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    Wyświetlanie 1-12 z 12
    Tytuł:
    Neural Network-Based Narx Models in Non-Linear Adaptive Control
    Autorzy:
    Dzieliński, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/907986.pdf
    Data publikacji:
    2002
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    automatyka
    neural networks
    adaptive control
    nonlinear systems
    Opis:
    The applicability of approximate NARX models of non-linear dynamic systems is discussed. The models are obtained by a new version of Fourier analysis-based neural network also described in the paper. This constitutes a reformulation of a known method in a recursive manner, i.e. adapted to account for incoming data on-line. The method allows us to obtain an approximate model of the non-linear system. The estimation of the influence of the modelling error on the discrepancy between the model and real system outputs is given. Possible applications of this approach to the design of BIBO stable closed-loop control are proposed.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2002, 12, 2; 235-240
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Fuzzy and Neural Control of an Induction Motor
    Autorzy:
    Denai, M., A.
    Attia, S. A.
    Powiązania:
    https://bibliotekanauki.pl/articles/908003.pdf
    Data publikacji:
    2002
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    automatyka
    fuzzy control
    neural networks
    induction motor
    vector control
    speed observer
    Opis:
    This paper presents some design approaches to hybrid control systems combining conventional control techniques with fuzzy logic and neural networks. Such a mixed implementation leads to a more effective control design with improved system performance and robustness. While conventional control allows different design objectives such as steady state and transient characteristics of the closed loop system to be specified, fuzzy logic and neural networks are integrated to overcome the problems with uncertainties in the plant parameters and structure encountered in the classical model-based design. Induction motors are characterised by complex, highly non-linear and time-varying dynamics and inaccessibility of some states and outputs for measurements, and hence can be considered as a challenging engineering problem. The advent of vector control techniques has partially solved induction motor control problems, because they are sensitive to drive parameter variations and performance may deteriorate if conventional controllers are used. Fuzzy logic and neural network-based controllers are considered as potential candidates for such an application. Three control approaches are developed and applied to adjust the speed of the drive system. The first control design combines the variable structure theory with the fuzzy logic concept. In the second approach neural networks are used in an internal model control structure. Finally, a fuzzy state feedback controller is developed based on the pole placement technique. A simulation study of these methods is presented. The effectiveness of these controllers is demonstrated for different operating conditions of the drive system.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2002, 12, 2; 221-233
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Improving the Generalization Ability of Neuro-Fuzzy Systems by e-Insensitive Learning
    Autorzy:
    Łęski, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/908037.pdf
    Data publikacji:
    2002
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    informatyka
    fuzzy systems
    neural networks
    tolerant learning
    generalization control
    robust methods
    Opis:
    A new learning method tolerant of imprecision is introduced and used in neuro-fuzzy modelling. The proposed method makes it possible to dispose of an intrinsic inconsistency of neuro-fuzzy modelling, where zero-tolerance learning is used to obtain a fuzzy model tolerant of imprecision. This new method can be called e-insensitive learning, where, in order to fit the fuzzy model to real data, the e-insensitive loss function is used. e-insensitive learning leads to a model with minimal Vapnik-Chervonenkis dimension, which results in an improved generalization ability of this system. Another advantage of the proposed method is its robustness against outliers. This paper introduces two approaches to solving e-insensitive learning problem. The first approach leads to a quadratic programming problem with bound constraints and one linear equality constraint. The second approach leads to a problem of solving a system of linear inequalities. Two computationally efficient numerical methods for e-insensitive learning are proposed. Finally, examples are given to demonstrate the validity of the introduced methods.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2002, 12, 3; 437-447
    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:
    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:
    Soft computing in model-based predictive control
    Autorzy:
    Tatjewski, P.
    Ławryńczuk, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/908473.pdf
    Data publikacji:
    2006
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    sterowanie procesami
    sterowanie predykcyjne
    system nieliniowy
    system rozmyty
    sieć neuronowa
    process control
    model predictive control
    nonlinear systems
    fuzzy systems
    neural networks
    Opis:
    The application of fuzzy reasoning techniques and neural network structures to model-based predictive control (MPC) is studied. First, basic structures of MPC algorithms are reviewed. Then, applications of fuzzy systems of the Takagi-Sugeno type in explicit and numerical nonlinear MPC algorithms are presented. Next, many techniques using neural network modeling to improve structural or computational properties of MPC algorithms are presented and discussed, from a neural network model of a process in standard MPC structures to modeling parts or entire MPC controllers with neural networks. Finally, a simulation example and conclusions are given.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2006, 16, 1; 7-26
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Stabilising solutions to a class of nonlinear optimal state tracking problems using radial basis function networks
    Autorzy:
    Ahmida, Z.
    Charef, A.
    Becerra, V. M.
    Powiązania:
    https://bibliotekanauki.pl/articles/908523.pdf
    Data publikacji:
    2005
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    system nieliniowy
    sterowanie optymalne
    radialna funkcja bazowa
    sieć neuronowa
    regulacja predykcyjna
    sterowanie wyprzedzające
    nonlinear systems
    optimal control
    radial basis functions
    neural networks
    predictive control
    feedforward control
    Opis:
    A controller architecture for nonlinear systems described by Gaussian RBF neural networks is proposed. The controller is a stabilising solution to a class of nonlinear optimal state tracking problems and consists of a combination of a state feedback stabilising regulator and a feedforward neuro-controller. The state feedback stabilising regulator is computed online by transforming the tracking problem into a more manageable regulation one, which is solved within the framework of a nonlinear predictive control strategy with guaranteed stability. The feedforward neuro-controller has been designed using the concept of inverse mapping. The proposed control scheme is demonstrated on a simulated single-link robotic manipulator.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2005, 15, 3; 369-381
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Neural network-based MRAC control of dynamic nonlinear systems
    Autorzy:
    Debbache, G.
