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- W2483284042 abstract "This chapter considers the estimation and control of systems with parametric uncertainty. An approach that combines moving-horizon estimation and model predictive control into a single min-max optimization is employed to estimate past and current values of the state, compute a sequence of optimal future control inputs, predict future values of the state, and estimate current values of uncertain parameters. This is done by inclusion of the state, inputs, and uncertain parameters as optimization variables. Learning the true values of the uncertain parameters requires a sufficiently large number of past measurements and that the system is persistently excited. The true values of the uncertain parameters may change over time, and the optimization computes future control inputs that adapt to changing estimates of the uncertain parameters so as to better control the uncertain system. Several linear and nonlinear examples with parametric uncertainty are discussed and effectively controlled with this combined moving-horizon estimation and model predictive control approach." @default.
- W2483284042 created "2016-08-23" @default.
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- W2483284042 date "2016-01-01" @default.
- W2483284042 modified "2023-10-16" @default.
- W2483284042 title "Addressing Adaptation and Learning in the Context of Model Predictive Control With Moving-Horizon Estimation" @default.
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- W2483284042 doi "https://doi.org/10.1016/b978-0-12-805246-4.00006-9" @default.
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