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A Tutorial on Model Predictive Control: Using a Linear Velocity‐Form Model
Author(s) -
Wang Liuping
Publication year - 2004
Publication title -
developments in chemical engineering and mineral processing
Language(s) - English
Resource type - Journals
eISSN - 1932-2143
pISSN - 0969-1855
DOI - 10.1002/apj.5500120511
Subject(s) - model predictive control , pid controller , state space , control theory (sociology) , transfer function , state space representation , field (mathematics) , computer science , linear model , state (computer science) , control engineering , control (management) , matrix (chemical analysis) , algorithm , engineering , mathematics , artificial intelligence , machine learning , statistics , temperature control , electrical engineering , pure mathematics , materials science , composite material
Model Predictive Control (MPC) has a long history in the field of control engineering. It is one of the few areas that has received on‐going interest from researchers in both industry and universities. It has been recognised that there are three major branches of MPC algorithms consisting of step‐response model based design: Dynamic Matrix Control (DMC); transfer function model based design: Generdised Predictive Control (GPC); and a general state space model based design. The DMC and GPC algorithms can also be caste in the state space framework. Along the genera lines of state space methods, there are two mainstreunts: one solves for the optinzal control signal while the other solves for the increment of the optimal control signal. The latter can be implemented in a velocity form analogous to the implementation of a PID controller on an industrial plant. Motivated by this advantage. and that integral action is naturally embedded in the algorithm, this tutorial paper focuses on an introduction to Model Predictive Control based on the state space approach using a linear velocity‐form model.