Titlebook: Nonlinear Industrial Control Systems; Optimal Polynomial S Michael J. Grimble,Paweł Majecki Book 2020 Springer-Verlag London Ltd., part of

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Michael J. Grimble,Paweł Majeckistics and decision theory. It covers both the basic ideas of statistical theory, and also some of the more modern and advanced topics of Bayesian statistics such as complete class theorems, the Stein effect, Bayesian model choice, hierarchical and empirical Bayes modeling, Monte Carlo integration in

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Michael J. Grimble,Paweł Majeckistics and decision theory. It covers both the basic ideas of statistical theory, and also some of the more modern and advanced topics of Bayesian statistics such as complete class theorems, the Stein effect, Bayesian model choice, hierarchical and empirical Bayes modeling, Monte Carlo integration in

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Open-Loop and Feedforward Nonlinear Controlthey provide a useful starting point for considering nonlinear systems. The optimization process is similar to that needed in most chapters but is much simpler in this linear case. The design lessons learned in the following for optimal nonlinear controllers are related to the well-known classical c

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Nonlinear Control Law Design and Implementationm variance control law but the general strategy is applicable to the other optimal control problems considered later. The definition of the criterion to be minimized and the different methods for choosing the dynamic cost-function weightings are considered. The problems in implementing the optimal c

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