Application of Machine Learning to Performance Assessment for a class of PID-based Control Systems

TL;DR

This paper introduces a machine learning-based control performance assessment system for PID control loops, capable of classifying performance quality in industrial processes with SOPDT characteristics, enabling practical, real-time evaluation.

Contribution

It presents a general, easy-to-configure CPA system using machine learning that automatically derives training data and assesses PID control performance without additional learning during operation.

Findings

Effective classification of control performance using machine learning.

Robust assessment applicable to real industrial systems.

Immediate practical application demonstrated on laboratory setup.

Abstract

In this paper, a novel machine learning derived control performance assessment (CPA) classification system is proposed. It is dedicated for a wide class of PID-based control industrial loops with processes exhibiting dynamical properties close to second order plus delay time (SOPDT). The proposed concept is very general and easy to configure to distinguish between acceptable and poor closed loop performance. This approach allows for determining the best (but also robust and practically achievable) closed loop performance based on very popular and intuitive closed loop quality factors. Training set can be automatically derived off-line using a number of different, diverse control performance indices (CPIs) used as discriminative features of the assessed control system. The proposed extended set of CPIs is discussed with comprehensive performance assessment of different machine learning based classification methods and practical application of the suggested solution. As a result, a general-purpose CPA system is derived that can be immediately applied in practice without any preliminary or additional learning stage during normal closed loop operation. It is verified by practical application to assess the control system for a laboratory heat exchange and distribution setup.