Titlebook: Explicit Nonlinear Model Predictive Control; Theory and Applicati Alexandra Grancharova,Tor Arne Johansen Book 2012 Springer-Verlag GmbH Be

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亞當心理陰影

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,Erratum to: Die Wandungen der Eisengef??e,ral multi-parametric nonlinear programming (mp-NLP) problem is formulated and the Karush-Kuhn-Tucker (KKT) optimality conditions are presented. Then, the three main groups of methods to find a local minimum of a NLP problem for a given parameter vector are reviewed (Newton-type methods, penalty func

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oblem must be solved numerically at every sampling interval, while explicit NMPC assumes that an explicit representation of the solution can be computed using multi-parametric nonlinear programming. This chapter considers the . of the optimization problem, which is an essential part of the control d

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H. D. Scheinert,C. Straub,H. Schmitz Programming (mp-NLP) approaches, described in Section?1.1. It is organized as follows. In Section?4.2, general regulation and reference tracking NMPC problems are formulated and represented as an mp-NLP problem. In Section?4.3, it is shown that bounding the approximation error of the approximate ex

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,Einführung und Hinweise zur Benutzung,ed nonlinear systems with quantized control inputs. It is organized as follows. In Section?5.2, general regulation and reference tracking quantized NMPC problems are formulated and represented as an mp-NIP problem. Then, in Section?5.3, an approximate mp-NIP approach to explicit quantized NMPC is de