Fast Real-Time Online Optimization for Output feedback Control

April 06, 2018, Webb 1100

Joao Hespanha

UCSB, Electrical and Computer Engineering


The availability of 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 focus the presentation on the theoretical 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.

Speaker's 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.
His current research interests include hybrid and switched systems; multi-agent control systems; distributed control over communication networks (also known as networked control systems); the use of vision in feedback control; stochastic modeling in biology; and network security.
Dr. Hespanha is the recipient of the Yale University’s Henry Prentiss Becton Graduate Prize for exceptional achievement in research in Engineering and Applied Science, a National Science Foundation CAREER Award, the 2005 best paper award at the 2nd Int. Conf. on Intelligent Sensing and Information Processing, the 2005 Automatica Theory/Methodology best paper prize, the 2006 George S. Axelby Outstanding Paper Award, and the 2009 Ruberti Young Researcher Prize. Dr. Hespanha is a Fellow of the IEEE and he was an IEEE distinguished lecturer from 2007 to 2013.

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