A review of techniques for machine learning of real-time control strategies

Abstract

In this paper, techniques for machine learning of real-time control strategies are presented and reviewed from a control engineer's point of view. The objective is to present a consolidated view, both in the context of classical control theory and modern artificial intelligence practice. The review seeks to present the principal contributions to the field and the impact of these contributions on control engineering, particularly from the machine learning point of view. >