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1
Content available remote A dead-beat internal model control for trajectory tracking in discrete- time linear system
Mahmoud Ines, Saidi Imen
Przegląd Elektrotechniczny
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2023
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R. 99, nr 1
116--119
EN
In this paper, a robust control law is applied. This command is called dead-beat internal model control. The application of this command on a discrete-time linear system presents good performance in precision, low overshoots and tracking of reference trajectories, which shows the effectiveness of the proposed command. The effect of the controller by dead-beat internal model that the error vanishes in a finite and minimal number of sampling periods and remains zero thereafter.
PL
W niniejszym artykule zastosowano solidne prawo kontrolne. To polecenie nazywa się kontrolą martwego modelu wewnętrznego. Zastosowanie tego polecenia na liniowym systemie dyskretnym daje dobre wyniki w precyzji, małych przeregulowaniach i śledzeniu trajektorii odniesienia, co świadczy o skuteczności proponowanego polecenia. Efekt działania regulatora przez martwy model wewnętrzny polegający na tym, że błąd zanika w skończonej i minimalnej liczbie okresów próbkowania, a następnie pozostaje zerowy.
2
Content available remote Internal model control using artificial neural networks for linear minimum phase systems
Mahmoud Ines, Saidi Imen, Bejaoui Islem
Przegląd Elektrotechniczny
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2022
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R. 98, nr 2
45--48
EN
In this paper, we are interested in the internal model control using neural networks in the case of linear minimum phase systems. We propose, to use the neural internal model control to solve the inversion problem of a model M(z) in order to design the IMC controller. An example application is presented and the implementation of the proposed approach is discussed.
PL
W niniejszym artykule interesuje nas sterowanie modelem wewnętrznym za pomocą sieci neuronowych w przypadku liniowych układów o minimalnej fazie. Proponujemy wykorzystanie neuronowego sterowania modelem wewnętrznym do rozwiązania problemu inwersji modelu M(z) w celu zaprojektowania sterownika IMC. Przedstawiono przykładową aplikację oraz omówiono wdrożenie proponowanego podejścia.
3
Content available remote Internal model control for underactuated systems based on novel virtual inputs method
Touati Nahla, Said Imen
Przegląd Elektrotechniczny
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2021
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R. 97, nr 9
95--99
EN
An internal model control strategy is proposed in this paper for underactuated linear systems. Their associated models are non-square. When applying internal model control strategy, a specific inversion of a square model is needed to design the controller. For that reason, squaring the model of underactuated system by adding virtual control inputs is proposed in this paper. The obtained internal model structure is then modified in order to eliminate the excess inputs. Simulation results on a three inputs/four outputs system is considered to prove the effectiveness and reliability of the proposed method.
PL
W artykule zaproponowano strategię kontroli modelu wewnętrznego dla niedostatecznie dostosowanych systemów liniowych. Powiązane z nimi modele nie są kwadratowe. Przy stosowaniu strategii kontroli modelu wewnętrznego do zaprojektowania regulatora potrzebna jest specyficzna inwersja modelu kwadratowego. Z tego powodu w niniejszym artykule zaproponowano podniesienie do kwadratu modelu niedostatecznie uruchomionego systemu poprzez dodanie wirtualnych wejść sterujących. Uzyskana struktura modelu wewnętrznego jest następnie modyfikowana w celu wyeliminowania nadmiernych nakładów. Uważa się, że wyniki symulacji w systemie trzech wejść / czterech wyjść potwierdzają skuteczność i niezawodność proponowanej metody.
4
Content available remote Zastosowanie sterowania z modelem wewnętrznym opartego o sieci neuronowe dla napędu z elastycznym sprzęgłem
Żychlewicz M., Derugo P.
Przegląd Elektrotechniczny
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2018
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R. 94, nr 5
163--168
PL
W artykule opisano sterowanie układem napędowym z połączeniem sprężystym, pętla regulacji prędkości została zaprojektowana w oparciu o dwa modele neuronowe. Jeden z nich stanowi główny regulator, natomiast drugi jest modelem odniesienia wykorzystywanym w trakcie obliczeń. Adaptacja wag sieci neuronowych jest realizowana on-line. Artykuł zawiera opis teoretyczny zaimplementowanej struktury, a także badania symulacyjne oraz eksperymentalne zrealizowane z wykorzystaniem procesora sygnałowego karty dSPACE1103.
EN
Paper presents control system applied for electrical drive with elastic connections. Speed control loop of the whole structure is based on two neural models. One of them is applied as the main controller, the second is the internal model of the plant used for calculations of control signal. Adaptation of weights in neural networks is done in on-line mode. Article contains theoretical description of implemented control structure, simulation tests as well as experimental tests using digital signal processor of dSPACE1103.
5
Content available Decoupling control for permanent magnet in-wheel motor using internal model control based on back-propagation neural network inverse system
Li Y., Zhang B., Xu X.
Bulletin of the Polish Academy of Sciences. Technical Sciences
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2018
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Vol. 66, nr 6
961--972
EN
The permanent magnet in-wheel motor (PMIWM) is a nonlinear, multivariable, strongly coupled and highly complex system. The key to the development and application of the PMIWM consists in the improvement of its control accuracy and dynamic performance. In order to effectively decouple the PMIWM, this paper presents a novel internal model control (IMC) approach based on the back-propagation neural network inverse (BPNNI) control method. First, theoretical analysis is conducted to show the existence of the PMIWM inverse system, to be modeled mathematically. The inverse system approximated and identified by the back-propagation neural network (BPNN) constitutes the back-propagation neural network inverse (BPNNI) system. Then, by cascading the BPNNI system on the left side of the original PMIWM system, a new decoupling, pseudo-linear system is established. Moreover, the 2-DOF internal model control (IMC) method is employed to design the extra closed-loop controller that further improves disturbance rejection and robustness of the whole system. Consequently, the proposed decoupling control approach incorporates the advantages of both the BPNNI and the IMC. Effectiveness of thus proposed control approach is verified by means of simulation and real-time hardware-in-the-loop (HIL) experiments.
