Optimizing PID parameters with machine learning

TL;DR

This paper explores using evolutionary programming, a derivative-free optimization method, to effectively tune PID controller parameters, demonstrating its ability to avoid local minima and improve control system performance.

Contribution

It introduces the application of evolutionary programming for PID parameter optimization, highlighting its effectiveness and robustness compared to traditional methods.

Findings

EP successfully optimizes PID parameters without local minima entrapment

The method improves control stability and performance

EP is suitable for diverse PID tuning applications

Abstract

This paper examines the Evolutionary programming (EP) method for optimizing PID parameters. PID is the most common type of regulator within control theory, partly because it's relatively simple and yields stable results for most applications. The p, i and d parameters vary for each application; therefore, choosing the right parameters is crucial for obtaining good results but also somewhat difficult. EP is a derivative-free optimization algorithm which makes it suitable for PID optimization. The experiments in this paper demonstrate the power of EP to solve the problem of optimizing PID parameters without getting stuck in local minimums.