Robust Adaptive Dynamic Programming (IEEE Press Series on Systems Science and Engineering),Used
Robust Adaptive Dynamic Programming (IEEE Press Series on Systems Science and Engineering),Used

Robust Adaptive Dynamic Programming (IEEE Press Series on Systems Science and Engineering),Used

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A comprehensive look at stateoftheart ADP theory and realworld applicationsThis book fills a gap in the literature by providing a theoretical framework for integrating techniques from adaptive dynamic programming (ADP) and modern nonlinear control to address datadriven optimal control design challenges arising from both parametric and dynamic uncertainties.Traditional modelbased approaches leave much to be desired when addressing the challenges posed by the everincreasing complexity of realworld engineering systems. An alternative which has received much interest in recent years are biologicallyinspired approaches, primarily RADP. Despite their growing popularity worldwide, until now books on ADP have focused nearly exclusively on analysis and design, with scant consideration given to how it can be applied to address robustness issues, a new challenge arising from dynamic uncertainties encountered in common engineering problems.Robust Adaptive Dynamic Programming zeros in on the practical concerns of engineers. The authors develop RADP theory from linear systems to partiallylinear, largescale, and completely nonlinear systems. They provide indepth coverage of stateoftheart applications in power systems, supplemented with numerous realworld examples implemented in MATLAB. They also explore fascinating reverse engineering topics, such how ADP theory can be applied to the study of the human brain and cognition. In addition, the book: Covers the latest developments in RADP theory and applications for solving a range of systems complexity problems Explores multiple realworld implementations in power systems with illustrative examples backed up by reusable MATLAB code and Simulink block sets Provides an overview of nonlinear control, machine learning, and dynamic control Features discussions of novel applications for RADP theory, including an entire chapter on how it can be used as a computational mechanism of human movement controlRobust Adaptive Dynamic Programming is both a valuable working resource and an intriguing exploration of contemporary ADP theory and applications for practicing engineers and advanced students in systems theory, control engineering, computer science, and applied mathematics.

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