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Wyszukujesz frazę "Predictive" wg kryterium: Temat


Tytuł:
Extension of first order predictive functional controllers to handle higher order internal models
Autorzy:
Khadir, M. T.
Ringwood, J. V.
Powiązania:
https://bibliotekanauki.pl/articles/907939.pdf
Data publikacji:
2008
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
sterowanie predykcyjne
system fazowy
system oscylacyjny
model predictive control
predictive functional control
non-minimum-phase systems
oscillatory systems
Opis:
Predictive Functional Control (PFC), belonging to the family of predictive control techniques, has been demonstrated as a powerful algorithm for controlling process plants. The input/output PFC formulation has been a particularly attractive paradigm for industrial processes, with a combination of simplicity and effectiveness. Though its use of a lag plus delay ARX/ARMAX model is justified in many applications, there exists a range of process types which may present difficulties, leading to chattering and/or instability. In this paper, instability of first order PFC is addressed, and solutions to handle higher order and difficult systems are proposed. The input/output PFC formulation is extended to cover the cases of internal models with zero and/or higher order pole dynamics in an ARX/ARMAX form, via a parallel and cascaded model decomposition. Finally, a generic form of PFC, based on elementary outputs, is proposed to handle a wider range of higher order oscillatory and non-minimum phase systems. The range of solutions presented are supported by appropriate examples.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2008, 18, 2; 229-239
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Dynamic Algorithm for Linear Quadratic Gaussian Predictive Control
Autorzy:
Ordys, A. W.
Hangstrup, M. E.
Grimble, M. J.
Powiązania:
https://bibliotekanauki.pl/articles/911167.pdf
Data publikacji:
2000
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
przestrzeń fazowa
sterowanie wielowymiarowe
sterowanie predykcyjne
state-space design
multivariable control
linear quadratic Gaussian predictive control
generalized predictive control
Opis:
In this paper, the optimal control law is derived for a multi-variable state-space Linear Quadratic Gaussian Predictive Controller (LQGPC). A dynamic performance index is utilized resulting in an optimal steady-state controller. Knowledge of future reference values is incorporated into the controller design and the solution is derived using the method of Lagrange multipliers. It is shown how the well-known GPC controller can be obtained as a special case of the LQGPC controller design. The important advantage of using the LQGPC framework for designing predictive controllers is that, based on stabilizing properties of LQG control, it enables a systematic approach to selection of the design parameters to yield a stable closed-loop system. The system model considered in this paper can be further extended toalso include direct feed-through and knowledge about future external inputs.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2000, 10, 2; 227-244
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Robust Predictive Control Using a Time-Varying Youla Parameter
Autorzy:
Van den Boom, T. J. J.
De Vries, R. A. J.
Powiązania:
https://bibliotekanauki.pl/articles/908311.pdf
Data publikacji:
1999
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
sterowanie predykcyjne
stabilność
niezawodność
predictive control
Youla parametrization
stability
robustness
Opis:
In this paper, a standard predictive control problem (SPCP) is formulated, which consists of one extended process description with a feedback uncertainty block. The most important finite horizon predictive control problems can be seen as special realizations of this SPCP. The SPCP and its solution are given in a state-space form. The objective of the controller is a nominal performance subject to signal constraints and robust stability with respect to a 1-norm bounded model uncertainty. The optimal controller consists of a feedforward part for nominal signal tracking and a feedback part for disturbance rejection and model error compensation. The feedforward part is realized by the predictive controller for the nominal disturbance-free case. The feedback part of the controller is realized by using the Youla parametrization. The Youla parameter is optimized at every sample time in a receding horizon setting to cope with signal constraints and (robust stability) constraints on the operator itself.
Źródło:
International Journal of Applied Mathematics and Computer Science; 1999, 9, 1; 101-128
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Self-Tuning Generalized Predictive Control With Input Constraints
Autorzy:
Królikowski, A.
Jerzy, D.
Powiązania:
https://bibliotekanauki.pl/articles/908330.pdf
Data publikacji:
2001
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
samostrojenie
sterowanie
generalized predictive control
constraints
self-tuning
ARIMAX/ARMAX systems
Opis:
The handling of various input constraints in the self-tuning generalized predictive control (STGPC) problem of ARIMAX/ARMAX systems is considered. The methods based on the Lagrange multipliers and Lemke's algorithm are used to solve the constrained optimization problem. A self-tuning controller is implemented in an indirect way, and the considered constraints imposed on the control input signal are of the rate, amplitude and energy types. A comparative simulation study of self-tuning control system behaviour is given with respect to the design parameters and constraints. The stability of a closed-loop control system is analyzed and the computational loads of both the methods are compared.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2001, 11, 2; 459-479
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Adaptive Predictive Control of a Distillation Column
Autorzy:
Yoon, T. W.
