This paper details nonlinear Model-based Predictive Control (MPC) algorithms for MIMO processes modelled by means of neural networks of a feedforward structure. Two general MPC techniques are considered: the one with Nonlinear Optimisation (MPC-NO) and the one with Nonlinear Prediction and Linearisation (MPC-NPL). In the first case a nonlinear optimisation problem is solved in real time on-line. In order to reduce the computational burden, in the second case a neural model of the process is used on-line to determine local linearisation and a nonlinear free trajectory. ; Single-point and multi-point linearisation methods are discussed. The MPC-NPL structure is far more reliable and less computationally demanding in comparison with the MPC-NO one because it solves a quadratic programming problem, which can be done efficiently within a foreseeable time frame. At the same time, closed-loop performance of both algorithm classes is similar. Finally, a hybrid MPC algorithm with Nonlinear Prediction, Linearisation and Nonlinear optimisation (MPC-NPL-NO) is discussed.
Zielona Góra: Uniwersytet Zielonogórski
AMCS, volume 17, number 2 (2007) ; kliknij tutaj, żeby przejść
Biblioteka Uniwersytetu Zielonogórskiego
2024-04-02
2024-04-02
30
https://zbc.uz.zgora.pl/publication/88404
Nazwa wydania | Data |
---|---|
A family of model predictive control algorithms with artificial neural networks | 2024-04-02 |
Ławryńczuk, Maciej Korbicz, Józef - ed.
Ławryńczuk, Maciej Tatjewski, Piotr Korbicz, Józef - red. Uciński, Dariusz - red.
Tatjewski, Piotr Ławryńczuk, Maciej Korbicz, Józef - red.
Declercq, Filip Keyser, Robin de Kowalczuk, Zdzisław - red.
Ławryńczuk, Maciej Iacono, Mauro - ed. Kołodziej, Joanna - ed.
Deng, Jiamei Becerra, Victor M. Stobart, Richard Korbicz, Józef - ed.
Ahmida, Zahir Charef, Abdelfettah Becerra, Victor M. Korbicz, Józef - red. Uciński, Dariusz - red.
Korbicz, Józef Patan, Krzysztof Korbicz, Józef - red. Uciński, Dariusz - red.