Abstract

This paper investigates the effect of model mismatch on the performance of model predictive control (MPC) when applied to the heating system. The controller uses a linear model and a quadratic cost function, while the actual process is non-linear in nature with a linear cost function. A genetic algorithm (NSGA II) is used to find the optimal solution to the actual problem and a number of variations, which are then compared the performance of the MPC controller. The results show that the model mismatch has a small but significant effect on the control performance, and it does prevent effective load shifting in certain situations.

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