Yang, D. R.
Lee, K. S.
Kwon, Y. M.
Powiązania:
https://bibliotekanauki.pl/articles/908309.pdf
Data publikacji:
1999
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
adaptacyjny układ sterowania
sterownik predykcyjny
adaptive control
multivariable predictive controller
distillation column
Opis:
Distillation processes reveal complicated multivariable nonlinear dynamics for which it is difficult to design a high-performance control system. This paper proposes an adaptive control scheme for a distillation column. The proposed adaptive system consists of a multivariable receding-horizon predictive controller using a transfer function model and a recursive least-squares (RLS) based estimator. Simulations show a consistent closed-loop performance despite the uncertain nonlinear characteristics of the distillation column.
Źródło:
International Journal of Applied Mathematics and Computer Science; 1999, 9, 1; 193-206
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
On Fast State-Space Algorithms for Predictive Control
Autorzy:
Błachuta, M. J.
Powiązania:
https://bibliotekanauki.pl/articles/908313.pdf
Data publikacji:
1999
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
równanie różniczkowe Riccatiego
równanie Chandrasekhara
sterowanie predykcyjne
Riccati equation
Chandrasekhar equation
LQG control
predictive control
Opis:
A Riccati-equation-based solution to a class of receding-horizon predictive control problems for an explicit-delay state-space model of an ARMAX system is found and the corresponding vector Chandrasekhar-type equations are derived for both filter and controller gains to improve the computational efficiency.
Źródło:
International Journal of Applied Mathematics and Computer Science; 1999, 9, 1; 149-160
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A family of model predictive control algorithms with artificial neural networks
Autorzy:
Ławryńczuk, M.
Powiązania:
https://bibliotekanauki.pl/articles/929631.pdf
Data publikacji:
2007
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
sterowanie predykcyjne
sieć neuronowa
optymalizacja
linearyzacja
programowanie kwadratowe
predictive control
neural networks
optimisation
linearisation
quadratic programming
Opis:
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.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2007, 17, 2; 217-232
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Efficient nonlinear predictive control based on structured neural models
Autorzy:
Ławryńczuk, M.
Powiązania:
https://bibliotekanauki.pl/articles/907652.pdf
Data publikacji:
2009
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
sterowanie procesami
sterowanie predykcyjne
sieć neuronowa
optymalizacja
linearyzacja
process control
model predictive control
neuron network
optimisation
linearisation
Opis:
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.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2009, 19, 2; 233-246
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A numerically efficient fuzzy MPC algorithm with fast generation of the control signal
Autorzy:
Marusak, Piotr M.
Powiązania:
https://bibliotekanauki.pl/articles/1838187.pdf
Data publikacji:
2021
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
model predictive control
fuzzy system
fuzzy control
nonlinear control
sterowanie predykcyjne
system rozmyty
sterowanie rozmyte
sterowanie nieliniowe
Opis:
Model predictive control (MPC) algorithms are widely used in practical applications. They are usually formulated as optimization problems. If a model used for prediction is linear (or linearized on-line), then the optimization problem is a standard, i.e., quadratic, one. Otherwise, it is a nonlinear, in general, nonconvex optimization problem. In the latter case, numerical problems may occur during solving this problem, and the time needed to calculate control signals cannot be determined. Therefore, approaches based on linear or linearized models are preferred in practical applications. A novel, fuzzy, numerically efficient MPC algorithm is proposed in the paper. It can offer better performance than the algorithms based on linear models, and very close to that of the algorithms based on nonlinear optimization. Its main advantage is the short time needed to calculate the control value at each sampling instant compared with optimization-based numerical algorithms; it is a combination of analytical and numerical versions of MPC algorithms. The efficiency of the proposed approach is demonstrated using control systems of two nonlinear control plants: the first one is a chemical CSTR reactor with a van de Vusse reaction, and the second one is a pH reactor.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2021, 31, 1; 59-71
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An infinite horizon predictive control algorithm based on multivariable input-output models
Autorzy:
Ławryńczuk, M.
Tatjewski, P.
Powiązania:
https://bibliotekanauki.pl/articles/907410.pdf
Data publikacji:
2004
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
sterowanie predykcyjne
horyzont nieskończony
programowanie kwadratowe
model predictive control
stability
infinite horizon
singular value decomposition
quadratic programming
Opis:
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.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2004, 14, 2; 167-180
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Input constraints handling in an MPC/feedback linearization scheme
Autorzy:
Deng, J.
Becerra, V. M.
Stobart, R.
