Practical implementation of computationally-efficient machine learning-based control performance assessment system for a class of closed loop systems

Abstract

This paper presents the concept of the computationally-efficient reduced order control performance assessment (RO-CPA) system for automatic detection of industrial single closed loop control systems whose performance could be not acceptable due to a poor tuning of the controller. The proposed machine learning (ML) based classification system is derived for a broad class of industrial control closed loops based on the proportional–integral–derivative (PID) controller. Apart from a high accuracy of the classification of the closed loop performance, the proposed RO-CPA system is derived to meet the requirements of low computational complexity and low memory usage. These requirements allow for practical implementation of RO-CPA in devices with low computational and memory resources, such as industrial programmable logic controllers (PLC) operating in manufacturing systems or embedded autonomous systems with dedicated PID-based control loops. This paper shows the rigorous ML-based design of the proposed RO-CPA system and the results of the validation of its classification accuracy. Additionally, an example of a practical implementation within a PLC in the form of a general purpose library function block is presented and the final experimental validation is made using a part of laboratory heat exchange and distribution setup.