Learning-Based Adaptive Control: An Extremum Seeking Approach – Theory and Applications

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.

1. Some Mathematical Tools 2. Adaptive Control: An Overview 3. Extremum Seeking-Based Iterative Feedback Gains Tuning Theory 4. Extremum Seeking-Based Indirect Adaptive Control 5. Extremum Seeking-Based Real-Time Parametric Identification for Nonlinear Systems 6. Extremum Seeking-Based Iterative Learning Model Predictive Control (ESILC-MPC)