Abstract
The direct application of a neural model in Model Predictive Control (MPC) algorithms results in a nonlinear, in general non-convex, optimisation problem which must be solved on-line. A linear approximation of the model for the current operating point can be used for prediction in MPC, but for significantly nonlinear processes control accuracy may be not sufficient. MPC algorithm in which the neural model is linearised on-line along a trajectory is discussed. The control policy is calculated from a quadratic programming problem, nonlinear optimisation is not necessary. Accuracy and computational burden of the algorithm are demonstrated for a high-purity high-pressure distillation column.
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Ławryńczuk, M. (2012). On-Line Trajectory-Based Linearisation of Neural Models for a Computationally Efficient Predictive Control Algorithm. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7267. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29347-4_15
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DOI: https://doi.org/10.1007/978-3-642-29347-4_15
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