    Bennia, A.
    Goléa, N.
    Powiązania:
    https://bibliotekanauki.pl/articles/908401.pdf
    Data publikacji:
    2006
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    sieć neuronowa
    model odniesienia
    system nieliniowy
    sterowanie adaptacyjne
    obserwator
    stabilność
    neural networks
    reference model
    nonlinear systems
    adaptive control
    observer
    stability
    Opis:
    This paper presents direct model reference adaptive control for a class of nonlinear systems with unknown nonlinearities. The model following conditions are assured by using adaptive neural networks as the nonlinear state feedback controller. Both full state information and observer-based schemes are investigated. All the signals in the closed loop are guaranteed to be bounded and the system state is proven to converge to a small neighborhood of the reference model state. It is also shown that stability conditions can be formulated as linear matrix inequalities (LMI) that can be solved using efficient software algorithms. The control performance of the closed-loop system is guaranteed by suitably choosing the design parameters. Simulation results are presented to show the effectiveness of the approach.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2006, 16, 2; 219-232
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Neural network based feedback linearization control of a servo-hydraulic vehicle suspension system
    Autorzy:
    Pedro, J. O.
    Dahunsi, O. A.
    Powiązania:
    https://bibliotekanauki.pl/articles/907825.pdf
    Data publikacji:
    2011
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    sieć neuronowa
    sterowanie bezpośrednie
    sterowanie ze sprzężeniem zwrotnym
    regulacja PID
    komfort jazdy
    układ zawieszenia
    neural networks
    direct adaptive control
    feedback linearization control
    PID control
    ride comfort
    suspension system
    servo-hydraulics
    Opis:
    This paper presents the design of a neural network based feedback linearization (NNFBL) controller for a two degree-offreedom (DOF), quarter-car, servo-hydraulic vehicle suspension system. The main objective of the direct adaptive NNFBL controller is to improve the system's ride comfort and handling quality. A feedforward, multi-layer perceptron (MLP) neural network (NN) model that is well suited for control by discrete input-output linearization (NNIOL) is developed using input-output data sets obtained from mathematical model simulation. The NN model is trained using the Levenberg- Marquardt optimization algorithm. The proposed controller is compared with a constant-gain PID controller (based on the Ziegler-Nichols tuning method) during suspension travel setpoint tracking in the presence of deterministic road disturbance. Simulation results demonstrate the superior performance of the proposed direct adaptive NNFBL controller over the generic PID controller in rejecting the deterministic road disturbance. This superior performance is achieved at a much lower control cost within the stipulated constraints.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2011, 21, 1; 137-147
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Almost periodic synchronization of fuzzy cellular neural networks with time-varying delays via state-feedback and impulsive control
    Autorzy:
    Li, Yongkun
    Wang, Huimei
    Meng, Xiaofang
    Powiązania:
    https://bibliotekanauki.pl/articles/331017.pdf
    Data publikacji:
    2019
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    almost periodic solution
    fuzzy cellular neural networks
    time varying delays
    state feedback
    impulsive control
    rozwiązanie okresowe
    sieć neuronowa
    opóźnienie czasowe
    sprzężenie zwrotne
    Opis:
    In this paper, we are concerned with drive-response synchronization for a class of fuzzy cellular neural networks with time varying delays. Based on the exponential dichotomy of linear differential equations, the Banach fixed point theorem and the differential inequality technique, we obtain the existence of almost periodic solutions of this class of networks. Then, we design a state feedback and an impulsive controller, and construct a suitable Lyapunov function to study the problem of global exponential almost periodic synchronization for the drive-response systems considered. At the end of the paper, we provide an example to verify the effectiveness of the theoretical results.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2019, 29, 2; 337-349
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Nonlinear model predictive control of a boiler unit: a fault tolerant control study
    Autorzy:
    Patan, K.
    Korbicz, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/331450.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    rekurencyjna sieć neuronowa
    model procesu
    sterowanie predykcyjne
    detekcja uszkodzeń
    zbiornik przepływowy
    recurrent neural networks
    process model
    predictive control
    fault detection
    boiler unit
    Opis:
    This paper deals with a nonlinear model predictive control designed for a boiler unit. The predictive controller is realized by means of a recurrent neural network which acts as a one-step ahead predictor. Then, based on the neural predictor, the control law is derived solving an optimization problem. Fault tolerant properties of the proposed control system are also investigated. A set of eight faulty scenarios is prepared to verify the quality of the fault tolerant control. Based of different faulty situations, a fault compensation problem is also investigated. As the automatic control system can hide faults from being observed, the control system is equipped with a fault detection block. The fault detection module designed using the one-step ahead predictor and constant thresholds informs the user about any abnormal behaviour of the system even in the cases when faults are quickly and reliably compensated by the predictive controller.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2012, 22, 1; 225-237
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Neural Network Evaluation of Model-Based Residuals in Fault Detection of Time Delay Systems
    Autorzy:
    Zitek, P.
    Mankova, R.
    Hlava, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/908288.pdf
    Data publikacji:
    1999
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    wykrywanie błędu
    model anizochroniczny
    obserwator stanów
    sterowanie wewnętrzne
    sieć neuronowa
    model-based fault detection
    anisochronic model
    state observer
    internal model control
    artificial neural networks
    Opis:
    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.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 1999, 9, 3; 599-617
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-12 z 12

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