A review of data-driven control systems design: concepts and methods

Over the past two decades, an increasing number of engineers and researchers in the field of control engineering have shifted their focus towards data-driven methods for system analysis and design. The defining characteristic of these data-driven approaches is their departure from conventional models and their associated assumptions. Instead, these methods harness the wealth of readily available, cost-effective, and reliable data derived from real, complex, and complex adaptive systems. Their primary objective is to facilitate system analysis and control solely through measured data, without relying on explicit or implicit model utilization. In this article, we commence by presenting an overview of the design principles embedded in model-based control systems, with a specific emphasis on adaptive and robust control system design methods. Subsequently, we delve into the fundamental principles of data-driven control system design. To comprehensively examine these data-driven methods, we categorize them into two groups: those grounded in machine learning and soft computing, and those based on traditional control systems analysis and design methods, often referred to as classical methods. Within this article, we initiate with a concise review of machine learning and soft computing-based methods before delving into a more comprehensive exploration of classical methods in this field.