Abstract
This paper discusses the problem of cooperation of economic optimisation with Model Predictive Control (MPC) algorithms when the dynamics of disturbances is comparable with the dynamics of the process. A dynamic neural model is used in the suboptimal nonlinear MPC algorithm with Nonlinear Prediction and Linearisation (MPC-NPL), a steady-state neural model is used in approximate economic optimisation which is executed as frequently as the MPC algorithm. The MPC-NPL algorithm requires solving on-line only a quadratic programming problem whereas approximate economic optimisation needs solving a linear programming problem. As a result, the necessity of repeating two nonlinear optimisation problems at each sampling instant is avoided.
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Ławryńczuk, M. (2007). Neural Models in Computationally Efficient Predictive Control Cooperating with Economic Optimisation. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74695-9_67
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DOI: https://doi.org/10.1007/978-3-540-74695-9_67
Publisher Name: Springer, Berlin, Heidelberg
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