03426nmm a2200385 u 4500001001200000003002700012005001700039007002400056008004100080020001800121100002700139245014300166250001700309260006300326300006300389505043100452653002100883653003000904653003100934653001900965653002800984653001801012653002301030653002401053653001301077700003301090041001901123989003601142490005401178028003001232856007201262082001401334082000601348520168601354EB000388933EBX0100000000000000024198600000000000000.0cr|||||||||||||||||||||130626 ||| eng a97836422878001 aGrancharova, Alexandra00aExplicit Nonlinear Model Predictive ControlhElektronische RessourcebTheory and Applicationscby Alexandra Grancharova, Tor Arne Johansen a1st ed. 2012 aBerlin, HeidelbergbSpringer Berlin Heidelbergc2012, 2012 aXIV, 234 p. 66 illus., 17 illus. in colorbonline resource0 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. aNonlinear Optics aApplied Dynamical Systems aControl and Systems Theory aControl theory aSystems Theory, Control aSystem theory aNonlinear theories aControl engineering aDynamics1 aJohansen, Tor Arnee[author]07aeng2ISO 639-2 bSpringeraSpringer eBooks 2005-0 aLecture Notes in Control and Information Sciences50a10.1007/978-3-642-28780-040uhttps://doi.org/10.1007/978-3-642-28780-0?nosfx=yxVerlag3Volltext0 a6,298,3120 a3 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.