A comprehensive look at state-of-the-art ADP theory and real-world
applications
This 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 data-driven optimal
control design challenges arising from both parametric and dynamic
uncertainties.
Traditional model-based approaches leave much to be desired when
addressing the challenges posed by the ever-increasing complexity of
real-world engineering systems. An alternative which has received much
interest in recent years are biologically-inspired 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
partially-linear, large-scale, and completely nonlinear systems. They
provide in-depth coverage of state-of-the-art applications in power
systems, supplemented with numerous real-world 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 real-world 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 control
Robust 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.
