Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Mathematical Programming: Series A and B
A Family of Model Predictive Control Algorithms With Artificial Neural Networks
International Journal of Applied Mathematics and Computer Science
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
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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.