Opposition based Henry gas solubility optimization as a novel algorithm for PID control of DC motor

Abstract

This paper proposes Henry gas solubility optimization with opposition-based learning (OBL/HGO) as a novel optimization approach for DC motor speed regulation. The proposed approach was used to obtain the best parameters (proportional, integral, derivative) of PID controller by minimizing the integral of time multiplied absolute error (ITAE) as the objective function. The optimized controller was used to regulate the speed of a DC motor. The analysis of statistical tests, convergence profile, performance index, robustness, and disturbance rejection along with transient and frequency responses were all conducted in order to validate the effectiveness of the proposed approach. Also, the performance of the proposed OBL/HGSO tuned PID (OBL/HGSO-PID) controller was not only compared with the PID controller tuned by the original HGSO algorithm but also with other controllers that were tuned by the state-of-the art meta-heuristic algorithms such as atom search optimization (ASO), stochastic fractal search (SFS), grey wolf optimization (GWO) and sine–cosine algorithm (SCA). The conducted simulation results and comparisons with the proposed HGSO-PID controller and other existing controllers have showed that the proposed OBL/HGO-PID controller has superior control performance and excellent robustness even under the conditions of both system uncertainties and load disturbances.