Powiązania:
https://bibliotekanauki.pl/articles/907653.pdf
Data publikacji:
2009
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
sterowanie predykcyjne
sterowanie odwrotne
sieć neuronowa
system nieliniowy
predictive control
feedback linearization
neural network
nonlinear system
constraints
Opis:
The combination of model predictive control based on linear models (MPC) with feedback linearization (FL) has attracted interest for a number of years, giving rise to MPC+FL control schemes. An important advantage of such schemes is that feedback linearizable plants can be controlled with a linear predictive controller with a fixed model. Handling input constraints within such schemes is difficult since simple bound contraints on the input become state dependent because of the nonlinear transformation introduced by feedback linearization. This paper introduces a technique for handling input constraints within a real timeMPC/FL scheme, where the plant model employed is a class of dynamic neural networks. The technique is based on a simple affine transformation of the feasible area. A simulated case study is presented to illustrate the use and benefits of the technique.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2009, 19, 2; 219-232
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Nonlinear predictive control based on neural multi-models
Autorzy:
Ławryńczuk, M.
Tatjewski, P.
Powiązania:
https://bibliotekanauki.pl/articles/907773.pdf
Data publikacji:
2010
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
sterowanie procesami
sterowanie predykcyjne
sieć neuronowa
optymalizacja
linearyzacja
process control
model predictive control
neural networks
optimisation
linearisation
Opis:
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.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2010, 20, 1; 7-21
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Adaptive Predictive Controller Using Orthonormal Series Functions
Autorzy:
Oliveira, G. H. C.
Amaral, W. C.
Favier, G.
Powiązania:
https://bibliotekanauki.pl/articles/908308.pdf
Data publikacji:
1999
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
sterowanie predykcyjne
adaptacyjny układ sterowania
niepewność
model-based predictive control
adaptive control
uncertain process
orthonormal series functions
Opis:
A constrained adaptive predictive control method that uses uncertain process modelling based on orthonormal series functions is considered. Such unstructured modelling is described as a weighted sum of orthonormal functions using approximate information about the time constant of the process. The orthonormal series functions model can thus be used to derive a j-step-ahead output prediction according to the constrained adaptive predictive control law. In relation to predictive controllers based on structured models, this approach presents the advantage of not requiring prior knowledge of the order or time delay, which decrease prediction errors and lead to a better closed loop performance when these parameters are not well known. Stability issues of the proposed control scheme are discussed and, finally, a simulation example is given to show the performance of the algorithm.
Źródło:
International Journal of Applied Mathematics and Computer Science; 1999, 9, 1; 175-191
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Suboptimal Nonlinear Predictive Controllers
Autorzy:
Declercq, F.
De Keyser, R.
Powiązania:
https://bibliotekanauki.pl/articles/908312.pdf
Data publikacji:
1999
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
sterowanie predykcyjne
sterowanie nieliniowe
programowanie sekwencyjne
równanie diofantyczne
predictive control
nonlinear control
sequential quadratic programming
diophantine equations
Opis:
Predictive control based on linear models has become a mature technology in the last decade. Many successful real-time applications can be found in almost every sector of industry. Nonlinear predictive control can further increase the performance of this easy-to-understand control strategy. One of the main problems of implementing nonlinear predictive control is the computational aspect, which is of most importance in real-life applications. In this paper, suboptimal nonlinear predictive control strategies are proposed and compared. The nonlinear predictors are built based on neural identification methods or by white modelling. The use of diophantine equations, which is a common technique to calculate the optimal contribution of the noise model, is avoided by using a more natural method. The comparison between the control algorithms is made based on a simulated discrete multivariable nonlinear system and a continuous stirred tank reactor.
Źródło:
International Journal of Applied Mathematics and Computer Science; 1999, 9, 1; 129-148
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Some Issues in the Design of Predictive Controllers
Autorzy:
Magni, L.
De Nicolao, G.
Scattolini, R.
Powiązania:
https://bibliotekanauki.pl/articles/908316.pdf
Data publikacji:
1999
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
sterowanie predykcyjne
identyfikacja w sterowaniu
predictive control
robust control
two degree of freedom regulation
identification for control
Opis:
In the paper, we discuss how to design a predictive controller capable of addressing a number of important issues ranging from nominal stability to the model identification/controller design interplay. Nominal stability is ensured by resorting to Constrained Receding Horizon Predictive Control. As for robust stability, the connections between the frequency weighting P-polynomial in the cost function and the achievable robustness against multiplicative uncertainty are investigated. Then, a two-step design procedure is proposed in order to enhance the closed-loop robustness and obtain nominal performances. A correlation technique is also proposed as a tool to estimate uncertainty bounds to be used in controller design. Finally, the control and identification procedures are put together to form an iterative identification/control design methodology. A simulation example is reported to illustrate the approach.
Źródło:
International Journal of Applied Mathematics and Computer Science; 1999, 9, 1; 9-24
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł

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