Control parameters optimization for servo feed system using an improved genetic algorithm

To improve the motion accuracy of servo feed system and aim at solving the problem that conventional methods for optimization of the servo control parameters mainly depend on manual tuning and cannot achieve optimal configuration of servo control parameters. A novel improved genetic algorithm is proposed to optimize the servo control parameters. An adaptive crossover, mutation strategy and elitism strategy were proposed to overcome the prematurity of population and improve convergence speed. The servo control parameters were updated online and the fitness function was adopted to evaluate the performances. The optimal value of servo control parameters were worked out by the improved genetic algorithm. The servo control parameters optimization experiment was implemented on a servo feed system. The experimental results show that the absolute maximum of tracking error, absolute mean value and range of tracking error were reduced greatly. The algorithm can improve the motion accuracy effectively before and after optimization. The effectiveness of the algorithm is verified.