Controller Optimization of an Automated Force Calibration Device Based on an Improved Particle Swarm Algorithm

Abstract

This paper presents a PID controller optimization method based on an Improved Particle Swarm Optimization (IPSO) algorithm, aimed at enhancing the design and performance of automated force calibration devices. Traditional force calibration systems often suffer from limitations in automation and application accuracy, which this method seeks to address. The IPSO algorithm incorporates logistic chaotic mapping to initialize particle positions and enhances the standard particle swarm algorithm through nonlinear decreasing weights and adaptive learning factors, resulting in improved global search capabilities and faster local convergence. Additionally, the objective function is refined by normalizing and weighting the integral of time-multiplied absolute error (ITAE) and overshoot, thereby further enhancing the accuracy of the PID controller. Simulation results demonstrate that IPSO offers advantages in settling time, overshoot, and ITAE error when compared to traditional PID tuning methods (e.g., Ziegler-Nichols) and other optimization algorithms (e.g., GA, GWO). Experimental results validate the effectiveness and practicality of the IPSO algorithm, providing an efficient solution for high-precision automation control of force sensor calibration systems.

Qiang Li

Professor and header of AG

My research interests include underwater robot, collaborative robots, humanoid robots.