Abstract
This paper describes a computationally efficient nonlinear Model Predictive Control (MPC) algorithm in which a state-space neural model of the process is used on-line. The model consists of two Multi Layer Perceptron (MLP) neural networks. It is successively linearised on-line around the current operating point, as a result the future control policy is calculated by means of a quadratic programming problem. The algorithm gives control performance similar to that obtained in nonlinear MPC, which hinges on non-convex optimisation.
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Ławryńczuk, M. (2010). Computationally Efficient Nonlinear Predictive Control Based on State-Space Neural Models. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Wasniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2009. Lecture Notes in Computer Science, vol 6067. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14390-8_36
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DOI: https://doi.org/10.1007/978-3-642-14390-8_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14389-2
Online ISBN: 978-3-642-14390-8
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