Richard E. Bellman Control Heritage Award

First of all: I'm very sorry to not be there in person. Over the last few years I've really missed my control colleagues from around the world.

I'm very honored and grateful to receive the Richard E. Bellman award. Bellman is one of the most frequently occurring names in my papers and books. He's right behind Gauss and Lyapunov, so he's in very good company. Bellman is also one of my heros, for formulating sequential decision making, also known as control, in such a clear way, as well as developing an elegant solution method that is often practical.

On the other hand, it's a bit scary to receive an award with the word 'heritage' in it. Is there a hidden message there? Like, maybe it's time for me to retire?

I've outlined my trajectory from pure math to EECS and control before, so I'll skip right to the many thanks I need to give.

My first thanks are to Charlie Desoer, Leon Chua, Shankar Sastry, Pravin Varaiya, and Alberto Sangiovanni. They are why I transferred from Math to EECS at Berkeley more than 40 years ago. I've never regretted that move. At the same time, I'm very happy that my initial training was in pure math, with Andy Gleason.

I thank all of my former and current PhD students, post-docs, research visitors, and research collaborators and co-authors. 'Thanks' really isn't the right word; these are the people who did the work. I'm proud of what we've done and created. *I* had a lot of fun, and I suspect my research collaborators mostly did too. (Maybe not when we were going over the 10th revision of a paper.) Special thanks to my collaborator Lieven Vandenberghe, with whom I've written two books, with another one in the oven.

I want to thank the students in my classes. My classes are big, and I've been teaching for almost 40 years now, so there are a lot of them. The students come from a wide range of fields and backgrounds. They were the training and test sets for explaining ideas. And they suggested plenty of new and interesting ideas. I have learned a lot from them.

I am very grateful to my colleagues in EE at Stanford. I really like my colleagues. And that's after serving as department chair. (You have to have been chair to fully understand that.) They are awesome, and I'm proud to be their colleague.

I are grateful for my colleagues in control and optimization
across the world. Control is a great field, which I fell into at Berkeley during my PhD, under the influence of Charlie Desoer and Shankar Sastry. Control is my intellectual home, it’s where I grew up, and where I’ve made a huge number of very good life-long friends. I’m really proud to be a part of this community.

Thanks also to my optimization colleagues, especially Arkadi Nemirovsky and Yuri Nesterov. And super special thanks to Boris Polyak, who was, like Bellman, a hero of mine.

I'm also very grateful to have spent time in industry, in multiple areas. I've learned an enormous amount from my industry friends, especially how someone with industry knowledge and experience, along with some good street-fighting skills, can do very well in a practical setting.

And finally, family. It has been a fantastic adventure, and I could not be more grateful to have shared it with Anna, my wife of more than 35 years, and our two great 'kids' Nick and Nora, now in their early 30s.

Thank you all again.

Dear Automatic Control colleagues,
I am happy and humbled to receive the Bellman Award.

My profound gratitude goes to the colleagues who supported my nomination. I am thankful and deeply moved by the selection committee and the A2C2, which advanced a candidate in his mid-fifties, an adolescent by Bellman award standards.

The timing of this award, which recognizes the achievement of an American control systems researcher, carries significance for me. The Bellman award came in the year that happened to be the thirtieth anniversary of my coming to the United States as a graduate student.

It is customary on this occasion for the recipient to say a few words about their formative years and professional trajectory.

I was born and grew up in a small city called Pirot, in remote southeastern Serbia. I was fortunate that my provincial city had one of the top science high schools in former Yugoslavia. And my caring parents spared no expense to provide my brother and me with broader cultural opportunities than those that our hometown could offer.

My undergraduate years at the Department of Electrical Engineering of the University of Belgrade provided me with two things. First, the toughest academic competition I’ve experienced, before or since, was during those five undergraduate years. And, second, I met my future wife in our freshman math class.

Before Petar Kokotovic gave me a PhD opportunity, I had only an inkling that I might have a shot at some success in research. But, within a few weeks of arriving in Santa Barbara, I had the fortune of solving a problem that had a reputation of being unsolvable, though I didn’t know that. So things moved quickly with research from that point on, and I had Petar’s unlimited attention. I could fill hours on being mentored by Petar. But let me just say that, during those Santa Barbara years, Petar’s enthusiasm and support for my work left me feeling that there was nothing more important happening in the world than what I was doing in research. At the same time, with everything I would produce or say, I had the training benefit of a keener, more unforgiving, and yet more nuanced critique than I would ever subsequently encounter, as a researcher or academic administrator.

Of the areas credited to me, the ones that probably come to mind first are PDE backstepping and extremum seeking. Let me describe how these interests started, soon after I left Santa Barbara.

Petar Kokotovic, Richard Murray, and Art Krener had a large project on controlling flow instabilities in jet engines. We solved those problems using reduced-order nonlinear ODE models of those flows. And it was clear that, for a nonlinear control researcher, there was hardly a more fertile ground than fluids. The only problem was: who would provide an ODE reduction for me for the next control design problem I tackle? If fluids people spend their entire careers refining, for a specific type of flow, the reductions from the Navier-Stokes representation to ODEs, it was obvious I could not count on them for control-oriented reduced models. I had to roll up my sleeves and build control methods directly for PDEs. From scratch. Because Riccati equations—in infinite dimension to boot—are not the way to extend PDE control to the nonlinear case. The answer to the challenge of constructive PDE control came in the form of continuum backstepping transformations, employing Volterra operators and easy-to-solve Goursat-form PDEs for the control gain functions. If you have interest in an example of this line of PDE control research, I recommend the paper with Coron, Bastin, and my student Vazquez, which has enabled stabilization of traffic flows in a congested, stop-and-go regime.

How I got drawn to extremum seeking is also interesting. In 1997, a combustion colleague at Maryland pointed me to publications from the 1940s and 1950s on what I would describe as an approach to adaptive control for nonlinear systems. Heuristic, but orders of magnitude simpler than what I had written my PhD on. Attempts at sleep were futile, for several days, until I figured out how to prove stability of this algorithm, using a combination of averaging and singular perturbation theorems. If you wanted to sample one control paper from the last quarter century on extremum seeking, I recommend the one on model-free seeking of Nash equilibria with Tamer Basar and my student Paul Frihauf.
To my students and collaborators, I would like to say: this Bellman award is yours. For your papers, books, theorems, and industrial products.

As I mention students, I want to extend gratitude to two companies that have been the environments in which my former students have been able to thrive and leave a legacy. At ASML, control of extreme ultraviolet photolithography has improved the density of microchips by 2-3 orders of magnitude. At General Atomics, control of aircraft arrestment on carriers has enabled one of the most impressive and deployed recent advances in defense technology.

