Robust Adaptive Dynamic Programming

Robust Adaptive Dynamic Programming zeros in on the practical concerns of engineers. The authors develop robust adaptive dynamic programming (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 and Simulink. 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 blocksets
• 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.