Abstract

A nonlinear model predictive control algorithm is proposed for control of constrained nonlinear systems. The basic idea is to calculate exactly the first control move, which is implemented, and approximate all other control moves, which are not implemented. Regardless of the control horizon, the number of decision variables for the online optimisation problem equals the number of manipulated variables, resulting in significant savings in online computational time. The feasibility for a practical implementation of the proposed algorithm is demonstrated on two examples including the Tennessee-Eastman Challenge process involving seven inputs and three outputs.

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