I won’t pretend that it is not a delight to see my name in the list of the 44 recipients of the Bellman award. Scholars of incredible depth and engineers of stunning impact. I’ve studied the list. Amazingly, the numbers of American-born and foreign-born recipients of this US award seem to be the same: 22 each. If you sought an example of how the US is unequaled in extending opportunity to scientific immigrants, like myself, you could hardly find a clearer illustration.

It was also impossible for me to miss in the list that, after India, represented by four Bellman awardees, the second most highly represented foreign country is a certain little country, just a few percent more populous than the city of Atlanta, the country from which Petar Kokotovic, Drago Šiljak, and I came to the US. If I don’t mention this, in the hope of inspiring a few young minds at the Universities of Belgrade, Novi Sad, or Niš, who should?

I couldn’t have made it here without role models and without pioneers who charted the pathways along which it was then not that hard for me to walk. Among them are people who have also generously supported me over the years: Tamer Basar, Manfred Morari, Art Krener, Eduardo Sontag, Masayoshi Tomizuka, Galip Ulsoy, Jason Speyer, Graham Goodwin, Jean-Michel Coron, Petros Ioannou—to limit myself to ten. I hope that, in the remainder of my research career, I more fully deserve their support, as well as by other friends I don’t mention here but who are aware of the extent of my gratitude and respect.

Let me close and thank you with a quote from my former department chair who astutely observed: “To you guys, in control systems, every other field is a special case of control theory.” What if that’s true?

June 7, 2022
Atlanta, GA USA
ACC 2022

Dear President Braatz, colleagues, students and friends.
I am very grateful and indeed humbled by being honored to receive the Richard E. Bellman Control Heritage Award for 2019 and to join the distinguished list of prior recipients. I wish to express my sincerest thanks to those who nominated me and supported my nomination and to the awards committee. I am deeply moved by the honor I receive today.

More as a rule than an exception, such an honor is not a credit to a single individual but rather the result of collective work and many collaborations over the years. This is particularly true in areas which are by nature interdisciplinary. And control theory, as such, is one of these. It offers an excellent example of synergy where purely theoretical questions, mathematical in nature, are prompted and stimulated by technological advances and engineering design.

I was attracted to mathematical control theory from my early days at the University of Warsaw, where I was privileged to join a distinct and (at that time) experimental program, called Studies in Applied Mathematics. This was an interdisciplinary initiative under the collaboration of a few home departments. After graduating with a Master Degree, I was fortunate to receive a doctoral fellowship which allowed me to complete my PhD in Applied Mathematics-Control Theory within 3 years, with a thesis on a problem of non-smooth optimization, which extended Milutin-Dubovitski's work and had applications to control systems with delays.

I am extremely grateful to my mentors of that time: Professors A. Wierzbicki, A. Manitius from Control Theory [the latter now chair at George Mason University], the late Professor S. Rolewicz and Professor K. Malanowski both from the Polish Academy of Sciences. They, along with other colleagues, gave me an opportunity to embrace a large spectrum of the field of control theory, to include functional analysis, abstract optimization, differential equations.

My further education took a critical turn at UCLA in Los Angeles, which I joined in 1978, at the invitation of the late Professor A.V. Balakrishnan, the 2001 recipient of the Bellman's award. Bal for all of us. Here, under his mentorship, I was offered the challenge to get involved in the mathematical area of boundary control theory for Distributed Parameter Systems, still at its infancy at that time, even from the viewpoint of Partial Differential Equations, with many basic mathematical problems still open. That was about the time when Richard Bellman's book on Dynamic Programming appeared, in 1977, rooted on Bellman's equation and the Optimality Principle. I always looked at Bellman as a problem-solving mathematician, and the mathematical theory of boundary control of DPS is in line with this philosophy.

Controlling or observing an evolution equation from a restricted set [such as the boundary of a multi-dimensional bounded domain where the controlled system evolves] is both a mathematical challenge and a technological necessity within the realm of practical and physically implementable control theory. Most often, the interior of the domain is not accessible to external manipulations. One first goal of the time within the DPS control community was to construct an appropriate control theory, inspired also by the late R. Kalman, the 1997 recipient of the Bellman's award. Main initial contributors were J.L. Lions, A. Bensoussan and their influential school in Paris, and A.V. Balakrishnan and his associates. But DPS come in a large variety. It requires that each distinct class (parabolic, hyperbolic, etc.) be studied on its own with properties and methods pertinent to it, which however fail for other classes. The systematic study of boundary control, which leads to distributional calculus for various distinct classes of physically significant DPS, became the first long-range object of my research. Both, the results and the methods are dynamics dependent. Finite or infinite speed of propagation becomes an essential feature in controllability theory. For instance, the wave equation is boundary exactly controllable on a sufficiently large time, while the heat equation is only null-controllable yet on an arbitrary short time. Existence, uniqueness and robustness of solutions to nonlinear dynamics were just the first questions asked but still open within the existing PDE culture.

Topics investigated over the years included: optimal control, Riccati and H-J-Bellman theory and their numerical implementation, appropriate controllability and stabilization notions, all in the framework of boundary control of partially observed systems. This research effort, which continues to this very day, was conducted with collaborators and PhD students. It started with my association with A.V. Balakrishnan at UCLA, J.L. Lions at College de France and R. Kalman during my 7 years at the University of Florida. And it continued during my subsequent 26 years at the University of Virginia, the home of MacShane, and now at the University of Memphis. In both cases with talented PhD students. Some of these occupy now distinguished positions in the US academia.

Once the control theory of single distinct DPS classes became mature, engineering applications motivated the need to move on toward the study of more complex DPS consisting of interactive structures where different types of dynamics coupled at an interface define a given control system. Propagation of control properties through the interface then plays a main role.

Thus, in its second phase, my research in DPS then evolved toward these coupled interactive systems of several PDEs. Applications include large flexible structures, structural acoustic interaction, fluid-structure interaction, attenuation of turbulence in fluid dynamics [Navier Stokes] and flutter suppression in nonlinear aero-elasticity. In the latter area, my collaboration with Earl Dowell [Duke Univ.] was most enlightening, and is a further proof of the interdisciplinary nature of the field. These problems, while deeply rooted in engineering control technology, were also benchmark models at the forefront of developing a PDE-based mathematical control theory, which accounts for the infinite dimensional nature of continuum mechanics and fluid dynamics.

