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

Irritate

Explicit Stochastic NMPC,es and/or parameter uncertainties with known probability distributions. In Section 7.2, an approach to explicit solution of . (.) stochastic NMPC problems for constrained nonlinear systems, described by stochastic . models, is considered. The approach constructs a piecewise nonlinear (PWNL) approxim

就职

Explicit NMPC Based on Neural Network Models,by neural network NARX models. The approach builds an orthogonal search tree structure of the . space partition and consists in constructing a piecewise linear (PWL) approximation to the optimal control sequence. A dual-mode control strategy is proposed in order to achieve an offset-free closed-loop

Clumsy

Semi-explicit Distributed NMPC,put constraints, is considered. The approach applies the dynamic dual decomposition method and reformulates the original . NMPC problem into a . quasi-NMPC problem by linearization of the nonlinear system dynamics. The approach is based on .. optimization (by gradient iterations) and can be applied

Watemelon

,Einführung und Hinweise zur Benutzung,utch actuator (described in Section 4.5) is designed and its performance is compared to that of the explicit NMPC with continuous control input. In Section 5.5, the approximate mp-NIP approach is applied to design an explicit quantized NMPC controller for optimal regulation of a continuous stirred tank reactor.

Vasoconstrictor

averse

Explicit MPC of Constrained Nonlinear Systems with Quantized Inputs,utch actuator (described in Section 4.5) is designed and its performance is compared to that of the explicit NMPC with continuous control input. In Section 5.5, the approximate mp-NIP approach is applied to design an explicit quantized NMPC controller for optimal regulation of a continuous stirred tank reactor.

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B-cell

H. D. Scheinert,C. Straub,H. Schmitzem parameters. In control applications such as nonlinear constrained model predictive control this allows efficient online implementation in terms of an explicit piecewise linear state feedback without any real-time optimization.

Perigee

Explicit NMPC Using mp-QP Approximations of mp-NLP,em parameters. In control applications such as nonlinear constrained model predictive control this allows efficient online implementation in terms of an explicit piecewise linear state feedback without any real-time optimization.