[ISS4E] Talk by Anil Aswani on July 5

Srinivasan Keshav keshav at uwaterloo.ca
Thu Jun 28 14:13:22 EDT 2012

Robust Statistical Control Theory for High Performance Systems

Anil Aswani, UC Berkeley

10:00 - 11:00 am
Thursday July 5, 2012
DC 1304

Achieving high efficiency and performance in engineered systems can be
challenging because of the difficulty in identifying accurate models. For
instance, heating, ventilation, and air-conditioning (HVAC) modulates
building environment to ensure occupant comfort. And though HVAC can be
described by simple physics-based processes, the impact of building
occupants makes it difficult to create models for energy-efficient HVAC

This talk describes two new techniques that rigorously combine statistical
methods with control engineering, for the purpose of identification,
analysis, and control of systems for which models are not well known. The
first is a regression technique that leverages differential geometry to
better reduce noise when identifying linear or local-linear models of
systems in which measurements have either manifold or collinear structure.
The second is a control method called learning-based model predictive
control (LBMPC) that allows statistics to improve performance through
identification of better system models, while providing theoretical
guarantees on the robustness and stability of the closed-loop control. The
improvements possible with these techniques are illustrated with
applications to energy-efficient HVAC systems and high performance control
of semi-autonomous systems.

Anil is a postdoc at UC Berkeley. He received his PhD from Berkeley in
2010, an MS from Berkeley in 2007, and a BSEE from the University of
Michigan, in Ann Arbor in 2005.

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