In closing, I would like to acknowledge with gratitude my personal and professional interaction over the years with people such as the late David Russell [VPI], Walter Littmann [U of Minnesota], Giuseppe Da Prato [Scuola Normale, Pisa], Michel Delfour [Univ. of Montreal] and Sanjoy Mitter [MIT], the latter the 2007 recipient of the Bellman award. Their pioneering works paved the way to further developments along a road-map which I am proud to be a part of.

Special thanks to my long-time collaborator and husband Roberto Triggiani, to the late Igor Chueshov [both co-authors of major research monographs, two with Roberto in Cambridge University Press and one with Igor in Monograph Series of Springer], as well as to my former students, now collaborators and colleagues.

Many thanks also to funding agencies such as NSF, AFOSR, ARO and NASA for many years of generous support.
July 11, 2019.
Philadelphia

Dear President Masada, colleagues, students, ladies and gentlemen:

I am deeply moved by this award and honor, and truly humbled to join a group of such stellar members of our extended systems and control community, several of whom have been my mentors, teachers and role models throughout my career.

I am grateful to those who nominated me and supported my nomination and to the selection committee for their decision to honor my work and accomplishments.

I was fortunate through my entire life to receive the benefits of exceptional education. From special and highly selective elementary school and high school back in Greece, to the National Technical University of Athens for my undergraduate studies and finally to Harvard University for my graduate studies. My sincere and deep appreciation for such an education goes to my parents who distilled in me a rigorous work ethic and the ambition to excel, my teachers in Greece for the sound education and training in basic and fundamental science and engineering and to my teachers and mentors at Harvard and MIT (Roger Brockett, Sanjoy Mitter and the late Jan Willems) and the incredibly stimulating environment in Cambridge in the early 70’s.

Many thanks are also due to my students and colleagues at the University of Maryland, in the US and around the world, and in particular in Sweden and Germany, for their collaboration, constructive criticism and influence through the years. Several are here and I would like to sincerely thank you all very much.
I am grateful to the agencies that supported my research: NSF, ARO, ARL, ONR, NRL, AFOSR, NIST, DARPA, NASA. I am particularly grateful to NSF for the support that helped us establish the Institute for Systems Research (ISR) at the University of Maryland in 1985, and to NASA for the support that helped us establish the Maryland Center for Hybrid Networks (HyNet) in 1992.

I would also like to thank many industry leaders and engineers for their advice, support, and collaboration during the establishment and development of both the ISR and HyNet to the renowned centers of excellence they are today.

Most importantly I am grateful to my wife Mary, my partner, advisor and supporter, for her love and selfless support and sacrifices during my entire career.

When I came to the US in 1970 I was debating whether to pursue a career in Mathematics, Physics or Engineering. The Harvard-MIT exceptional environment allowed me freedom of choice. Thanks to Roger Brockett I was convinced that systems and control, our field, would be the best choice as I could pursue all of the above. It has indeed proven to be a most exciting and satisfying choice. But there were important adjustments that I had to make and lessons I learned.

I did my PhD thesis work on infinite dimensional realization theory, and worked extensively with complex variable methods, Hardy function algebras, the famous Carleson corona theorem and several other rather esoteric math. From my early work at the Naval Research Laboratory in Electronic Warfare (the “cross-eye” system) and in urban traffic control (adaptive control of queues) I learned, the hard way, the difficulty and critical importance of building appropriate models and turning initially amorphous problems to models amenable to systems and control thinking and methods. I learned the importance of judiciously blending data-based and model-based techniques.

In the seventies, I took a successful excursion into detection, estimation and filtering with quantum mechanical models, inspired by deep space laser communication problems, where my mathematical physics training at Harvard came in handy. I then worked on nonlinear filtering, trying to understand how physicists turned nonlinear inference problems to linear ones and investigate why we could not do the same for nonlinear filtering and partially observed stochastic control. This led me to unnormalized conditional densities, the Duncan-Mortensen-Zakai equation and to information states. This led me naturally to construct nonlinear observers as asymptotic limits of nonlinear filtering problems and the complete solution of the nonlinear robust output feedback control problem (nonlinear H-infinity problem) via two coupled Hamilton Jacobi Bellman equations. We even investigated the development of special chips to implement real-time solutions, a topic we are revisiting currently.

With the development and progress of the ISR I worked on many problems including: speech and image compression breaking the Shannon separation of source and channel coding, manufacturing processes, network management, communication network protocols, smart materials (piezoelectric, shape memory alloys), mobile wireless network design, network security and trust, and more recently human-machine perception and cognition, networked control systems, networked cyber-physical systems, combining metric temporal logic and reachability analysis for safety, collaborative decision management in autonomous vehicles and teams of humans and robots, new analytics for learning and for the design of deep learning networks mapping abstractions of the brain cortex, quantum control and computing.

Why I am telling you about all these diverse topics? Not to attract your admiration. But because at the heart of all my works are fundamental principles and methods from systems and controls, often appropriately extended and modified. Even in my highest impact (economic and social) work in conceiving, demonstrating and commercializing Internet over satellite services (with billions of sales world-wide – remember me when you use Internet in planes over oceans), we modified the flow control algorithm (the TCP) and the physical path, to avoid having TCP interpret the satellite physical path delay as congestion. That is we used systems and control principles.

Our science and engineering, systems and control, has some unparalleled unifying power and efficiency. That is, if we are willing to build the new models required by the new applications (especially models requiring a combination of multiple physics and cyber logic) and if we are willing to learn and apply the incredible new capabilities and technologies that are developed in information technology and materials. As is apparent especially in this conference (ACC), and in the CDC conference, by any measure, our field is exceptionally alive and well and continues to surprise many other disciplines by its contributions and accomplishments, which now extend even in biology, medicine and healthcare. So for the many young people here, please continue the excitement, continue getting involved in challenging and high impact problems, and continue the long tradition and record of accomplishments we have established for so many years. And most importantly continue seeking the common ground and unification of our methods and models.

Let me close with what I consider some major challenges and promising broad areas for the next 10 years or so:

1) Considering networked control systems we need to understand what we mean by a “network” and the various abstractions and system aspects involved. Clearly there are more than one dynamic graphs involved. This needs new foundations for control, communication, information, computing.

2) Systems and control scientists and engineers are the best qualified to develop further the modern field of Model-Based Systems Engineering (MBSE): the design, manufacturing/implementation and operation of complex systems with heterogeneous physical, cyber components and even including humans.

3) The need for analog computing is back, for example in real-time and progressive learning and in CPS. Some of the very early successes of control were implemented in analog electromechanical systems due to the need for real-time behavior. Yet we do not have a synthesis theory and methodology for such systems due to the heterogeneous physics that may be involved. Nothing like we have for VLSI.

