Titlebook: Nonlinear Model Predictive Control; Frank Allg?wer,Alex Zheng Conference proceedings 2000 Springer Basel AG 2000 Differentialgleichungen.S

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Nonlinear Model Predictive Control:Challenges and Opportunitiesve control, estimation, adaptation and robustness but, in addition, there is a daunting challenge: the solution, online, of non-convex optimal control problems. This paper reviews the convergence of methodologies for Nonlinear Model Predictive Control, assesses the challenges arising from uncertaint

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Nonlinear Moving Horizon State Estimationfor a global lower bound for the arrival cost. This result is significant, because, in general, we cannot calculate nor bound the arrival cost. Notable exceptions are linear systems, where the Kalman filter covariance generates a global lower bound for the arrival cost.

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Efficient Solution of Dynamic Optimization and NMPC Problemsstabilize the algorithm to converge from poor starting points. Limitations of these Newton-based methods are also well-known. They experience difficulties in the presence of ill conditioning and extreme nonlinearities. Also, for optimization algorithms, nonconvexity can also lead to a number of diff

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