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In this paper we propose a solution to the model predictive control problem for the case when the model is given by a nonlinear neural network. The solution follows the algorithm proposed by Peterson et al.11 where the linear DMC is extended to handle nonlinear systems by updating the linear model with a `disturbance due to nonlinearities' term. Simulation results of a reaction in a CSTR are included. Results show the improvement in control of the proposed algorithm over linear DMC.
Hernandaz et al. (Tue,) studied this question.
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