Thank you all very much! This is indeed a very special day for me!

When I look back upon my career in the field of control, I think it may have started in 1957, when Sputnik was launched by the Russians. I was in the seventh grade at that time. The reaction of our local school board to losing the space race was to have a group of students take algebra one year earlier, in the eighth grade. During high school, I participated in my class science fairs and won at the state level. When I was a freshman at the University of Kansas in 1967, I was given the ability to do independent research in the area of nucleate boiling. I also was exposed to computer programming, which was a fairly new topic at that time in undergraduate engineering. I became interested in numerical analysis and selected Princeton University for doctoral study, because Professor Leon Lapidus was a leading authority on that topic.

I discovered his interest in numerical analysis was driven by solving control problems (specifically two point boundary value problems). The optimal control project I selected was on singular bang-bang and minimum time control. I used discrete dynamic programming with penalty functions (influenced by Bellman and Kalman) as a way to solve this particular class of control problems. In 1971 I accepted a faculty position at the University of Texas.

That era was the heyday of optimal control in the aerospace program. Many of us in chemical engineering wanted to apply these ideas to chemical plants, however, there were some obstacles. Economic justification was strictly required for any commercial application vs. government funding for space vehicles. In addition, proprietary considerations prevented technology transfer from one plant to another. It wasn't until the late 1970s, when Honeywell introduced the distributed digital control system, that computer process control really began to become more popular (and economic) in industry. In 1972, I purchased a Data General minicomputer to be used with a distillation column for process control. That computer was very antiquated by today’s standards; in fact, we had to use paper tape for inputting software instructions to the machine.

Given that there was a lack of industrial receptivity to advanced control research and NSF funding was very limited, I looked around for other types of problems where my skills might be valuable. In 1974 the energy crisis was rearing its head due to the Arab oil embargo. Funding agencies like NSF and the Office of Coal Research in the U.S. were quite interested in how we could use the large domestic resource of coal to meet the shortage of oil and gas. I came across some literature about a technology called underground coal gasification (UCG), where one would gasify the coal resource in situ as a way of avoiding the mining step. I recall reading it was a very promising technology but they didn't know how to control it. That sparked my interest as a possible topic where I could apply my skill set. But I first had to learn about the long history of coal gasification and coal utilization in general.

There were many issues that had to be addressed before developing control methodologies for UCG. There was a need to develop three-dimensional modeling tools that would predict the recovery of the coal as well as the gas composition that you make (similar to a chemical reactor). Thus 80% of the research work was on modeling as opposed to control. It was also a highly multidisciplinary project involving rock mechanics and environmental considerations. I worked in this area for about 10 years. Later in the mid-1980s, the U.S. no longer had an energy crisis, so I started looking at some other possible areas for application of modeling and control.

In 1984 a new senior faculty member joined my department from Texas Instruments. He was very familiar with semiconductor manufacturing and the lack of process control, and he was able to teach me a lot about that industry. Fortunately I did not have to learn a new field on my own since I was Department Chair with limited discretionary time. The same issues were present as for UCG: models were needed in order to develop control strategies. I have continued working in that area with over 20 graduate students spread out over the past 25 years and process control is now a mature technology in semiconductor manufacturing (see my plenary talk at this year’s ACC).

During the 1980s, I became interested in textbook writing and particularly the need to develop a new textbook in process control. I began collaborating with two colleagues at UC Santa Barbara (Dale Seborg and Duncan Mellichamp) and thought that UCSB would be a great place to spend some time in the summer writing and giving short courses on the topic. The course notes were eventually developed into a textbook eight years later. We now are working on the fourth edition of the book and it is the leading textbook for process control in the world. It has been a very rewarding endeavor to work with other educators, and I would recommend that anyone writing a textbook collaborate with other co-authors as a way of improving the product. In 2010, we added a fourth co-author (Frank Doyle) to cover biosystems control; in fact, he is receiving the practice award from AACC today.

In the early 1990s at UT Austin, Jim Rawlings and I concluded that we wanted to work on control problems that would impact industrial practice rather than just writing more technical papers that maybe only a few people would read. So we formed the Texas Modeling and Control Consortium (TMCC) which had 16 member companies. Over twenty plus years the consortium has morphed into one involving multiple universities investigating process control, monitoring, optimization, and modeling. When Jim left the University of Texas and went to Wisconsin, we decided to keep the consortium going, so it became TWMCC (Texas Wisconsin Modeling and Control Consortium). Joe Qin replaced Jim on the faculty at UT but then 10 years later he left for USC. So our consortium became TWCCC (Texas Wisconsin California Control Consortium). I have learned a lot from both Joe and Jim over the years and have been able to mentor them in their professional development as faculty members. I am now mentoring a new UT control researcher (Michael Baldea) as we continue to close the gap between theory and practice.

One other thing I should mention is my involvement with the American Control Conference. I first gave a paper in 1972 at what was known as the Joint Automatic Control Conference (JACC) and have been coming to this meeting ever since. In the 1970s each meeting was entirely run by a different society each year. To improve the business model and instill more interdisciplinarity with five participating societies, in 1982 we started the American Control Conference with leadership from Mike Rabins, John

Zaborsky, and also Bill Levine who is here today. I was Treasurer of the 1982 meeting, which was held in Arlington, VA. That began an extremely successful series of meetings that is one of the best conference values today. It is very beneficial to attend to see control research carried out in the other societies and not just your own society.

During my 40+ year career, I have had a lot of help from colleagues in academia and industry and collaborated with over 100 bright graduate students. I also should thank my wife Donna, who has put up with me over these many years since we first started going to the computer center at the University of Kansas for dates 50 years ago.

My advice to younger researchers is to think 10 years out as to what the new areas might be and start learning about them. Fortunately, today’s control technology is more ubiquitous than ever and the future is bright, although the path forward may not be clear. I still remember a discussion I had with a fellow graduate student before leaving Princeton in 1971 as we embarked on academic careers. His view was that after all the great things achieved by luminaries like Pontryagin, Bellman, and Kalman, all that's really left are the crumbs… So I guess that means that I must have had a pretty crummy career.

President Rhinehart, Lucy, Danny, fellow members of the greatest technological field in the world, I am to, say the least, absolutely thrilled and profoundly humbled to be this year’s recipient of the Richard E. Bellman Control Heritage Award. I am grateful to those who supported my nomination, as well to the American Automatic Control Council for selecting me.

