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.

- Includes a good number of Mechatronics Examples of the techniques.

- Compares and blends Model-free and Model-based learning algorithms.

- Covers fundamental concepts, state-of-the-art research, necessary tools for modeling, and control.