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008			210921s2022 sz | s |||| 0|eng d
020			_a9783030838157
024	7		_a10.1007/978-3-030-83815-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTJ217.6 _b2022 EB
100	1		_aŁawryńczuk, Maciej _eautor _9685979
245	1	0	_aNonlinear Predictive Control Using Wiener Models : _bComputationally Efficient Approaches for Polynomial and Neural Structures _cby Maciej Ławryńczuk
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XXIII, 343 páginas) _b167 ilustraciones, 121 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aStudies in Systems Decision and Control _x2198-4190 _v389
505	0		_aIntroduction to Model Predictive Control -- MPC Algorithms Using Input-Output Wiener Models -- MPC Algorithms Using State-Space Wiener Models -- Conclusions -- Index.
520			_aThis book presents computationally efficient MPC solutions. The classical model predictive control (MPC) approach to control dynamical systems described by the Wiener model uses an inverse static block to cancel the influence of process nonlinearity. Unfortunately, the model's structure is limited, and it gives poor control quality in the case of an imperfect model and disturbances. An alternative is to use the computationally demanding MPC scheme with on-line nonlinear optimisation repeated at each sampling instant. A linear approximation of the Wiener model or the predicted trajectory is found on-line. As a result, quadratic optimisation tasks are obtained. Furthermore, parameterisation using Laguerre functions is possible to reduce the number of decision variables. Simulation results for ten benchmark processes show that the discussed MPC algorithms lead to excellent control quality. For a neutralisation reactor and a fuel cell, essential advantages of neural Wiener models are demonstrated.
988			_aSpringer_Robotics_2022
650		7	_2embne _9666986 _aSistemas de control no lineal
776	0	8	_iPrinted edition: _z9783030838140
776	0	8	_iPrinted edition: _z9783030838164
776	0	8	_iPrinted edition: _z9783030838171
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-83815-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2023 _dz _eu _zSI