I am indebted to a great many people who have helped me throughout my career. Among these are my graduate students, post docs, and colleagues including in recent years, John Baillieul, Roger Brockett, Bruce Francis, Art Krener, and Jan Willems. In addition, I‘ve been fortunate enough to have had the opportunity to collaborate with some truly great people including Brian Anderson, Ali Bellabas, Chris Byrnes, Alberto Isidori, Petar Kokotovic, Eduardo Sontag and Murray Wonham. I‘ve been lucky enough to have had a steady stream of research support from a combination of agencies including AFOSR, ARO and NSF.

I actually never met Richard Bellman, but I certainly was exposed to much of his work. While I was still a graduate student at Purdue, I learned all about Dynamic Programming, Bellman’s Equation, and that the Principle of Optimality meant “Don’t cry over spilled milk.” Then I found out about the Curse of Dimensionally. After finishing school, I discovered that there was life before dynamic programming, even in Bellman ‘s world. In particular I read Bellman ‘s 1953 monograph on the Stability Theory of Differential Equations. I was struck by this book ‘s clarity and ease of understanding which of course are hallmarks of Richard Bellman ‘s writings. It was from this stability book that I first learned about what Bellman called his “fundamental lemma.” Bellman used this important lemma to study the stability of perturbed differential equations which are nominally stable. Bellman first derived the lemma in 1943, apparently without knowing that essentially the same result had been derived by Thomas Gronwall in 1919 for establishing the uniqueness of solutions to smooth differential equations. Not many years after learning about what is now known as the Bellman - Gronwall Lemma, I found myself faced with the problem of trying to prove that the continuous time version of the Egardt - Goodwin - Ramadge - Caines discrete-time model reference adaptive control system was “stable.” As luck would have it, I had the Bellman - Gronwall Lemma in my hip pocket and was able to use it to easily settle the question. As Pasteur one said, “Luck favors the prepared mind.”

After leaving school I joined the Office of Control Theory and Application at the now defunct NASA Electronics Research Center in Cambridge, Mass. OCTA had just been formed and was headed by Hugo Schuck. OCTA ‘s charter was to bridge the gap between theory and application. Yes, people agonized about the so-called theory - application gap way back then. One has to wonder if the agony was worth it. Somehow the gap, if it really exists, has not prevented the field from bringing to fruition a huge number of technological advances and achievements including landing on the moon, cruise control, minimally invasive robotic surgery, advanced agricultural equipment, anti-lock brakes, and a great deal more. What gap? The only gap I know about sells clothes.

In the late 1990s I found myself one day listening to lots of talks about UAVs at a contractor’s meeting at the Naval Post Graduate School in Monterey Bay, California. I had a Saturday night layover and so I spent Saturday, by myself, going to the Monterey Bay 1 Aquarium. I was totally awed by the massive fish tank display there and in particular by how a school of sardines could so gracefully move through the tank, sometimes bifurcating and then merging to avoid larger fish. With UAVs in the back of my mind, I had an idea: Why not write a proposal on coordinated motion and cooperative control for the NSF ‘s new initiative on Knowledge and Distributed Intelligence? Acting I this, I was fortunate to be able to recruit a dream team: Roger Brockett, for his background in nonlinear systems; Naomi Leonard for her knowledge of underwater gliders; Peter Belhumeur for his expertise in computer vision, and biologists Danny Grunbaum and Julia Parish for their vast knowledge of fish schooling. We submitted a proposal aimed at trying to understand on the one hand, the traffic rules which large animal aggregations such as fish schools and bird flocks use to coordinate their motions and on the other, how one might use similar concepts to coordinate the motion of manmade groups. The proposal was funded and at the time the research began in 2000, the playing field was almost empty. The project produced several pieces of work about which I am especially proud. One made a connection between the problem of maintaining a robot formation and the classical idea of a rigid framework; an offshoot of this was the application of graph rigidity theory to the problem of localizing a large, distributed network of sensors. Another thrust started when my physics - trained graduate student Jie Lin, ran across a paper in Physical Review Letter by Tomas Vicsek and co-authors which provided experimental justification for why a group of self - driven particles might end up moving in the same direction as a result of local interactions. Jie Lin, my post doc Ali Jadbabaie, and I set out to explain the observed phenomenon, but were initially thwarted by what seemed to be an intractable convergence question for time - varying, discrete - time, linear systems. All attempts to address the problem using standard tools such as quadratic Lyapunov functions failed. Finally, Ali ran across a theorem by Jacob Wolfowitz, and with the help of Marc Artzrouni at the University of Pau in France, a convergence proof was obtained. We immediately wrote a paper and submitted it to a well-known physics journal where it was promptly rejected because the reviewers did not like theorems and lemmas. We then submitted a full-length version of the work to the TAC where it was eventually published as the paper “Coordination of Groups of Mobile Autonomous Agents Using Nearest Neighbor Rules.”

Over the years, many things have changed. The American Control Conference was once the Joint Automatic Control Conference and was held at universities. Today the ACC proceedings sits on a tiny flash drive about the size of two pieces of bubble gum while a mere 15 years ago the proceedings consisted of 6 bound volumes weighing about 10 pounds and taking up approximately 1100 cubic inches of space on one ‘s bookshelf. And people carried those proceedings home on planes - of course there were no checked baggage fees back then.

The field of automatic control itself has undergone enormous and healthy changes. When I was a student, problem formulations typically began with “Consider the system described by the differential equation.” Today things are different and one of the most obvious changes is that problem formulations often include not only a differential equations, but also graphs and networks. The field has broadened its outlook considerably as this American Control Conference clearly demonstrates.

And where might things be going in the future? Take a look at the “Impact of Control Technology” papers on the CSS website including the nice article about cyber - physical systems by Kishan Baheti and Helen Gill. Or try to attend the workshop on “Future Directions in Control Theory” which Fariba Fahroo is organizing for AFOSR.

Automatic control is a really great field and I love it. However, it is also probably the most difficult field to explain to non - specialists. Paraphrasing Donald Knuth: “A {control} algorithm will have to be seen to be believed.”

I believe that most people do not understand what a control engineer does or what a control system is. This of course is not an unusual situation. But it is a problem. IBM, now largely a service company, faced a similar problem trying to explain itself after it stopped producing laptops. We of course are primarily a service field. Perhaps like IBM, we need to take some time to rethink how we should explain what we do?

Thank you very much for listening and enjoy the rest of the conference.

Usually when you are nominated for an award you know about it or – at least – you have a suspicion – for example, when somebody asks you for your CV, but you are sure that they are not interested in hiring you. This award came to me as a total surprise. Indeed, I had written a letter of support for another most worthy candidate. So, when I received Tamer Başar’s email I thought that it was to inform me that this colleague had won. Who was actually responsible for my nomination? Several of my former graduate students! So, not only were they responsible for doing the work that qualified me for the award, they were even responsible for my getting it!