6
Content available remote Regulatory neuronowe trenowane off-line zastosowane w strukturze sterowania prędkością napędu dwumasowego
Kamiński M., Orłowska-Kowalska T.
Przegląd Elektrotechniczny
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2015
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R. 91, nr 1
9-12
PL
W artykule przedstawiono analizę działania neuronowych regulatorów prędkości trenowanych off-line, zaimplementowanych w układzie napędowym z połączeniem elastycznym. Testom poddano sterowanie z modelem odwrotnym (Direct Inverse Control) oraz sterowanie z modelem wewnętrznym (Internal Model Control). Istotnym założeniem prowadzonych badań był brak adaptacji modeli neuronowych w trakcie działania struktury sterowania. Zaprezentowano wyniki badań symulacyjnych oraz eksperymentalnych zrealizowanych dla znamionowych oraz zmienionych parametrów obiektu.
EN
In this paper analysis of neural speed controllers implemented in electrical drive with elastic connection is presented. Two control structures were tested: Direct Inverse Control and Internal Model Control. Important assumption in described application is lack of weights adaptation during work of the drive. Simulations and experimental tests prepared for nominal and changed values of parameters are shown.
7
Content available remote Fuzzy feedback linearizing controller and its equivalence with the fuzzy nonlinear internal model control structure
Boukezzoula R., Galichet S., Foulloy L.
International Journal of Applied Mathematics and Computer Science
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2007
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Vol. 17, no 2
233-248
EN
This paper examines the inverse control problem of nonlinear systems with stable dynamics using a fuzzy modeling approach. Indeed, based on the ability of fuzzy systems to approximate any nonlinear mapping, the nonlinear system is represented by a Takagi-Sugeno (TS) fuzzy system, which is then inverted for designing a fuzzy controller. As an application of the proposed inverse control methodology, two popular control structures, namely, feedback linearization and Nonlinear Internal Model Control (NIMC) are investigated. Moreover, the paper points out that, under some conditions, both of the control structures are equivalent and naturally implement a Smith predictor in the presence of time delays.
8
Content available remote Ehmac - a New Simple Tool for Robust Linear Multivariable Control
Latawiec K. J., Rojek R.
International Journal of Applied Mathematics and Computer Science
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2000
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Vol. 10, no 3
575-590
EN
A combination of long range predictive control-originated EHPC and internal model control-structured MAC is shown to produce a new, simple but effective Extended Horizon Model Algorithmic Control (EHMAC). The EHMAC strategy can be used to robustly control open-loop stable non-minimum phase (possibly non-square) MIMO systems under very large model-plant mismatches. Robust EHMAC design is made straightforward by means of a separate selection of a single prediction horizon and an IMC filter parameter, which can be easily auto-tuned.
9
Content available remote Neural network based adaptive internal model control for nonlinear plants
Ma Z., Bajić V. B., Ho D. W. C.
Journal of Applied Computer Science
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1999
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Vol. 7, nr 1
63-78
EN
A novel non-parametric adaptive control method for nonlinear plants is proposed. It combines neural network (NN) based identification and internal model control (IMC) strategy. The NN is used to determine on-line an approximation of the unknown nonlinear process model. The NN parameters are updated according to the error between the plant output and the NN output. The NN can track the system output very well, so that an adaptive IMC can be implemented successfully. The design does not require computation of the inverse of the internal model of the process. Instead, it uses only system input-output data and NN output. The effectiveness of the proposed method is illustrated by a simulation experiment.
10
Content available remote Neural Network Evaluation of Model-Based Residuals in Fault Detection of Time Delay Systems
Zitek P., Mankova R., Hlava J.
International Journal of Applied Mathematics and Computer Science
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1999
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Vol. 9, no 3
599-617
EN
Model-based fault detection becomes rather questionable if a supervised plant belongs to the class of systems with distributed parameters and significant delays. Two methods of fault detection have been developed for this class of plants, namely a method of functional (anisochronic) state observer and a modified internal model control scheme adopted for that purpose. Both these model schemes are employed to generate residuals, i.e. differences suitable to watch whether a malfunction of the control operation has occurred. Continuous evaluation of residuals is provided by means of a dynamic application of artificial neural networks (ANNs). This evaluation is carried out on the basis of prediction of time series evolution, where the accordance obtained between the prediction and measured outputs is used as a classification criterion. Implementation of both the methods is demonstrated on a laboratory-scale heat transfer set-up, making use of the Real-Time Matlab software.
11
Content available remote The central control accuracy theorem for internal model control and its applications
Latawiec K., Marciak C., Wrzuszczak J.
Archives of Control Sciences
|
1998
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Vol. 7, no. 3/4
227-239
EN
The well-known feature of the Internal Model Control (IMC) structure, providing steady-state error-free servo and regulatory controls for step-wise forcing signals, is generalized by what is termed the "central IMC theorem". The generalization consists not only in the original extension of the SISO and square MIMO results to nonsquare MIMO systems, but mainly in redeveloping certain IMC accuracy-related properties to any appropriately scaled, stabilizing controller. Instructive applications of the central IMC theorem in Model Algorithmic Control (MAC), Extended Horizon Model Algorithmic Control (EHMAC) and Generalized Predictive Control (GPC) are demonstrated.
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