Computationally efficient nonlinear predictive control based on RBF neural multi-models

Authors:
Maciej Ławryńczuk
Affiliations:
Institute of Control and Computation Engineering, Warsaw University of Technology, Warsaw, Poland
Venue:
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Year:
2009

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Abstract

This paper is concerned with RBF neural multi-models and a computationally efficient nonlinear Model Predictive Control (MPC) algorithm based on such models. The multi-model has an ability to calculate predictions over the whole prediction horizon without using previous predictions. Unlike the classical Nonlinear Auto Regressive with eXternal input (NARX) model, themulti-model is not used recursively in MPC, the prediction error is not propagated. The presented MPC algorithm needs solving on-line only a quadratic programming problem but in practice it gives closed-loop control performance similar to that obtained in nonlinear MPC, which hinges on on-line non-convex optimisation.