Optimal tuning of linear quadratic regulator controller using ant colony optimization algorithm for position control of a permanent magnet DC motor

Abstract EN

This paper presents the design of an optimal Linear Quadratic Regulator (LQR) controller using Ant Colony Optimization (ACO) and particle swarm optimization (PSO) methods for position control of a permanent magnet DC (PMDC) motor.

In this work, Ant Colony control and particle swarm control algorithms have been utilized to set the optimal elements of the weighting matrices subjected to a proposed cost function.

The proposed cost function is a combination of the quadratic performance index and integral square error.

The proposed design can overcome the difficulty in setting the weighting matrices with the suitable elements.

The simulation results using (Matlab Package) show that the optimal LQR controller using ACO algorithm can give excellent performance in terms of obtaining smooth and unsaturated state voltage control action that will stabilize the DC motor system performance and minimize the position tracking error of the system output.

In addition, the rising time and settling time is decreased in comparison with the LQR based PSO controller performance.