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Titlebook: Nonlinear Model Predictive Control; Towards New Challeng Lalo Magni,Davide Martino Raimondo,Frank Allg?wer Book 2009 Springer-Verlag Berlin

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Gilberto Pin,Thomas Parisiniset points well below their normoxic minima or maintenance levels. This volume will be valuable reading for researchers in physiology, medicine and general biological sciences, and of great importance to pharmaceutical companies researching novel models for stroke and brain ischemia. .978-90-481-6237-6978-0-306-48197-0
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James B. Rawlings,Rishi Amritnatural ecosystems is unsurpassable. The northeast region of India is endowed with enormous natural resources and the huge potential of the Brahmaputra River ba978-90-481-6481-3978-94-017-0540-0Series ISSN 0921-092X Series E-ISSN 1872-4663
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Self-optimizing Robust Nonlinear Model Predictive Controlonvex control Lyapunov functions and disturbance bounds to optimize robustness of the closed-loop system on-line, at each sampling instant - a unique feature in MPC. Moreover, the proposed MPC algorithm is computationally efficient for nonlinear systems that are affine in the control input and it al
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Adaptive Robust MPC: A Minimally-Conservative Approachstabilization in the presence of state or input constraints. By adapting a set-valued measure of the parametric uncertainty within the framework of robust nonlinear-MPC, the results of this paper establish such constrained adaptive stability. Furthermore, it is shown that the ability to account for
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