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The notion of what constitutes a robot has evolved considerably over the past five decades, from simple manipulator arms to large networks of interconnected autonomous and semi-autonomous agents. A constant in this evolutionary development has been the central nature of control theory in robotics to enable a vast array of applications in manufacturing automation, field and service robotics, medical robotics and other areas. In this talk we will present an historical perspective of control in robotics together with specific results in passivity-based control and control of underactuated robots. Finally, we will speculate about the future role of control theory in robotics in the era of human-robot interaction, machine learning, and big data analytics.
Distributed robotics refers to the control of, and design methods for, a system of mobile robots that 1) are autonomous, that is, have only sensory inputs---no outside direct commands, 2) have no leader, and 3) are under decentralized control. The subject of distributed robotics burst onto the scene in the late twentieth century and became very popular very quickly. The first problems studied were flocking and rendezvous. The most highly cited IEEE TAC paper in the subject is by Jadbabaie, Lin, and Morse (2003). This lecture gives a classroom-style presentation of the rendezvous problem. It is the most basic coordination task for a network of mobile robots. The robots in the rendezvous problem in the literature are most frequently kinematic points, modeled as simple integrators, dx/dt = u. Of course, a real wheeled robot has dynamics and is nonholonomic, and the first part of the lecture looks at this discrepancy. The second part reviews the solution to the rendezvous problem. The final part of the lecture concerns infinitely many robots. The lecture is aimed at non-experts.
The year 1948 was auspicious for information science and technology. Norbert Wiener's book Cybernetics was published by Wiley, the transistor was invented (and given its name), and Shannon's seminal paper "A Mathematical Theory of Communication" was published in the Bell System Technical Journal. In the years that followed, important ideas of Shannon, Wiener, Von Neumann, Turing and many others changed the way people thought about the basic concepts of control systems. Hendrik Bode himself was a Shannon collaborator in a paper on smoothing and prediction published in the Proceedings of the IRE in 1950. It is thus not surprising that by the time the earliest direct predecessor of CDC (the Discrete Adaptive Processes Symposium) was held in New York in June, 1962, concepts from machine intelligence and information theory were not at all foreign to the control community.
This talk will examine the interwoven evolution of control and information over the past fifty years during which time the IEEE Conference on Decision and Control went from infancy to maturity. The talk will also discuss two new areas in information based control. In collaboration with W.S. Wong, some recent work on control communication complexity has been aimed at a new class of optimal control problems in which distributed agents communicate through the dynamics of a control system in such a way that the control cost is minimized over many messages. Applications of the theory to robot communication through relative motions (e.g. robot dancing and team sports) and to distributed control of semi-classical models of quantum systems will be discussed. The talk will also discuss some recently discovered links between information and the differential topology of smooth random fields (joint work with D. Baronov). The latter work has been applied to a problem of rapid information acquisition in robotic reconnaissance, and it has suggested metrics by which to assess the tradeoff between speed and accuracy.
Fifty years ago, when control was emerging as a scientific discipline fueled by developments in dynamic, recursive decision making, dynamical systems and stability theory, a separate discipline, differential games, was being born in response to the need to develop a framework and associated solution tools for strategic dynamic decision making in adversarial environments, as an outgrowth of game theory. The evolution of the two disciplines-control theory, and particularly optimal control, and the theory of differential games-initially followed somewhat different paths, but soon a healthy interaction between the two developed. Differential games, in both zero-sum and nonzero-sum settings, enabled the embedding of control into a broader picture and framework, and enriched the set of conceptual tools available to it. One of its essential ingredients-information structures (who knows what and when)-became a mainstay in control research, particularly in the context of large-scale systems. In the other direction, the rich set of mathematical tools developed for control, such as viscosity solutions, directly impacted progress in solvability of differential games. Such interactions between the two disciplines reached a climax when robustness became a prevalent issue in control, leading among others to a comprehensive treatment of H¥ control of both linear and nonlinear uncertain systems.
This Bode Lecture will dwell on the parallel developments in the two fields to the extent they influenced each other over the past half century, talk about the present, and embark on a journey into the future.
The input to state stability (ISS) paradigm is motivated as a generalization of classical linear systems concepts under coordinate changes. A summary is provided of the main theoretical results concerning ISS and related notions of input/output stability and detectability. A bibliography is also included, listing extensions, applications, and other current work.
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An understanding of fundamental limitations is an essential element in all engineering. Shannon’s early results on channel capacity have always had center court in signal processing. Strangely, the early results of Bode were not accorded the same attention in control. It was therefore highly appropriate that the IEEE Control Systems Society created the Bode Lecture Award, an honor which also came with the duty of delivering a lecture. Gunter Stein gave the first Hendrik W. Bode Lecture at the IEEE Conference on Decision and Control in Tampa, Florida, in December 1989. In his lecture he focused on Bode’s important observation that there are fundamental limitations on the achievable sensitivity function expressed by Bode’s integral. Gunter has a unique position in the controls community because he combines the insight derived from a large number of industrial applications at Honeywell with long experience as an influential adjunct professor at the Massachusetts Institute of Technology from 1977 to 1996. In his lecture, Gunter also emphasized the importance of the interaction between instability and saturating actuators and the consequences of the fact that control is becoming increasingly mission critical.
After more than 13 years I still remember Gunter’s superb lecture. I also remember comments from young control scientists who had been brought up on state-space theory who said: “I believed that controllability and observability were the only things that mattered.” At Lund University we made Gunter’s lecture a key part of all courses in control system design. Gunter was brought into the classroom via videotapes published by the IEEE Control Systems Society and the written lecture notes. It was a real drawback that the lecture was not available in more archival form. I am therefore delighted that IEEE Control Systems Magazine is publishing this article. I sincerely hope that this will be followed by a DVD version of the videotape. The lecture is like really good wine; it ages superbly.
—Karl J Åström, Professor Emeritus, Lund University, Lund, Sweden (2003)
An article based on Gunter Stein’s Bode Lecture was published in Control Systems Magazine in August 2003 and is available on IEEE Xplore at http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1213600&isnumber=27285.
(This introduction is from the article cited above.)