Work

Parameter Self-Tuning of Servo Control Systems Based on Nonlinear Adaptive Whale Optimization Algorithm

Year: 2026

Type: article

Abstract: Parameter self-tuning of servo control systems is crucial for optimizing automation processes, especially in complex systems such as permanent magnet synchronous motors. In this paper, a nonlinear adaptive whale optimization algorithm (NAWOA) is proposed and applied to parameter self-tuning, which improves the traditional whale optimization algorithm (WOA) by nonlinearly adaptively adjusting two parameters during optimization to enhance fast convergence and global search capabilities. A servo control system with three parameters to be tuned is constructed using both simulation and physical methods. Simulation and experimental results show that the NAWOA outperforms the genetic algorithm, particle swarm optimization, and WOA in parameter self-tuning of the servo control system with lower error indicators and fast convergence speed. Although it still faces the challenge of initial condition dependency, the proposed NAWOA provides a powerful solution for real-time industrial applications.

Source: Machines

DOI: 10.3390/machines14020242

Authors: Huarong Gu, Xinyuan Wang, Xinyu Hu

Institution: Nanjing University of Aeronautics and Astronautics

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896