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It is widely recognized that many of the most important challenges faced by control engineers involve the development of methods to design and analyze systems having components most naturally described by differential equations interacting with components best modeled using sequential logic. This situation can arise both in the development of high volume, cost sensitive, consumer products and in the design and certification of one of a kind, complex and expensive systems. The response of the control community to this challenge includes work on limited communication control, learning control, control languages, and various efforts on hybrid systems. This work has led to important new ideas but progress has been modest and the more interesting results seem to lack the kind of unity that would lead to a broadly inclusive theory. In this talk we describe an approach to problems of this type based on sample path descriptions of finite state Markov processes and suitable adaptations of known results about linear systems. The result is an insightful design technique yielding finite state controllers for systems governed by differential equations. We illustrate with concrete examples.
Networked embedded sensing and control systems are increasingly becoming ubiquitous in applications from manufacturing, chemical processes and autonomous robotic space, air and ground vehicles, to medicine and biology. They offer significant advantages, but present serious challenges to information processing, communication and decision-making. This area, called cyber-physical systems, which has been brought to the forefront primarily because of advances in technology that make it possible to place computational intelligence out of the control room and in the field, is the latest challenge in systems and control, where our quest for higher degrees of autonomy has brought us, over the centuries, from the ancient water clock to autonomous spacecrafts. Our quest for autonomy leads to consideration of increasingly complex systems with ever more demanding performance specifications, and to mathematical representations beyond time-driven continuous linear and nonlinear systems, to event-driven and to hybrid systems; and to interdisciplinary research in areas at the intersection of control, computer science, networking, driven by application needs in physics, chemistry, biology, finance. After an introduction to some of the main research and education issues we need to address and a brief description of lessons learned in hybrid systems research, we shall discuss recent methodologies we are currently working on to meet stability and performance specifications in networked control systems, which use passivity, model-based control and intermittent feedback control.
A gray-box model is one that has a known structure (generally constrained to a strict subset of the class of models it is drawn from) but has unknown parameters. Such models typically embody or reflect the underlying physical or mechanistic understanding we have about the system, as well as structural features such as the delineation of subsystems and their interconnections. The unknown parameters in the gray-box model then become the focus of our system identification efforts.
In a variety of application domains, ranging from biology and medicine to power systems, the gray-box models that practitioners accept --- as plausible representations of the reality they deal with every day --- have been built up over decades of study, and are large, detailed and complex. In addition to being difficult to simulate or compute or design with, a significant feature of these models is the uncertainty associated with many or most of the parameters in the model. The data that one collects from the associated system is rarely rich enough to allow reliable identification of all these parameters, yet there are good reasons to not be satisfied with direct black-box identification of a reduced-order model. The challenge then is to develop meaningful reduced-order gray-box models that reflect the detailed, hard-won knowledge one has about the system, while being better suited to identification and simulation and control design than the original large model.
Practitioners generally seem to have an intuitive understanding of what aspects of the original model structure, and which variables and parameters, should be retained in a physically or mechanistically meaningful reduced-order model for whatever aspect of the system behavior they are dealing with at a particular time. Can we capture and perhaps improve on what they are doing when they develop their (often informal) reduced models?
This talk will illustrate and elaborate on the above themes. Examples will be presented of approaches and tools that might be used to explore and expose structure in a detailed gray-box model, to guide gray-box reduction.
Most individuals form their opinions about the quality of products, social trends and political issues via their interactions in social and economic networks. While the role of social networks as a conduit for information is as old as humanity, recent social and technological developments, such as Facebook, Blogs and Tweeter, have added further to the complexity of network interactions. Despite the ubiquity of social networks and their importance in communication, we know relatively little about how opinions form and information is transmitted in such networks. For example, does a large social network of individuals holding disperse information aggregate it efficiently? Can falsehoods, misinformation and rumors spread over networks? Do social networks, empowered by our modern communication means, support the wisdom of crowds or their ignorance? Systematic analysis of these questions necessitate a combination of tools and insights from game theory, the study of multiagent systems, and control theory. Game theory is central for studying both the selfish decisions and actions of individuals and the information that they reveal or communicate. Control theory is essential for a holistic study of networks and developing the tools for optimization over networks. In this talk, I report recent work on combining game theoretic and control theoretic approaches to the analysis of social learning over networks.
