Efficient Nonlinear Model Predictive Control
نویسندگان
چکیده
The growing interest in model predictive control for nonlinear systems, also called NMPC, is motivated by the fact that today’s processes need to be operated under tighter performance specifications to guarantee profitable and environmentally safe production. One of the remaining essential problems for NMPC is the high on-line computational load. At each sampling instant, a nonlinear optimal control problem must be solved. In this paper, we summarize recent results showing the practical applicability of NMPC for process control. We show how recent advances in NMPC theory and dynamic optimization can be used to make the real-time application of NMPC feasible even for high dimensional problems. As an application example the real-time control of a high purity distillation column is considered.
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