Nonlinear PID Controller Parameters Optimization Using Improved Particle Swarm Optimization Algorithm for the CNC System

In this paper, a nonlinear PID (NLPID) controller is used to replace traditional overcome the influence of factors in computer numerical control (CNC) system. A particle swarm optimization based on generalized opposition-based learning (G-PSO) algorithm proposed optimize NLPID controller. The convergence speed and global ability (PSO) are improved by introducing learning. natural selection mutation introduced into G-PSO further avoid particles falling local optimization. Different from existing research, paper designs special fitness function according objectives improving system response suppressing overshoot. By comparing differential evolution (DE) algorithm, ant lion optimizer (ALO) genetic (GA) through simulation, it proven that has faster better ability. Compared Fuzzy MRAC PID, shown be more suitable for CNC systems. Additionally, experiments rise time settling optimized 22.22% 24.52% faster, respectively, than controller, overshoot successfully suppressed.