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    Tytuł:
    Sub-Optimal Nonlinear Predictive and Adaptive Control Based on the Parametric Volterra Model
    Autorzy:
    Haber, R.
    Bars, R.
    Lengyel, O.
    Powiązania:
    https://bibliotekanauki.pl/articles/908307.pdf
    Data publikacji:
    1999
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    sterowanie predykcyjne
    sterowanie nieliniowe
    sterowanie optymalne
    układ nieliniowy
    adaptacyjny układ sterowania
    predictive control
    nonlinear control
    optimal control
    nonlinear systems
    adaptive control
    Opis:
    Predictive control algorithms have been worked out mainly to control linear plants. There is a great demand to apply different control ideas to nonlinear systems. Using predictive control algorithms for nonlinear systems is a promising technique. Extended horizon one-step-ahead and long-range optimal predictive control algorithms are given here for the parametric Volterra model (which includes also the generalized Hammerstein model). A quadratic cost function is minimized which considers the quadratic deviations of the reference signal and the output signal at a future point (or points) beyond the dead time and also penalizes large control signal increments. For prediction of the output signal, a predictive model is applied which uses information about the input and output signals up to the current time. A predictive transformation of the nonlinear dynamic model is given. The incremental model is advantageous since the cost function contains the control increment and not the control signal itself. An incremental transformation of the predictive forms is also described. Sub-optimal solutions to the optimal control algorithms are discussed with different assumptions for the control signal during the control horizon. The effect of the different strategies and the effect of the tuning parameters is investigated through simulation examples.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 1999, 9, 1; 161-173
    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:
    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:
    Supervisory predictive control and on-line set-point optimization
    Autorzy:
    Tatjewski, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/929583.pdf
    Data publikacji:
    2010
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    sterowanie predykcyjne
    sterowanie nieliniowe
    linearyzacja
    model niepewności
    sterowność wymuszona
    optymalizacja
    predictive control
    nonlinear control
    linearisation
    model uncertainty
    constrained control
    set-point optimization
    Opis:
    The subject of this paper is to discuss selected effective known and novel structures for advanced process control and optimization. The role and techniques of model-based predictive control (MPC) in a supervisory (advanced) control layer are first shortly discussed. The emphasis is put on algorithm efficiency for nonlinear processes and on treating uncertainty in process models, with two solutions presented: the structure of nonlinear prediction and successive linearizations for nonlinear control, and a novel algorithm based on fast model selection to cope with process uncertainty. Issues of cooperation between MPC algorithms and on-line steady-state set-point optimization are next discussed, including integrated approaches. Finally, a recently developed two-purpose supervisory predictive set-point optimizer is discussed, designed to perform simultaneously two goals: economic optimization and constraints handling for the underlying unconstrained direct controllers.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2010, 20, 3; 483-495
    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 for processes with complex dynamics: A parameterisation approach using Laguerre functions
    Autorzy:
    Ławryńczuk, Maciej
    Powiązania:
    https://bibliotekanauki.pl/articles/329999.pdf
    Data publikacji:
    2020
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    process control
    nonlinear model predictive control
    Laguerre functions
    linearisation
    sterowanie procesami
    model nieliniowy
    sterowanie predykcyjne
    funkcje Laguerre’a
    Opis:
    Classical model predictive control (MPC) algorithms need very long horizons when the controlled process has complex dynamics. In particular, the control horizon, which determines the number of decision variables optimised on-line at each sampling instant, is crucial since it significantly affects computational complexity. This work discusses a nonlinear MPC algorithm with on-line trajectory linearisation, which makes it possible to formulate a quadratic optimisation problem, as well as parameterisation using Laguerre functions, which reduces the number of decision variables. Simulation results of classical (not parameterised) MPC algorithms and some strategies with parameterisation are thoroughly compared. It is shown that for a benchmark system the MPC algorithm with on-line linearisation and parameterisation gives very good quality of control, comparable with that possible in classical MPC with long horizons and nonlinear optimisation.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2020, 30, 1; 35-46
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Finite horizon nonlinear predictive control by the Taylor approximation: Application to robot tracking trajectory
    Autorzy:
    Hedjar, R.
