Abstract
This paper describes a neural approach to economic set-point optimisation which cooperates with Model Predictive Control (MPC) algorithms. Because of high computational complexity, nonlinear economic optimisation cannot be repeated frequently on-line. Alternatively, an additional steady-state target optimisation based on a linear or a linearised model and repeated as often as MPC is usually used. Unfortunately, in some cases such an approach results in constraint violation and numerical problems. The approximate neural set-point optimiser replaces the whole nonlinear economic set-point optimisation layer.
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Ławryńczuk, M., Tatjewski, P. (2010). Approximate Neural Economic Set-Point Optimisation for Control Systems. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artifical Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13232-2_37
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DOI: https://doi.org/10.1007/978-3-642-13232-2_37
Publisher Name: Springer, Berlin, Heidelberg
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