Enhanced Output Tracking Control for Direct Current Electric Motor Systems Using Bio-Inspired Optimization

Abstract

In this paper, an efficient output reference trajectory tracking control scheme for direct current electric motor systems based on bio-inspired optimization is proposed. The differential flatness structural property of the electric motor along with dynamic tracking error compensation is suitably exploited for the backstepping control design. Off-line optimal selection of control parameters, implementing bio-inspired ant colony and particle swarm optimization algorithms, is addressed by minimizing an objective function where the decision variables are the tracking error and control input effort. A novel adaptive version of the control approach based on B-spline artificial neural networks is provided as well. The introduced flat output feedback tracking control design approach can be further extended for other differentially flat dynamic systems. Considerably perturbed, diverse velocity and position reference trajectory tracking scenarios are developed for demonstrating the acceptable closed-loop system performance. The results prove the efficient and robust tracking of the position and velocity reference profiles planned for the operation of the controlled electric motor system under variable torque disturbances using bio-inspired optimization.