# A family of model predictive control algorithms with artificial neural networks

International Journal of Applied Mathematics and Computer Science (2007)

- Volume: 17, Issue: 2, page 217-232
- ISSN: 1641-876X

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topŁawryńczuk, Maciej. "A family of model predictive control algorithms with artificial neural networks." International Journal of Applied Mathematics and Computer Science 17.2 (2007): 217-232. <http://eudml.org/doc/207833>.

@article{Ławryńczuk2007,

abstract = {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.},

author = {Ławryńczuk, Maciej},

journal = {International Journal of Applied Mathematics and Computer Science},

keywords = {optimisation; neural networks; quadratic programming; linearisation; predictive control},

language = {eng},

number = {2},

pages = {217-232},

title = {A family of model predictive control algorithms with artificial neural networks},

url = {http://eudml.org/doc/207833},

volume = {17},

year = {2007},

}

TY - JOUR

AU - Ławryńczuk, Maciej

TI - A family of model predictive control algorithms with artificial neural networks

JO - International Journal of Applied Mathematics and Computer Science

PY - 2007

VL - 17

IS - 2

SP - 217

EP - 232

AB - 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.

LA - eng

KW - optimisation; neural networks; quadratic programming; linearisation; predictive control

UR - http://eudml.org/doc/207833

ER -

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