Over the course of my career, I was fortunate to have worked with a fantastic group of people and I am very proud of them: 64 Phd Students to date and about 25 postdocs. 27 of them are holding professorships all over the world – from the Korean Advanced Institute of Science and Technology KAIST in the East to Berkeley and Santa Barbara in the West from the Norwegian Technical University and the U Toronto in the North to the Technion in Israel and the Instituto Tecnologico de Buenos Aires in the South. Many others are now in industry, about 15 in Finance, Management Consulting and Legal, holding positions of major responsibility. I regard this group of former co-workers as my most important legacy.

This award means a lot to me because of the awe-inspiring people who received it in the past. I remember Hendrik Bode receiving the inaugural award in 1979. I remember Rutherford Aris, one of my PhD advisors at the University of Minnesota receiving it in 1992. Aris had actually worked and published with Richard Bellman. I remember Harmon Ray receiving it in 2000, my colleague and mentor at the University of Wisconsin.

Receiving this award made me also reflect on what I felt our major contributions were in these 34 years since I started my career as an Asst. Prof at Wisconsin. In what way was our work important? I was reminded of a dinner conversation a few months back with a group of my former PhD students who had joined McKinsey after graduating from ETH. One of them told me that our group had supplied more young consultants to McKinsey Switzerland than any other institute of any university in Switzerland. He also talked informally about the results of a survey done internally on what may be the main traits characterizing a CEO. It is not charm. It is not tactfulness and sensitivity. It is not intelligence. The only common trait seems to be that in their past these CEOs headed a division that experienced unusual growth. For example, the CEO of a telecom company had headed the mobile phone division. All the CEOs seemed to have been at the right place at the right time.

Similar considerations may apply to doing research and to heading a research group. Richard Hamming, best known for the Hamming code and the Hamming window, wrote in a wonderful essay: “If you are to do important work then you must work on the right problem at the right time and in the right way. Without any one of the three, you may do good work but you will almost certainly miss real greatness….”

So, what are the right problems? Eric Sevareid, the famous CBS journalist once quipped: “The chief cause of problems is solutions.” We were never interested in working on problems solely for their mathematical beauty. We always wanted to solve real practical problems with potential impact. Several times we were lucky to be standing at a turning point, ready to embark on a new line of research before the community at large had recognized it. Let me share with you three examples.

Around 1975, when I started my PhD at the University of Minnesota, interest in process control was just about at an all-time low. In 1979 this conference, which was then called the Joint Automatic Control Conference, had barely 300 attendees. The benefits of optimal control and the state space approach had been hyped so much for more than a decade that disillusionment was unavoidable. Many people advised me not to commence a thesis in process control. But my advisor George Stephanopoulos convinced me that the reason for all the disappointment was that people had been working on the wrong problem. The problem was not how to design controllers for poorly designed system but how to design systems such that they are easy to control. The work that was started at that time by us and several other groups provided valuable insights that are in common use today and set off a whole research movement with special sessions, special journal issues and even separate workshops and conferences.

The second example is our work on Internal Model Control (IMC) and Robust Control. In the early 1980s the term “robust control” did not exist or, at least, it was not widely used and accepted. From our application work and influenced by several senior members of our community we had become convinced that model uncertainty is a critical obstacle affecting controller design. We discovered singular values and the condition number as important indicators before we learned that these were established mathematical quantities with established names. In 1982 at a workshop in Interlaken I met John Doyle, Gunter Stein and essentially everybody else who started to push the robust control agenda. Indeed, it was there that Jürgen Ackerman made the researchers in the West aware of the results of Kharitonov. A year later I went to Caltech, John Doyle followed soon afterwards and an exciting research collaboration commenced that lasted for almost a decade. We also cofounded the Control and Dynamical Systems option/department at that time.

The third example is our more recent work on Model Predictive Control (MPC) and Hybrid Systems. As I returned to Switzerland 17 years ago, I moved from a chemical to an electrical engineering department. I was thrown into a new world of systems with time constants of micro- or even nanoseconds rather than the minutes or hours that I was used to. So, we set out to dispel the myth that MPC was only suited to slow process control problems and showed that it could even be applied to switched power electronics systems. Through this activity in parallel with a couple of other groups in the world, among them the group of Graham Goodwin, we started this era of “fast MPC” and contributed to the spread of MPC to just about every control application area.

I would never claim that in the mentioned areas we made the most significant contributions and some of the results may even seem trivial to you now, but we were there at the beginning. The Hungarian author Arthur Koestler remarked that “the more original a discovery, the more obvious it seems afterwards”

Notwithstanding this over-the-hill award that I received today and the mandatory retirement age in Switzerland I fully intend to strive to match these contributions in the coming years – together with my students, of course.

I want to close my remarks quoting from an interview Woody Allen gave last year. When he was asked “How do you feel about the aging process?” he replied: “Well, I’m against it. I think it has nothing to recommend it.”

First of all, I wish to express my sincere thanks to the American Automatic Control Council for bestowing on me the Bellman Control Heritage Award. This great honor was completely unexpected so that my gratitude is very deep indeed. I would like to use this rare opportunity to say a few words about a topic which has concerned me for some time, namely, the question Who did what first?. In so doing, I shall relate two examples of which the first is especially a propos since it involves the patron of the award, Richard Bellman, as well as Rufus Isaacs, both long-time friends of mine. When I attended the 1966 International Congress of Mathematicians in Moscow, where Dick was a plenary speaker and Rufus was to present a paper entitled Differential games and dynamic programming, and what the latter can learn from the former, the meeting was buzzing with excitement about an upcoming confrontation between two well known American mathematicians. And indeed, when Rufus presented his paper it was his take on the discovery of the Principle of Optimality which, in his view, appeared after the in-house publication of three RAND reports on differential games, and which appeared to be just a one-player version of his Tenet of Transition. The result of this implied accusation of plagiarism had two unhappy consequences. I had lunch with Dick on that day. He was deeply hurt, so much so that he was near tears. Equally unfortunate was the effect on Rufus who devoted much of his remaining time to trying to prove the priority of his discovery instead of continuing to produce new and important research of which his fertile mind was surely capable. The second example is a much happier one. In the mid-1960's I published a brief paper in which I proposed constructive sufficiency conditions for extremizing a class of integrals by solving an equivalent problem by inspection. It was not until 1999 that I returned to this subject at the urging of a Canadian colleague. After revisiting the original 1967 paper, I published a generalization in JOTA in 2001. On presenting these results at my 75th birthday symposium in Sicily in 2001, Pierre Bernhard remarked that my approach seemed to be related to Caratheodory's in his 1935 text on the calculus of variations and partial differential equations, first translated into English in the mid-1960's and not known to me. And indeed, in 2002, Dean Carlson published in JOTA a paper in which he discussed a relation between the two approaches in that both are based on the equivalent problem methodology. Caratheodory obtained an equivalent problem by allowing for a different integrand, and I obtained an equivalent problem by the use of transformed variables. Dean then proposed a generalization by combining the two approaches. A happy consequence of this paper has been and continues to be a fruitful collaboration which has resulted in many extensions and applications, e.g., to classes of optimal control and differential game problems, to multiple integrals, and to economic problems, the most recent concerned with differential constraints (state equations) and presented just a couple of weeks ago at the 15th International Workshop on Dynamics and Control. A particularly interesting discussion and some generalizations by Florian Wagener may be found in the July 2009 issue of JOTA. Thus, Caratheodory received his well deserved citation and I learned a great deal, allowing me to make some small contributions to optimization theory.
June 11, 2009. St. Louis, MO

