Auto-tuned Predictive Control based on Minimal Plant Information

Guillermo Valencia-Palomo and J. Anthony Rossiter
University of Sheffield


Abstract

This paper makes two key contributions. First there is a definition and implementation of a novel auto-tuned predictive controller. The key novelty is that the modelling is based on relatively crude but pragmatic plant information. Secondly, the paper tackles the issue of availability of predictive control for low level control loops. Hence the paper describes how the controller is embedded in an industrial Programmable Logic Controller (PLC) using the IEC 1131.1 programming standard. Laboratory experiment tests were carried out in two bench-scale laboratory systems to prove the effectiveness of the combined algorithm and hardware solution. For completeness, the results are compared with a commercial PID controller (also embedded in the PLC) using the most up to date auto-tuning rules.