Adaptive control has been one of the main problems studied in control theory.
The subject is well understood, yet it has a very active research frontier.
This book focuses on a specific subclass of adaptive control, namely, learning-based adaptive control.
As systems evolve during time or are exposed to unstructured environments, it is expected that some of their characteristics may change.
This book offers a new perspective about how to deal with these variations.
By merging together Model-Free and Model-Based learning algorithms, the author demonstrates, using a number of mechatronic examples, how the learning process can be shortened and optimal control performance can be reached and maintained.
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