Automatic optimization of PID control software parameters based on machine learning

In order to understand the method of automatic parameter optimization for PID control software, research on automatic parameter optimization for PID control software based on machine learning has been proposed. PID parameter tuning and optimization have always been an important issue in the control field, and appropriate PID parameters can enable the system to have good control effects. Since Ziegler and Nichols proposed the PID parameter tuning method, many algorithms have been used for PID parameter tuning optimization. Machine learning algorithms have asymptotic convergence and are a global optimization algorithm that converges to the global optimal solution with probability I. This article focuses on machine learning algorithms and uses MATLAB to optimize the design of PID parameters. This method can make the entire optimization process simple, accurate, and improve computational efficiency.