Pursuit phenomena in nature have a vital role in survival of species. In addition to prey-capture and mating behavior, pursuit phenomena appear to underlie territorial battles in certain species of insects. In this talk we discuss the geometric patterns in certain pursuit and prey capture phenomena in nature, and suggest sensorimotor feedback laws that explain such patterns. Our interest in this problem first arose from the study of a motion camouflage (stealth) hypothesis due to Srinivasan and his collaborators, and an investigation of insect capture behavior in the FM bat Eptesicus fuscus, initiated by Cynthia Moss. Models of interacting particles, developed in collaboration with Eric Justh, prove effective in formulating and deriving biologically plausible steering laws that lead to observed patterns.
The echolocating bat E. fuscus perceives the world around it in the dark, primarily through the information it gathers rapidly and dependably by probing the environment through controlled streams of pulses of frequency modulated ultrasound. The returning echoes from scatterers such as obstacles (cave walls, trees), predators (barn owls) and prey (insects), are captured and transduced into neuronal spike trains by the highly sensitive auditory system of the bat, and processed in the sensorimotor pathways of the brain to steer the bat’s flight in purposeful behavior. In joint work with Kaushik Ghose, Timothy Horiuchi, Eric Justh, Cynthia Moss, and Viswanadha Reddy, we have begun to understand the control systems guiding the flight. The effectiveness of the bat in coping with, attenuation and noise, uncertainty of the environment, and sensorimotor delay, makes it a most interesting model system for engineers concerned with goal-directed and resource-constrained information processing in robotics. The bat’s neural realizations of auditory-motor feedback loops may serve as models for implementations of algorithms in robot designs. While the primary focus of this talk is on pursuit, the results suggest ways to synthesize interaction laws that yield cohesion in collections of particles, treating pursuit as a building block in mechanisms for flocking in nature and in engineered systems.
Over the past decade, game theorists have made substantial progress in identifying simple learning heuristics that lead to equilibrium behavior without making unrealistic demands on agents information or computational abilities, as is the case in the perfect rationality approach to game theory. Recent research shows that very complex, interactive systems can equilibrate even when agents have virtually no knowledge of the environment in which they are embedded. This talk will survey different approaches to the problem of learning in games, show the various senses in which learning rules converge to equilibrium, and sketch the theoretical limits to what is achievable.
The analysis of signals into constituent harmonics and the estimation of their power distribution are considered fundamental to systems engineering. Due to its significance in modeling and identification, spectral analysis is in fact a "hidden technology" in a wide range of application areas, and a variety of sensor technologies, ranging from radar to medical imaging, rely critically upon efficient ways to estimate the power distribution from recorded signals. Robustness and accuracy are of at most importance, yet there is no universal agreement on how these are to be quantified. Thus, in this talk, we will motivate the need for ways to compare power spectral distributions.
Metrics, in any field of scientific endeavor, must relate to physically meaningful properties of the objects under consideration. In this spirit, we will discuss certain natural notions of distance between power spectral densities. These will be motivated by problems in prediction theory and related properties of time-series. Analogies will be drawn with an old subject of a similar vein, that of quantifying distances between probability distributions, which has given rise to information geometry. The contrast and similarities between metrics will be highlighted by analyzing mechanical vibrations, speech, and visual tracking.
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.
More information provided here.
Modern controller design packages often fall short of offering what is truly practical: low order controllers, discrete-time controllers operating in a sampled-data loop, and finite word length realizations of controllers with the FWL property minimally impacting closed-loop performance. The lecture describes how to achieve these objectives.
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) Support Files: 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.)
In this talk, I will discuss the problem of interactive learning by discussing how we can actively learn objective functions from human feedback capturing their preferences. I will then talk about how the value alignment and reward design problem can have solutions beyond active preference-based learning by tapping into the rich context available from large language models. In the second section of the talk, I will more generally talk about the role of large pretrained models in today’s robotics and control systems. Specifically, I will present two viewpoints: 1) pretraining large models for downstream robotics tasks, and 2) finding creative ways of tapping into the rich context of large models to enable more aligned embodied AI agents. For pretraining, I will introduce Voltron, a language-informed visual representation learning approach that leverages language to ground pretrained visual representations for robotics. For leveraging large models, I will talk about a few vignettes about how we can leverage LLMs and VLMs to learn human preferences, allow for grounded social reasoning, or enable teaching humans using corrective feedback. Finally, I will conclude the talk by discussing some preliminary results on how large models can be effective pattern machines that can identify patterns in a token invariant fashion and enable pattern transformation, extrapolation, and even show some evidence of pattern optimization for solving control problems.
