Titlebook: Nonlinear Model Predictive Control; Theory and Algorithm Lars Grüne,Jürgen Pannek Book 20111st edition Springer-Verlag London Limited 2011

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Nonlinear Model Predictive Control,tant reference and continue by formalizing state and control constraints. Viability (or weak forward invariance) of the set of state constraints is introduced and the consequences for the admissibility of the NMPC feedback law are discussed. After having introduced NMPC in a special setting, we desc

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Stability and Suboptimality Without Stabilizing Constraints,g and presenting motivating examples we introduce an asymptotic controllability assumption and give a detailed derivation of stability and performance estimates based on this assumption and the relaxed dynamic programming framework introduced before. We show that our stability criterion is tight for

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Variants and Extensions,me of these introduce new combinations of techniques developed in the previous chapters, others relax some of the previous assumptions in order to obtain more general results or strengthen assumptions in order to derive stronger results. Several sections contain algorithmic ideas which can be added

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Numerical Optimal Control of Nonlinear Systems,ch iterate of the NMPC procedure. To this end, we first discuss standard discretization techniques to obtain a nonlinear optimization problem in standard form. Utilizing this form, we outline basic versions of the two most common solution methods for such problems, that is Sequential Quadratic Progr