Pełnotekstowe zasoby PLDML oraz innych baz dziedzinowych są już dostępne w nowej Bibliotece Nauki.
Zapraszamy na https://bibliotekanauki.pl
Preferencje help
Polish version English version Język
Widoczny [Schowaj] Abstrakt
Liczba wyników

Znaleziono wyników: 8

Liczba wyników na stronie
first rewind previous Strona / 1 next fast forward last

Wyniki wyszukiwania

help Sortuj według:

help Ogranicz wyniki do:
first rewind previous Strona / 1 next fast forward last
1
Artykuł dostępny w postaci pełnego tekstu - kliknij by otworzyć plik
Content available

Nonlinear state-space predictive control with on-line linearisation and state estimation

100%
Ławryńczuk M.
International Journal of Applied Mathematics and Computer Science
|
2015
|
tom 25
|
nr 4
833-847
EN
This paper describes computationally efficient model predictive control (MPC) algorithms for nonlinear dynamic systems represented by discrete-time state-space models. Two approaches are detailed: in the first one the model is successively linearised on-line and used for prediction, while in the second one a linear approximation of the future process trajectory is directly found on-line. In both the cases, as a result of linearisation, the future control policy is calculated by means of quadratic optimisation. For state estimation, the extended Kalman filter is used. The discussed MPC algorithms, although disturbance state observers are not used, are able to compensate for deterministic constant-type external and internal disturbances. In order to illustrate implementation steps and compare the efficiency of the algorithms, a polymerisation reactor benchmark system is considered. In particular, the described MPC algorithms with on-line linearisation are compared with a truly nonlinear MPC approach with nonlinear optimisation repeated at each sampling instant.
2
Content available remote

A family of model predictive control algorithms with artificial neural networks

100%
Ławryńczuk M.
International Journal of Applied Mathematics and Computer Science
|
2007
|
tom 17
|
nr 2
217-232
EN
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.
3
Content available remote

Efficient nonlinear predictive control based on structured neural models

100%
Ławryńczuk M.
International Journal of Applied Mathematics and Computer Science
|
2009
|
tom 19
|
nr 2
233-246
EN
This paper describes structured neural models and a computationally efficient (suboptimal) nonlinear Model Predictive Control (MPC) algorithm based on such models. The structured neural model has the ability to make future predictions of the process without being used recursively. Thanks to the nature of the model, the prediction error is not propagated. This is particularly important in the case of noise and underparameterisation. Structured models have much better long-range prediction accuracy than the corresponding classical Nonlinear Auto Regressive with eXternal input (NARX) models. The described suboptimal MPC algorithm needs solving on-line only a quadratic programming problem. Nevertheless, it gives closed-loop control performance similar to that obtained in fully-fledged nonlinear MPC, which hinges on online nonconvex optimisation. In order to demonstrate the advantages of structured models as well as the accuracy of the suboptimal MPC algorithm, a polymerisation reactor is studied.
4
Content available remote

Nonlinear predictive control based on neural multi-models

64%
Ławryńczuk M., Tatjewski P.
International Journal of Applied Mathematics and Computer Science
|
2010
|
tom 20
|
nr 1
7-21
EN
This paper discusses neural multi-models based on Multi Layer Perceptron (MLP) networks and a computationally efficient nonlinear Model Predictive Control (MPC) algorithm which uses such models. Thanks to the nature of the model it calculates future predictions without using previous predictions. This means that, unlike the classical Nonlinear Auto Regressive with eXternal input (NARX) model, the multi-model is not used recurrently in MPC, and the prediction error is not propagated. In order to avoid nonlinear optimisation, in the discussed suboptimal MPC algorithm the neural multi-model is linearised on-line and, as a result, the future control policy is found by solving of a quadratic programming problem.
5
Content available remote

An infinite horizon predictive control algorithm based on multivariable input-output models

64%
Ławryńczuk M., Tatjewski P.
International Journal of Applied Mathematics and Computer Science
|
2004
|
tom 14
|
nr 2
167-180
EN
In this paper an infinite horizon predictive control algorithm, for which closed loop stability is guaranteed, is developed in the framework of multivariable linear input-output models. The original infinite dimensional optimisation problem is transformed into a finite dimensional one with a penalty term. In the unconstrained case the stabilising control law, using a numerically reliable SVD decomposition, is derived as an analytical formula, calculated off-line. Considering constraints needs solving on-line a quadratic programming problem. Additionally, it is shown how free and forced responses can be calculated without the necessity of solving a matrix Diophantine equation.
6
Content available remote

Fail-bounded implementations of the numerical model predictive control algorithms

45%
Gawkowski P., Ławryńczuk M., Marusak P., Sosnowski J., Tatjewski P.
Control and Cybernetics
|
2010
|
tom 39
|
nr 4
1117-1134
7
Content available remote

Cooperation of model predictive control with steady-state economic optimisation

45%
Ławryńczuk M., Marusak P. M., Tatjewski P.
Control and Cybernetics
|
2008
|
tom 37
|
nr 1
133-158
8
Content available remote

A computationally efficient stable dual-mode type nonlinear predictive control algorithm

45%
Ławryńczuk M., Tadej W.
Control and Cybernetics
|
2008
|
tom 37
|
nr 1
99-132
first rewind previous Strona / 1 next fast forward last
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.