Premium
Assessing the Performance of Model Predictive Controllers
Author(s) -
Patwardhan Rohit S.,
Shah Sirish L.,
Qi Kent Z.
Publication year - 2002
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.5450800519
Subject(s) - model predictive control , metric (unit) , computer science , statistic , control theory (sociology) , function (biology) , measure (data warehouse) , value (mathematics) , relevance (law) , performance metric , mathematical optimization , control (management) , mathematics , engineering , artificial intelligence , machine learning , data mining , statistics , operations management , management , evolutionary biology , law , political science , economics , biology
Abstract Performance assessment of model predictive controllers is a problem of significant industrial relevance. Model predictive controllers belong to a class of linear time‐varying controllers, which compute the future control actions by minimizing a constrained, time‐varying objective function. In this work we propose a performance statistic that takes into account the time‐varying and constrained nature of model predictive control. The proposed measure compares the achieved objective function with its design value, online. Analytical expressions are derived to calculate the expected value of the design objective function under closed loop conditions. Simulation and industrial case studies are used to illustrate the applicability of the proposed metric.