000			03803nam a22004935i 4500
001			978-3-642-28780-0
003			DE-He213
005			20140220083313.0
007			cr nn 008mamaa
008			120321s2012 gw | s |||| 0|eng d
020			_a9783642287800 _9978-3-642-28780-0
024	7		_a10.1007/978-3-642-28780-0 _2doi
050		4	_aTJ212-225
072		7	_aTJFM _2bicssc
072		7	_aTEC004000 _2bisacsh
082	0	4	_a629.8 _223
100	1		_aGrancharova, Alexandra. _eauthor.
245	1	0	_aExplicit Nonlinear Model Predictive Control _h[electronic resource] : _bTheory and Applications / _cby Alexandra Grancharova, Tor Arne Johansen.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2012.
300			_aXIV, 234p. 66 illus., 17 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aLecture Notes in Control and Information Sciences, _x0170-8643 ; _v429
505	0		_aMulti-parametric Programming -- Nonlinear Model Predictive Control -- Explicit NMPC Using mp-QP Approximations of mp-NLP -- Explicit NMPC via Approximate mp-NLP -- Explicit MPC of Constrained Nonlinear Systems with Quantized Inputs -- Explicit Min-Max MPC of Constrained Nonlinear Systems with Bounded Uncertainties -- Explicit Stochastic NMPC -- Explicit NMPC Based on Neural Network Models -- Semi-Explicit Distributed NMPC.
520			_aNonlinear Model Predictive Control (NMPC) has become the accepted methodology to solve complex control problems related to process industries. The main motivation behind explicit NMPC is that an explicit state feedback law avoids the need for executing a numerical optimization algorithm in real time. The benefits of an explicit solution, in addition to the efficient on-line computations, include also verifiability of the implementation and the possibility to design embedded control systems with low software and hardware complexity. This book considers the multi-parametric Nonlinear Programming (mp-NLP) approaches to explicit approximate NMPC of constrained nonlinear systems, developed by the authors, as well as their applications to various NMPC problem formulations and several case studies. The following types of nonlinear systems are considered, resulting in different NMPC problem formulations: Ø  Nonlinear systems described by first-principles models and nonlinear systems described by black-box models; Ø  Nonlinear systems with continuous control inputs and nonlinear systems with quantized control inputs; Ø  Nonlinear systems without uncertainty and nonlinear systems with uncertainties (polyhedral description of uncertainty and stochastic description of uncertainty); Ø  Nonlinear systems, consisting of interconnected nonlinear sub-systems. The proposed mp-NLP approaches are illustrated with applications to several case studies, which are taken from diverse areas such as automotive mechatronics, compressor control, combustion plant control, reactor control, pH maintaining system control, cart and spring system control, and diving computers.
650		0	_aEngineering.
650		0	_aSystems theory.
650		0	_aPhysics.
650	1	4	_aEngineering.
650	2	4	_aControl.
650	2	4	_aComplexity.
650	2	4	_aSystems Theory, Control.
650	2	4	_aNonlinear Dynamics.
700	1		_aJohansen, Tor Arne. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642287794
830		0	_aLecture Notes in Control and Information Sciences, _x0170-8643 ; _v429
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-642-28780-0
912			_aZDB-2-ENG
999			_c102887 _d102887