Data‐driven robust control: Special issue dedicated to the 70th birthday of Michael G. Safonov

Control design of uncertain systemswith few prior assumptions has been recognized as a challenging problem in robust control of widespread importance for a long time. Alongside its advantages, several open questions have surfaced: as current analysis and synthesis methods in robust control are mainly based on prior assumptions given in the form of models, when those models are replaced or enhanced by data, the problem of describing the uncertainty, analyzing robustness, and designing controllers becomes critical. Since themid-1990s, a control paradigm termed data-driven control has been gaining growing attention for both academic research and its practical applicability reasons. Instead of relying on prior assumptions about the plant and uncertainty, fully data-driven control tries to utilize the information extracted from raw measured plant input–output data to guide the selection of controllers in order to improve control system performance. Prior knowledge of the plant, such as assumed plant and disturbance models, are avoided except when used in the design of candidate controllers. This control design paradigm, based on the principle of unfalsification, represents an increasingly promising method for achieving robustness against model mismatch problems that have been known to plague numerous model-based techniques. At the heart of it is the notion of cost detectability—the design of data-driven cost functions, which, in conjunction with the chosen candidate controller set, appropriately represent control performance and stability goals. Dedicated to Professor Michael Safonov on the occasion of his 70th birthday, this Special Issue aims to report current state-of-the-art in data-driven robust control and draw further research attention into different aspects of this methodology. We hope that this sample of relevant papers will form a catalyst that will lead to a more unified understanding of data-driven approach to improving robustness, where models are replaced or supplemented by data. The volume begins with the paper Robust Control — Fooled by Assumptions, by Michael G. Safonov, on the formative scientific principle of unfalsification that Karl Popper identified as the logical basis of the data driven learning process commonly known as the scientific method. The paper presents a tutorial overview of Popper’s scientific unfalsification principle and its implications regarding science-based design of fully data-driven adaptive learning control algorithms. The next paper by Sung, Patil, and Safonov develops a data-driven, real-time test for candidate controllers’ ability to meet given McFarlane-Glover loop-shape specifications, as reported in Data-Driven H∞Loop-Shaping Controller Design. Robustness of closed-loop systems is improved by eliminating controllers that have been proven by past collected data to fail loop-shape requirements. Controller-shaping is shown to play a role dual to the role of plant-shaping in the modelbased H∞loop-shaping design procedure. In Direct control design via controller unfalsification, Battistelli, Mari, Selvi, and Tesi propose a non-iterative direct approach for controller design based on experimental data. The considered approach only requires data from a single experiment and does not need initial controller for stable plants even though the plant is non-minimum phase. Sufficient conditions for closed-loop stability are investigated and numerical examples are reported for different case studies. InData-driven fault diagnosis and robust control: Application to PEM fuel cell systems, OcampoMartinez, Sánchez-Peña, Bianchi, and Ingimundarson present fault diagnosis and isolation (FDI) and fault-tolerant control (FTC) systems as applications of data-driven robust control paradigm. Multiple-model indirect adaptive method is combined with a direct unfalsified adaptive hysteresis switching to improve performance and robustness. The method is applied to a polymer electrolyte membrane (PEM) fuel cell’s nonlinear experimentally validated model of the breathing system.