Evolutionary Machine Learning in Control

This chapter aims to give an overview of recent applications of Evolutionary Machine Learning (EML) to control including opportunities and challenges. Control is at the heart of engineering applications. Examples include regulation, stabilization, reference tracking, synchronization, and coordination. Yet, control design of complex systems may be challenged by high dimensionality, nonlinearities, and delayed responses. A new path for control design is to reformulate the control problem as a regression problem to leverage powerful Machine Learning (ML) methods. In particular, bio-inspired ML methods are well adapted for solving control tasks thanks to easy deployment, interpretabilityInterpretability, and little/no prior knowledge of the system to control needed. Hence, since the 50s, EML methods have been successful in optimizing intelligent controllersController to solve many control tasks, including adaptive, multi-objective, robust control for robotics, electric engineering, and fluid mechanics, to cite a few examples.