Suboptimal nonlinear predictive control with structured neural models

Authors:
Maciej Ławrynczuk
Affiliations:
Institute of Control and Computation Engineering, Warsaw University of Technology, Warsaw, Poland
Venue:
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Year:
2007

Citing 3
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Abstract

This paper details a computationally efficient (suboptimal) nonlinear Model Predictive Control (MPC) algorithm with structured neural models and discusses its application to a polymerisation reactor. Thanks to the nature of the model it is not used recursively, the prediction error is not propagated. The model is used on-line to determine a local linearisation and a nonlinear free trajectory. The algorithm needs solving on-line only a quadratic programming problem. It gives closedloop control performance similar to that obtained in the fully-fledged nonlinear MPC, which hinges on non-convex optimisation.