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Andrew Lamperski

Graduate Student
Control and Dynamical Systems
California Institute of Technology

Office: Steele 19
Address:
1200 E California Blvd
MC 107-81
Pasadena, CA 91125
Email: andyl(at)cds(dot)caltech(dot)edu

Curriculum Vitae PDF

Research Summary

My current research focuses on networked and distributed control for neuroscience applications. Most recently, I have developed techniques to derive explicit linear quadratic Gaussian (LQG) controllers for distributed systems with limited information propagation rates between subsystems. Such architectures are motivated by coordination tasks in neural control systems. Neurons transmit information relatively slowly, so an optimal neural controller for a coordination task, such as walking, must account for communication delays. The eventual goal of the research is to use the structure of the distributed LQG controllers to provide insight into how neural control systems combine information from the brain, the spinal cord, and the peripheral nervous system.

My previous research dealt with hybrid systems and bio-inspired robotics. In hybrid systems, I developed Lyapunov-based techniques to analyze Zeno behavior, a phenomenon unique to hybrid systems, in which an infinite number of jumps occur in a finite amount of time. My work in robotics focused on antenna-based wall following, inspired by cockroach locomotion.

Education

Ph.D. in Control and Dynamical Systems, California Institute of Technology, expected 2011
Advisor: John C. Doyle

B.S. in Biomedical Engineering and Mathematics, The Johns Hopkins University, 2004

Publications

PhD Thesis

Hierarchies, Spikes, and Hybrid Systems: Physiologically Inspired Control Problems, 2011

Teaching