    Toumi, R.
    Boucher, P.
    Dumur, D.
    Powiązania:
    https://bibliotekanauki.pl/articles/908454.pdf
    Data publikacji:
    2005
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    układ nieliniowy
    układ ciągły
    sterowanie predykcyjne
    aproksymacja Taylora
    śledzenie trajektorii
    nonlinear continuous time predictive control
    Taylor approximation
    tracking trajectory and robot
    Opis:
    In industrial control systems, practical interest is driven by the fact that today’s processes need to be operated under tighter performance specifications. Often these demands can only be met when process nonlinearities are explicitly considered in the controller. Nonlinear predictive control, the extension of well-established linear predictive control to nonlinear systems, appears to be a well-suited approach for this kind of problems. In this paper, an optimal nonlinear predictive control structure, which provides asymptotic tracking of smooth reference trajectories, is presented. The controller is based on a finite–horizon continuous time minimization of nonlinear predicted tracking errors. A key feature of the control law is that its implementation does not need to perform on-line optimization, and asymptotic tracking of smooth reference signal is guaranteed. An integral action is used to increase the robustness of the closed-loop system with respect to uncertainties and parameters variations. The proposed control scheme is first applied to planning motions problem of a mobile robot and, afterwards, to the trajectory tracking problem of a rigid link manipulator. Simulation results are performed to validate the tracking performance of the proposed controller.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2005, 15, 4; 527-540
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Effective dual-mode fuzzy DMC algorithms with on-line quadratic optimization and guaranteed stability
    Autorzy:
    Marusak, P. M.
    Tatjewski, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/907866.pdf
    Data publikacji:
    2009
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    system nieliniowy
    system rozmyty
    sterowanie predykcyjne
    stabilność
    sterowność wymuszona
    nonlinear system
    fuzzy system
    model predictive control
    stability
    constrained control
    dual-mode control
    Opis:
    Dual-mode fuzzy dynamic matrix control (fuzzy DMC-FDMC) algorithms with guaranteed nominal stability for constrained nonlinear plants are presented. The algorithms join the advantages of fuzzy Takagi-Sugeno modeling and the predictive dual-mode approach in a computationally efficient version. Thus, they can bring an improvement in control quality compared with predictive controllers based on linear models and, at the same time, control performance similar to that obtained using more demanding algorithms with nonlinear optimization. Numerical effectiveness is obtained by using a successive linearization approach resulting in a quadratic programming problem solved on-line at each sampling instant. It is a computationally robust and fast optimization problem, which is important for on-line applications. Stability is achieved by appropriate introduction of dual-mode type stabilization mechanisms, which are simple and easy to implement. The effectiveness of the proposed approach is tested on a control system of a nonlinear plant-a distillation column with basic feedback controllers.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2009, 19, 1; 127-141
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Actuator fault tolerance in control systems with predictive constrained set-point optimizers
    Autorzy:
    Marusak, P. M.
    Tatjewski, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/929879.pdf
    Data publikacji:
    2008
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    sterowanie odporne na błędy
    sterowanie predykcyjne
    optymalizacja
    system nieliniowy
    fault tolerant control
    model predictive control
    set-point optimization
    nonlinear system
    Opis:
    Mechanisms of fault tolerance to actuator faults in a control structure with a predictive constrained set-point optimizer are proposed. The structure considered consists of a basic feedback control layer and a local supervisory set-point optimizer which executes as frequently as the feedback controllers do with the aim to recalculate the set-points both for constraint feasibility and economic performance. The main goal of the presented reconfiguration mechanisms activated in response to an actuator blockade is to continue the operation of the control system with the fault, until it is fixed. This may be even long-term, if additional manipulated variables are available. The mechanisms are relatively simple and consist in the reconfiguration of the model structure and the introduction of appropriate constraints into the optimization problem of the optimizer, thus not affecting the numerical effectiveness. Simulation results of the presented control system for a multivariable plant are provided, illustrating the efficiency of the proposed approach.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2008, 18, 4; 539-551
    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:
    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
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