Note: This content is accessible to all versions of every browser. However, this browser does not seem to support current Web standards, preventing the display of our site's design details.


Robust Model Predictive Control with Waysets

Waypoint guidance is a well-known technique for reducing the computational complexity of large control problems, especially those pertaining to vehicle trajectory planning. However, it suffers from the inability to provide robustness guarantees in the presence of persistent disturbances. This talk will introduce the notion of waysets as a generalisation of waypoints, allowing robustness issues to be addressed. It will be shown how model predictive control, combined with wayset guidance and constraint tightening, can robustly steer the state of a system in the presence of bounded disturbances, whilst significantly reducing computational complexity when compared to existing approaches. A simple UAV model with non-convex minimum speed and collision avoidance constraints will be used to demonstrate the technique, as well as motivate other potential applications of the developed theory.

Type of Seminar:
IfA Seminar
Dr. Rohan Shekhar
University of Melbourne, Australia
Jun 30, 2014   16:15

HG D 3.2, Rämistrasse 101
Contact Person:

Dr. Paul Goulart
File Download:

Request a copy of this publication.
Biographical Sketch:
Rohan Shekhar received the B.E. degree (with honours) from The University of Queensland and the Ph.D. degree from the University of Cambridge, in 2006 and 2012 respectively. Since 2012, he has been a postdoctoral research fellow at The University of Melbourne. His research interests include extremum-seeking control and robust model predictive control, with applications to automotive systems, autonomous vehicles and mining technology.