Abstract
This paper presents a predictive control scheme integrated with economic optimisation. Two neural models are used: a dynamic one (for the control subproblem) and a steady-state one (for the economic optimisation subproblem). The algorithm is computationally efficient because it needs solving on-line only one quadratic programming problem. Unlike the classical control system structure, the necessity of repeating two nonlinear optimisation problems at each sampling instant is avoided.
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Ławryńczuk, M., Tatjewski, P. (2008). Efficient Predictive Control Integrated with Economic Optimisation Based on Neural Models. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2008. ICAISC 2008. Lecture Notes in Computer Science(), vol 5097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69731-2_12
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DOI: https://doi.org/10.1007/978-3-540-69731-2_12
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