Nonlinear Controller Design and its Optimization for Real‐Time Implementation for a Servo Motor Drive

ABSTRACT

A proportional‐integral (PI) controller is still the workhorse in the industry due to its ease of commissioning and reliability. Therefore, this paper introduces an exponential PI (EXP‐PI) controller as a potential alternative. In the proposed scheme, two tuneable EXP functions acting nonlinearly on the error and the rate of change of the error are incorporated in cascade with the PI control architecture. This controller is implemented as a speed controller on a permanent magnet DC motor drive system. Five recent intelligent algorithms, namely stochastic fractal search (SFS), snake optimizer (SO), dragonfly search algorithm (DSA), symbiotic organisms search (SOS) and reptile search algorithm (RSA) are employed to identify the best performer for calibrating the controller gains. According to the statistical results, SFS is found to provide controller gains of higher quality, reducing the designed cost function value to 48.19. The superiority of SFS is verified by the nonparametric Wilcoxon rank‐sum test. Several experimental results with SFS‐calibrated EXP‐PI controller and other existing control schemes are presented using the DSP of TMS320F28335. The results show that our proposal performs better than its competing opponents in terms of various performance metrics, including integral‐based error criteria, stability margin, overshoot and settling time for plants with and without dead time.