Data Driven Learning and Control seminar series is organized by the Information and Decision Science Lab at Cornell University and aims to explore the latest advancements and interdisciplinary approaches to data-driven learning and control systems.

Watch on YouTube Live
 

Optimal Control or Optimal Learning?… How About Both!

Real-time learning strategies where, at infinite time and under PE conditions, gains become optimal are common — they are a particular variety of RL and called also adaptive dynamic programming or "adaptive-optimal" control. What is, in contrast, truly of interest to the practitioner are OPTIMAL-ADAPTIVE controllers (note the commuted order of the two adjectives). They are optimal during the entire infinite horizon. Conceived in 1997 by the speaker, this idea failed to spread widely because in general its controllers - while given explicitly - have complicated expressions. The exception, where the optimal-adaptive controllers become elegant, are the so-called “driftless systems,” of the form dx/dt = g(x)u (with f=0), where dim(x)>dim(u). Nonholonomic mobile robots are archetypal driftless systems. For such vehicles, we first present globally uniformly Lagrange asymptotically stabilizing (GULAS) feedback laws, which don’t contradict Brockett’s condition. With their strict CLFs, adaptive LgV-type feedback laws are then designed for vehicles with completely unknown wheel traction coefficients. These controllers optimize cost functionals that not only penalize the longitudinal and angular velocity inputs, as well as the unicycle’s three configuration states, but also the parameter estimation errors - over the entire infinite time horizon (not only in the asymptotic limit). As a bonus, control designs that complete parking in user-desired time will be shown: (1) a time-varying feedback, with gains that are singular at terminal time but keep the controls bounded, and (2) a static homogeneous feedback, which is nonsmooth at the target values of position and heading.
 

Bio: Miroslav Krstic is Distinguished Professor of mechanical and aerospace engineering, holds the Alspach endowed chair, and is the founding director of the Center for Control Systems and Dynamics at UC San Diego. He also serves as Senior Associate Vice Chancellor for Research at UCSD.

As a graduate student, Krstic won the UC Santa Barbara best dissertation award and student best paper awards at CDC and ACC. Krstic has been elected Fellow of IEEE, IFAC, ASME, SIAM, AAAS, IET (UK), and AIAA (Assoc. Fellow) - and as a foreign member of the Serbian Academy of Sciences and Arts and of the Academy of Engineering of Serbia. He has received the IEEE Roger W. Brockett Control Systems Award, SIAM Reid Prize, ASME Oldenburger Medal, Control Systems Society Distinguished Member Award, the PECASE, NSF Career, and ONR Young Investigator awards. Krstic is a Fellow-Ambassador of the French CNRS and has also been awarded the Springer Visiting Professorship at UC Berkeley, the Distinguished Visiting Fellowship of the Royal Academy of Engineering, the Invitation Fellowship of the Japan Society for the Promotion of Science, and four honorary professorships outside of the United States.

He serves as editor-in-chief of Systems & Control Letters and has been serving as senior editor in Automatica and IEEE Transactions on Automatic Control, as editor of two Springer book series, and has served as Vice President for Technical Activities of the IEEE Control Systems Society and as chair of the IEEE CSS Fellow Committee. Krstic has coauthored 19 books on adaptive, nonlinear, and stochastic control, extremum seeking, control of PDE systems including turbulent flows, and control of delay systems.