Self-Tuning Control of Manipulator Positioning Based on Fuzzy PID and PSO Algorithm - Liu, Ying; Jiang, Du; Yun, Juntong; Sun, Ying; Li, Cuiqiao; Jiang, Guozhang; Kong, Jianyi; Tao, Bo; Fang, Zifan (Frontiers in Bioengineering and Biotechnology, 2022)
With the manipulator performs fixed-point tasks, it becomes adversely affected by external disturbances, parameter variations and random noise. Therefore, it is essential to improve the robust and accuracy of the controller. In this paper, a self-tuning particle swarm optimization (PSO) fuzzy PID positioning controller is designed based on fuzzy PID control. The quantization and scaling factors in the fuzzy PID algorithm are optimized by PSO in order to achieve high robustness and high accuracy of the manipulator. First of all, a mathematical model of the manipulator is developed and the manipulator positioning controller is designed. A PD control strategy with compensation for gravity is used for the positioning control system. Then, combination with the fuzzy control rules, the PID controller parameters are dynamically fine-tuned by fuzzy controller 1 and the magnitude of the quantization factor-proportion factor are adjusted online by modified fuzzy controller 2. A quantization factor-proportion factor online self-tuning strategy is achieved to find the optimal parameters for the controller. Finally, the control performance of the improved controller is verified by simulation experiments. The results show that the transient response speed, tracking accuracy and follower characteristics of the system are significantly improved.