Silvia Ferrari, Assistant Professor of Mechanical Engineering and Materials Science
Professor Ferrari's research aims at providing intelligent control systems with a higher degree of mathematical structure to guide their application and improve reliability. Decision-making processes are automated based on concepts drawn from control theory and the life sciences. Recent efforts have focused on the development of reconfigurable controllers implementing neural networks with procedural long-term memories. Full-scale simulations show that these controllers are capable of learning from new and unmodeled aircraft dynamics in real time, improving performance and even preventing loss of control in the event of control failures, nonlinear and near-stall dynamics, and parameter variations. New optimal control problems and methods based on computational geometry are being investigated to improve the effectiveness of integrated surveillance systems by networks of autonomous vehicles, such as, underwater gliders and ground robots.
| Office Location: | |
| Office Phone: | +1 919 660 5310 |
| Email Address: |   |
Teaching (Fall 2008):
- ME 125L.001, CONTROL SYSTEMS
- Hudson 115A, MF 01:15 PM-02:30 PM
- ME 125L.01L, CONTROL SYSTEMS
- Hudson 207, Tu 04:25 PM-06:55 PM
- ME 125L.02L, CONTROL SYSTEMS
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- ME 125L.03L, CONTROL SYSTEMS
- Hudson 224, F 03:05 PM-05:30 PM
- ME 125L.04L, CONTROL SYSTEMS
- Hudson 201, W 11:55 AM-02:20 PM
- ME 125L.05L, CONTROL SYSTEMS
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- Education:
| PhD | Princeton University, Princeton, NJ | 2002 |
| MA | Princeton University, Princeton, NJ | 1999 |
| BS | Embry-Riddle Aeronautical University, Daytona Beach, FL | 1997 |
- Research Interests:
Approximate dynamic programming
Reconfigurable control of aircraft
On-line learning in neural and Baysian networks
Sensor planning for integrated surveillance systems
Intelligent systems for criminal profiling
- Keywords:
- Approximate dynamic programming
• Reconfigurable control of aircraft
• On-line learning in neural and Baysian networks
• Sensor planning for integrated surveillance systems
• Intelligent systems for criminal profiling
- Representative Publications
- Ferrari, S., Palermo, G., Bruzzone, R., Strano, M., Network Models of Criminal Behavior,
IEEE Control Systems Magazine
(Accepted, 2008)
- Ferrari, S., and Jensenius M., A Constrained Optimization Approach to Preserving Prior Knowledge During Incremental Training,
IEEE Transactions on Neural Networks
(Accepted, April 2008)
- Ferrari, S., Cai, C., Fierro, R., and Perteet, B., A Multi-Objective Optimization Approach to Detecting and Tracking Dynamic Targets in Pursuit-Evasion Games,
Proc. of the 2007 American Control Conference, New York, NY
(July 2007),
pp. 5316-5321
- Baumgartner, K. C., and Ferrari, S., Optimal Control of a Moving Sensor Network for Track Coverage,
Proc. of the 2007 American Control Conference, New York, NY
(July 2007),
pp. 4040-4046
- Cai, C., and Ferrari, S., On the Development of an Intelligent Computer Player for CLUE®, the Great Detective Boardgame: A Case Study in Preposterior Decision Analysis,
Proc. of the 2006 American Control Conference, Minneapolis, MN
(June 2006),
pp. 4350-4355
- Ferrari, S., Vaghi, A., Demining Sensor Modeling and Feature-level Fusion by Bayesian Networks,
IEEE Sensors Journal, vol. 6 no. 2
(April 2006),
pp. 471-483
- S. Ferrari, Track Coverage in Sensor Networks,
Proc. of the 2006 American Control Conference, Minneapolis, MN
(June 2006),
pp. 2053-2059
- Ferrari, S., Stengel, R. F., Smooth Function Approximation by Neural Networks,
IEEE Transactions on Neural Networks, vol. 16 no. 1
(January 2005),
pp. 24-38
- Crews B. K., Ferrari, S., Salfati, C. G., Bayesian Network Modeling of Offender Behavior for Criminal Profiling,
Proc. of the 44th IEEE Conference on Decision and Control, and the European Control Conference
(December 12-15, 2005),
pp. 2702-2709 (Seville, Spain.)
- Ferrari, S., Stengel, R. F., On-line Adaptive Critic Flight Control,
Journal of Guidance, Control, and Dynamics, vol. 27 no. 5
(Sept-Oct 2004),
pp. 777-786
- Ferrari, S., Stengel, R. F., Model-based Adaptive Critic Designs,
in Learning and Approximate Dynamic Programming: Scaling Up to the Real World, edited by J. Si, A. Barto, W. Powell, D. Wunsch
(2004), IEEE Press and John Wiley & Sons
- Ferrari, S., Stengel, R. F., Classical/Neural Synthesis of Nonlinear Control Systems,
Journal of Guidance, Control, and Dynamics, vol. 25 no. 3
(May-June 2002),
pp. 442-448
Recent Honors and Awards
- Presidential Early Career Award for Scientists and Engineers (PECASE), 2006
- International Crime Analysis Association Research Award
- National Science Foundation CAREER Award, 2005
- Office of Naval Research Young Investigator Award, 2008