Data-driven and learning-based control – perspectives and prospects

The use of data to build models for feedback design is a classic topic of systems and control. Indeed, the term system identification can be traced back to the early 1956 work of Lotfi Zadeh [1]. Step and impulse responses remain pivotal concepts in undergraduate control education. Yet, recently, there is a rapidly growing trend towards data-driven and learning-based control in which classic results are revisited and expanded. As a prime example one may consider the 2005 note by Jan C. Willems and co-authors on persistency of excitation [2]. As illustrated in Figure 1, for the first 15 years after its publication this paper did not spark major interest beyond the community working on subspace identification of dynamic systems. Beginning with the understanding that its main result – which is also coined Willems’ fundamental lemma – enables data-driven control of LTI systems, its citation impact bifurcated to exponential growth after 2018. On this canvas, this special issue presents a snapshot of research on data-driven and learning-based methods in the German control community. Inter alia this special issues demonstrates that the ever-growing impact of data-driven and learning-based methods on systems and control goes far beyond Willems’ lemma. This includes papers working also with Koopman operators, neural networks and Gaussian processes. A major commonality of all contributed papers is the consideration of optimization-based approaches. Following the established structure of the journal the articles of this special issue are clustered into two categories – methods and applications.

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600: Technology

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Data-driven and learning-based control – perspectives and prospects