A Computationally Efficient Nonlinear Predictive Control Algorithm with RBF Neural Models and Its Application

Authors:
Maciej Ławryńczuk;Piotr Tatjewski
Affiliations:
Institute of Control and Computation Engineering, Warsaw University of Technology ul. Nowowiejska 15/19, 00-665 Warsaw, Poland;Institute of Control and Computation Engineering, Warsaw University of Technology ul. Nowowiejska 15/19, 00-665 Warsaw, Poland
Venue:
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Year:
2007

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Abstract

This paper details a computationally efficient (suboptimal) nonlinear Model Predictive Control (MPC) algorithm with Radial Basis Function (RBF) type neural network models and discusses its application to a polymerisation reactor. Neural model of the process is used on-line to determine the local linearisation and the nonlinear free trajectory. Unlike the nonlinear MPC technique, which hinges on non-convex optimisation, the presented algorithm is more reliable and less computationally demanding because it results in a quadratic programming problem, whereas its closed-loop control performance is similar.