Robust PID parameters optimization design based on improved Particle Swarm Optimization

The article, based on satisfying robustness of the system and put forward the objective function of time-domain performance and dynamic characteristics, introduced genetic operators into Particle Swarm Optimization. The algorithm improve the diversity of particles by selection and hybridization operations and strengthen the excellent characteristics of particles in the swarm by introducing crossover and mutation genes, which can avoid bog down into local optima and premature convergence and enhance searching efficiency. The simulation results indicate that when the algorithm is applied to the optimization of PD controller parameters of servo system of grinding wheel rack of MKS8332A CNC camshaft grinder, its performance is better than the single Genetic Algorithms or Particle Swarm Optimization, and it can also satisfy the demand of rapidity, stability and robustness.