Identification of Nonlinear Systems Using Neural Networks and Polynomial Models: A Block-Oriented Approach (Lecture Notes in Control and Information Sciences)
Efficient model predictive control algorithm with fuzzy approximations of nonlinear models
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Numerically efficient analytical MPC algorithm based on fuzzy hammerstein models
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
Advanced prediction method in efficient MPC algorithm based on fuzzy hammerstein models
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part I
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In the paper a novel method of prediction generation, based on fuzzy Hammerstein models, is proposed. Using this method one obtains the prediction described by analytical formulas. The prediction has such a form that the MPC (Model Predictive Control) algorithm utilizing it can be formulated as a numerically efficient quadratic optimization problem. At the same time, the algorithm offers practically the same performance as the MPC algorithm in which a nonlinear, non-convex optimization problem must be solved at each iteration. It is demonstrated in the control system of the distillation column - a nonlinear control plant with significant time delay.