Optimization of PID Parameters Based on Ant Colony Algorithm
Xiaoli Zhang, Qian Zhang
Abstract
In order to solve the problem that traditional PID controllers cannot easily obtain accurate models and good performance, a PID parameter optimization method based on ant colony optimization algorithm is proposed. This method analyzes the characteristics of PID parameters, combines the positive feedback mechanism of the ant colony algorithm to dynamically adjust the update strategy, optimizes the PID parameters to select the best parameters, so that the system has better performance. On the basis of the above, through use the ant colony algorithm (ACO), genetic algorithm (GA), particle swarm algorithm (PSO) to establish the mathematical model of the PID controller, and uses the algorithm to set the controller parameters. The results of the algorithm research show that the ACO has a faster convergence speed and a higher rate of convergence. It can be seen from the experiment that the optimal value of ACO is significantly better than the other two algorithms. Excellent global search capability and better robustness provide good application effects for mechanical control.