Learning-Based Adaptive Control

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.