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

Research output: Contribution to journalJournal article


Journal publication date2010
JournalInternational Journal of Control
Number of pages13
Original languageEnglish


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.