Enhanced artificial satellite search algorithm with memory and evolutionary operator for PID controller parameter estimation

Abstract

The effective tuning of Proportional-Integral-Derivative (PID) controllers is crucial for industrial process control, but achieving optimal parameters for complex systems remains challenging. The recent Artificial Satellite Search Algorithm (ASSA) is strong in exploration but suffers from an imbalance between global and local search and a greedy selection strategy, leading to premature convergence. To overcome these limitations, this paper proposes an enhanced variant, MEASSA (Memory-based and Evolutionary-enhanced ASSA), which integrates a memory mechanism to preserve elite solutions, an evolutionary operator for guided population dynamics, and a stochastic local search for intensive refinement. Experimental evaluations on three dynamic systems are a DC motor, a three-tank liquid level system, and a fourth-order system which demonstrate MEASSA's superior performance. The algorithm achieved the lowest Integral Absolute Error (IAE) values of 9.977, 9.0781, and 9.697, respectively, outperforming several benchmark metaheuristics. Time-domain and frequency-domain analyses further confirm its ability to minimize overshoot, improve settling time, and enhance system stability, validating MEASSA as a robust and accurate method for complex PID controller tuning.