It is a great honor for me to receive the Bellman Award—quite undeserved I believe, but I decided not to emulate Gregory Perelman by refusing to accept the award. I might however follow his footsteps (apparently he has stopped doing Mathematics) and concentrate only on the more conceptual and philosophical aspects of the broad field of Systems and Control.

On an occasion like this it is perhaps appropriate to say a few words about the seminal contributions of Richard Bellman. As we all know, he is the founder of the methodological framework of Dynamic Programming, probably the only general method of systematically and optimally dealing with uncertainty, when uncertainty has a probabilistic description, and there is an underlying Markov structure in the description of the evolution of the system. It is often mentioned that the work of Bellman was not as original as would appear at first sight. There was, after all, Abraham Wald’s seminal work on Optimal Sequential Decisions and the Carat´eodory view of Calculus of Variations, intimately related to Hamilton–Jacobi Theory. But the generality of these ideas, both for deterministic optimal control and stochastic optimal control with full or partial observations, is undoubtedly due to Bellman. Bellman, I believe, was also the first to present a precise view of stochastic adaptive control using methods of dynamic programming. Now, there are two essential steps in invoking Dynamic Programming, namely, invariant embedding whereby a fixed variational problem is embedded in a potentially infinite family of variational problems and then invoking the Principle of Optimality which states that any sub-trajectory of an optimal trajectory is necessarily optimal to characterize optimal trajectories. This is where the Markov structure of dynamic evolution comes into operation. It should be noted that there is wide flexibility in the invariant embedding procedure and this needs to be exploited in a creative way. It is this embedding that permits obtaining the optimal control in feedback form (that is a “control law” as opposed to open loop control).

The solution of the Partially-Observed Stochastic Control in continuous time leading to the characterization of the optimal control as a function of the unnormalized conditional density of the state given the observations via the solution of an infinite-dimensional Bellman–Hamilton–Jacobi equation is one of the crowning achievements of the Bellman view of stochastic control. It is worth mentioning that Stochastic Finance Theory would not exist but for this development. There are still open mathematical questions here that deserved further work. Indeed, the average cost problem for partially-observed finite-state Markov chains is still open—a natural necessary and sufficient condition for the existence of a bounded solution to the dynamic programming equation is still not available.

Much of my recent work has been concerned with the unification of theories of Communication and Control. More precisely, how does one bring to bear Information Theory to gain understanding of Stochastic Control and how does one bring to bear the theory of Partially-Observed Stochastic Control to gain qualitative understanding of reliable communication. There does not exist a straightforward answer to this question since the Noisy Channel Coding Theorem which characterizes the optimal rate of transmission for reliable communication requires infinite delay. The encoder in digital communication can legitimately be thought of as a controller and the decoder an estimator, but they interact in complicated ways. It is only in the limit of infinite delay that the problem simplifies and a theorem like the Noisy Channel Coding Theorem can be proved. This procedure is exactly analogous to passing to the thermodynamic limit in Statistical Mechanics.

In the doctoral dissertation of Sekhar Tatikonda, and in subsequent work, the Shannon Capacity of a Markov Channel with Feedback under certain information structure hypotheses can be characterized as the value function of a partially-observed stochastic control problem. This work in many ways exhibits the power of the dynamic programming style of thinking. I believe that this style of thinking, in the guise of a backward induction procedure, will be helpful in understanding the transmission capabilities of wireless networks. More generally, dynamic programming, when time is replaced by a partially ordered set, is a fruitful area of research.

Can one give an “information flow” view of path estimation of a diffusion process given noisy observations? An estimator, abstractly can be thought of as a map from the space of observations to a conditional distribution of the estimand given the observations. What is the nature of the flow of information from the observations to the estimator? Is it conservative or dissipative? In joint work with Nigel Newton, I have given a quite complete view of this subject. It turns out that the path estimator can be constructed as a backward likelihood filter which estimate the initial state combined with a fully observed stochastic controller moving in forward time starting at this estimated state solves the problem in the sense that the resulting path space measure is the requisite conditional distribution. The backward filter dissipates historical information at an optimal rate, namely that information which is not required to estimate the initial state and the forward control problem fully recovers this information. The optimal path estimator is conservative. This result establishes the relation between stochastic control and optimal filtering. Somewhat surprisingly, the optimal filter in a stationary situation satisfies a second law of thermodynamics.

What of the future? Undoubtedly we have to understand control under uncertainty in a distributed environment. Understanding the interaction between communication and control in a fundamental way will be the key to developing any such theory. I believe that an interconnection view where sensors, actuators, controllers, encoders, channels and decoders, each viewed abstractly as stochastic kernels, are interconnected to realize desirable joint distributions, will be the “correct” abstract view for a theory of distributed control. Except in the field of distributed algorithms, not much fundamental seems to be known here.

It is customary to end acceptance discourses on an autobiographical note and I will not depart from this tradition. Firstly, my early education at Presidency College, Calcutta, where I had the privilege of interacting with some of the most brilliant fellow students, decisively formed my intellectual make-up. Whatever culture I acquired, I acquired it at that time. At Imperial College, while I was doing my doctoral work, I was greatly influenced by John Florentin (a pioneer in Stochastic Control), Martin Clark and several other fellow students. I have also been fortunate in my association with two great institutions—MIT and the Scuola Normale, Pisa. I cannot overstate everything that I have learnt from my doctoral students, too many to mention by name—Allen gewidmet von denen ich lernte [Dedicated to all from whom I have learnt (taken from dedication of G¨unter Grass in “Beim H¨auten der Zwiebel” (“Peeling the Onion”))]. I find that they have extraordinary courage in shaping some half-baked idea into a worthwhile contribution. In recent years, my collaborative work with Vivek Borkar and Nigel Newton has been very important for me. I have great intellectual affinity with members of Club 34, the most exclusive club of its kind and I thank the members of this club for their friendship. There are many others whose intellectual views I share, but at the cost of exclusion let me single out Jan Willems and Pravin Varaiya. I admire their passion for intellectual discourse. Last, but not least, I thank my wife, Adriana, for her love and support. I am sorry she could not be here today. My acceptance speech is dedicated to her.

