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, the multi-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.
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Ławryńczuk, M. (2009). Computationally Efficient Nonlinear Predictive Control Based on RBF Neural Multi-models. In: Kolehmainen, M., Toivanen, P., Beliczynski, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2009. Lecture Notes in Computer Science, vol 5495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04921-7_10
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DOI: https://doi.org/10.1007/978-3-642-04921-7_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04920-0
Online ISBN: 978-3-642-04921-7
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