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Multi-objective performance optimisation for model predictive control by goal attainment

Research output: Contribution to Journal/MagazineJournal articlepeer-review

<mark>Journal publication date</mark>2010
<mark>Journal</mark>International Journal of Control
Issue number7
Number of pages13
Pages (from-to)1374-1386
Publication StatusPublished
<mark>Original language</mark>English


This paper proposes an approach for performance tuning of Model Predictive Control (MPC) using goal-attainment optimisation of the cost function weighting matrices. The approach is developed for three formulations of the control problem: (i) minimal and (ii) non-minimal design based on the same cost function; and (iii) a non-minimal MPC approach with an explicit integral-of-error state variable and modified cost function. This approach is based on earlier research into multi-objective optimisation for Proportional-Integral-Plus (PIP) control systems. Simulation experiments for a 3-input, 3-output Shell Heavy Oil Fractionator model illustrate the feasibility of MPC goal-attainment for multivariable decoupling and attainment of a specific output response. For this example, the integral-of-error state variable offers improved design flexibility and hence, when it is combined with the proposed tuning method, yields an improved closed loop response in comparison to minimal MPC.