Nonlinear Self-Tuning of Fractional-Order PID Speed Controller for PMDC Motor

This paper presents a robust adaptation mechanism for the orders of a fractional-order proportional-integral derivative (PID) controller in order to optimally regulate the speed of a permanent magnet direct current motor. The fractional-number powers (order) of the integration and differentiation operator in the proposed controller are self-tuned in real-time using hyperbolic secant functions of error dynamics of motor's angular-speed. The proportional, integral and derivative gains of the fractional-order controller are meta-heuristically tuned via particle-swarm-optimization algorithm, and are kept fixed throughout the experimentation. The QNET DC Motor Control Trainer is used as a benchmark to experimentally compare the proposed control scheme with a fixed-gain generic PID controller as well as a fixed-gain and fixed-order fractional-order PID controller. The results of the 'hardware-in-the-loop' tests are presented to validate the robustness of the proposed speed controller.