Origins of Robust Control: Early History and Future Speculations

A revolution in control theory thought happened in the early 1970s when the dominant focus of research shifted from optimality to robustness in response to unexpected failures of optimal control theory to produce feedback control designs capable of tolerating normal differences between design models and reality. The robustness concept has since become such an integral part of present day control theory that it is difficult to imagine that time long ago when the concept lacked a clear mathematical representation and the tools of multivariable robustness analysis were yet to be identified. We shall revisit that time to examine the events that facilitated, and necessitated, this remarkable paradigm shift. Next, looking to the future, we will consider how failures of robust control designs to cope with incorrect uncertainty estimates are beginning to spur control theorists to consider data-driven problem formulations for estimation and control that tacitly question the roles of basic concepts like true model and uncertainty bounds, stochastic noise models and even Bayesian probability. We will discuss how and why Karl Popper's scientific logic of unfalsification seems to be emerging as a central concept in these data-driven problem formulations, and how the unfalsification concept might again shift the focus of mathematical research in the areas of estimation and control.