Colloquium speaker: João Hespanha, UC Santa Barbara – 5/4, 10:30 a.m.

March 30, 2021

We hope you can join us for our ECE Colloquium Research Series 2021 this Spring Quarter that highlights our quarter’s theme of “At the Intersection of Machine Learning and Game Theory.”

Abstract
Low-cost, low-power embedded computation enables the use of online optimization to solve nonlinear control problems with hard state and input constraints, leading to the popularity of Model Predictive Control (MPC) in numerous industrial applications. More recently, online optimization also became popular to solve estimation problems that can take advantage of known constraints on the state, measurement noise, and disturbances. In particular, Moving Horizon Estimation (MHE) computes states estimates that are maximally compatible with measurements observed over a finite window of time. 

In this talk, we discuss an optimization-based approach to solve output feedback control problems that combines state estimation and control into a single min-max optimization. We discuss the challenges involved in guaranteeing the convergence of the closed loop systems, as well as the computational techniques that are needed to solve the resulting optimizations in real-time control systems with sampling times on the order of just a few milliseconds.

Bio
João P. Hespanha was born in Coimbra, Portugal, in 1968. He received the Licenciatura in electrical and computer engineering from the Instituto Superior Técnico, Lisbon, Portugal in 1991 and the Ph.D. degree in electrical engineering and applied science from Yale University, New Haven, Connecticut in 1998. From 1999 to 2001, he was Assistant Professor at the University of Southern California, Los Angeles. He moved to the University of California, Santa Barbara in 2002, where he currently holds a Professor position with the Department of Electrical and Computer Engineering. More info…

See full schedule at https://www.ece.uw.edu/colloquium/