Nonlinear Model Predictive Control (NMPC) has become a serious option for industrial applications, with advances in software and algorithms making economics optimizing control suitable even for large scale systems. However, the industrial applications are posing a number of engineering challenges, related to the performance of NMPC in real plant environments. In this paper we address the issue of validating NMPC solutions in a real-time simulation environment, which mimics precisely the behavior of the controlled plant.We propose a validation strategy and describe the simulation framework that was developed in order to support the validation process. The software platform used for this purpose is based on the do-mpc framework, benefiting from its modularity and efficient implementation of NMPC algorithms. The focus of this work is the extension of the software environment to a real application oriented platform where control scenarios can be simulated in real time. The necessity of the proposed validation strategy is demonstrated for a semi-batch polymerization example, where it is shown that feedback delays have a significant impact on the real-time performance. The necessary steps for a transition from simulation studies to the real application are discussed and a method for improving the NMPC performance is proposed based on the real-time simulation studies.
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