational cost. Recently, Particle Swarm optimization technique appeared as a promising algorithm for handling the
optimization problems. PSO is a population based stochastic
optimization technique shares many similarities with Genetic
Algorithm. One of the promising advantage of PSO over GA is its algorithmic simplicity, as it uses a few parameters and easy to implement.