July 12, 2007. New York, NY

Grow old along with me
The best is yet to be'
Browning

I don't feel particularly old but to be in the midst of friends and colleagues with this recognition is as good as it gets.

I'd like to use these few minutes to comment on several of the times when I've come to a fork in the road as an illustration of how difficult it is to predict how a given path will turn out. There may be people who plan their lives carefully and take each step based on the best prediction of a good outcome; I'm not one of them. Too many events in my life were based on random events to pretend that they were based on any good planning of mine.

My first decision was a good one: I selected outstanding parents. My father was a math teacher, my mother an RN and they gave me a love of books and learning that have served me well for over 7 decades. They did, however, make one mistake: they gave me a defective gene that prevents me from seeing colors the way most others see them. If you see me going Ooh and Ah over a rainbow, don't believe it; I'm faking it.

The next decision I wish to mention was in 1945 when I became eligible for the military draft. The good news was that I was admitted to the Navy Radio Technician program but the bad news was that I had to sign up for four years to accept the offer. The evidence was that the war would last several more years so I signed up. That decision did not look so good a few weeks later when President Truman approved use of atomic bombs to reduce Hiroshima to rubble and Nagasaki to ruin in a matter of seconds. The war ended soon after but I was still stuck with four years obligation to the Navy. When I got to Chicago for my final physical, one of the doctors asked me to identify the numbers in a set of circles filled with colored dots. I'm sure that I gave him some values never before found! My performance was such that he marked me as partially disabled, put me on medical special assignment, and sent me off to the electronics school.

I finished the school in the summer of 1946 and was selected to be an instructor at a new campus being set up at the Great Lakes Naval Training Center north of Chicago. I taught electronic amplifiers there using the book Radio Engineering by F E Terman. One of my fellow students there later became well known in the control field (and a Vice President of IBM): Jack Bertram had also signed up for the Navy electronics program. In the early summer of 1947 my defective gene came to my rescue. The Navy announced that any sailor on medical special assignment was eligible for discharge! My response: That's ME.

Out of the Navy I went and set about looking for a school that would accept me at that late date. I was turned down by several fine schools but Georgia Tech told me to come on down so off I went to Atlanta where I got my EE degree in 1950. The months I'd served in the Navy made me eligible for enough GI Bill support to pay the tuition and expenses which I could never have afforded otherwise. This time the bad news was that in the spring of 1950 the Bureau of Labor Statistics reported that the country was to graduate twice as many engineers as the economy could absorb. My only choice was to accept a fellowship to MIT and continue my education using the last of my GI Bill of Rights tuition support. As an aside, while there I took a graduate course on pulse and timing circuits that contained little new from what we had learned in the Navy program as high school graduates! I also had a great time learning how to play rugby from a group of graduate students from South Africa. A most memorable part of this experience was when we were one of the teams selected to play in a tournament as the entertainment for spring break in Bermuda.

After finishing my MS in 1952 I had married the love of my life and needed to get a job. A fellow student introduced me to Professor Jack Millman who was visiting MIT looking for possible appointments to Columbia University. I interviewed with him and was offered a position as Instructor which involved teaching responsibilities but allowed me to study for the doctorate at the same time. I had no idea that I was stepping into a fantastic center of control research assembled by John Ragazzini. With his colleague Lotfi Zadeh he had attracted great students including Eli Jury, Art Bergen, Jack Bertram, Rudy Kalman, Bernie Friedland, George Kranc, and Phil Sarachik. Sampled Data control was never the same again. The first treatment of 'pulsed circuits' was chapter 5 by Hurewicz in the Rad Lab Vol. 25 on The Theory of Servomechanisms edited by James Nichols and Phillips. Hurewicz selected the variable of discrete transforms as z, a prediction of one period and we kept the same convention. At about the time as Ragazzini's group were starting our study, some at MIT selected z to be a delay operator. In the end, z as predictor prevailed but to this day MATLAB treats discrete transforms differently in the Signal Processing toolbox as they do in the Control toolbox. You can look it up.

After I got my degree in 1955 I was promoted to Assistant Professor. I loved Columbia and was pleased to be selected by Professor Ragazzini to join him as co-author of a book on sampled data but New York City left a lot to be desired as a place to raise the two children who had joined my family by this time and soon another fork in the road appeared. It was presented in the person of Professor John Linvill whose class I had taken at MIT and who had moved from MIT to Stanford by way of Bell Labs. John knew Lotfi Zadeh and at his invitation came to Columbia looking for possible new appointments to Stanford's faculty. Again I interviewed and was offered a position on the Stanford Faculty. Thus it was that in late May of 1957 we loaded up the (non air-conditioned) Ford and headed west. I'll never forget the hot day in June when we stopped for gas in Sacramento where the temperature was well over 100 degrees. The pavement was so soft my shoes sank into the asphalt. Then later that day we crossed the mountains into the Bay Area and the temperature dropped about 1 degree per mile for the last 30 miles. We've been in love with the San Francisco Bay area ever since.

As an aside comment on control at the time, in the paper on The history of the Society by Danny Abramovitch and myself, George Axelby is quoted as saying that papers presented at the 1959 conference on control by Kalman and Bertram using state notation were 'quite a mystery to most attendees.' I'd say that the idea of state was not long a mystery to those who had worked with analog computers. On those machines, the only dynamic elements are integrators whose outputs comprise the state quite naturally. In my opinion, every control engineer should be required to program an analog computer where one also quickly learns the value of amplitude and time scaling too.

In any case, such was the random walk through time and space that has taken me from the mountains of North Carolina to the coast of California. My tenure at Stanford has been marked by many things but first and foremost in my affection has been the steady stream of excellent students with whom I have been privileged to work. Without a doubt they have made major contributions to control and to them is owed much of the credit for which this award in made. So let me close with the moral of my story aimed mainly to those in academia:
You can never be too careful when selecting your students.
The corollary to this is applicable to everyone:It's hard to soar like an Eagle if you fly with a bunch of turkeys.

Thank you very much.
June 9, 2005. Portland, OR