Disturbance modeling and state estimation for offset-free predictive control with state-space process models
Piotr Tatjewski (2014)
International Journal of Applied Mathematics and Computer Science
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Piotr Tatjewski (2014)
International Journal of Applied Mathematics and Computer Science
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Maciej Ławryńczuk, Piotr Marusak, Piotr Tatjewski (2008)
Control and Cybernetics
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Piotr Tatjewski (2014)
International Journal of Applied Mathematics and Computer Science
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Piotr Tatjewski (2010)
International Journal of Applied Mathematics and Computer Science
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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...
Anna Witkowska, Mirosław Tomera, Roman Smierzchalski (2007)
International Journal of Applied Mathematics and Computer Science
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As an object of course control, the ship is characterised by a nonlinear function describing static manoeuvring characteristics that reflect the steady-state relation between the rudder deflection and the rate of turn of the hull. One of the methods which can be used for designing a nonlinear ship course controller is the backstepping method. It is used here for designing two configurations of nonlinear controllers, which are then applied to ship course control. The parameters of the...
Anna Witkowska, Roman Śmierzchalski (2012)
International Journal of Applied Mathematics and Computer Science
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The article discusses the problem of designing a proper and efficient adaptive course-keeping control system for a seagoing ship based on the adaptive backstepping method. The proposed controller in the design stage takes into account the dynamic properties of the steering gear and the full nonlinear static maneuvering characteristic. The adjustable parameters of the achieved nonlinear control structure were tuned up by using the genetic algorithm in order to optimize the system performance....
Przemysław Orłowski (2007)
International Journal of Applied Mathematics and Computer Science
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Numerical evaluation of the optimal nonlinear robust control requires estimating the impact of parameter uncertainties on the system output. The main goal of the paper is to propose a method for estimating the norm of an output trajectory deviation from the nominal trajectory for nonlinear uncertain, discrete-time systems. The measure of the deviation allows us to evaluate the robustness of any designed controller. The first part of the paper concerns uncertainty modelling for nonlinear...
Andreas Rauh, Saif S. Butt, Harald Aschemann (2013)
International Journal of Applied Mathematics and Computer Science
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The focus of this paper is to develop reliable observer and filtering techniques for finite-dimensional battery models that adequately describe the charging and discharging behaviors. For this purpose, an experimentally validated battery model taken from the literature is extended by a mathematical description that represents parameter variations caused by aging. The corresponding disturbance models account for the fact that neither the state of charge, nor the above-mentioned parameter...
Jacek Czeczot (2007)
Control and Cybernetics
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Maciej Ławryńczuk (2009)
International Journal of Applied Mathematics and Computer Science
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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...
Ramdane Hedjar, Redouane Toumi, Patrick Boucher, Didier Dumur (2005)
International Journal of Applied Mathematics and Computer Science
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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...