Silvia Ferrari

Ferrari

Adjunct Professor in the Department 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.

Appointments and Affiliations

  • Adjunct Professor in the Department of Mechanical Engineering and Materials Science
  • Faculty Network Member of the Duke Institute for Brain Sciences

Contact Information

  • Office Location: 144 Hudson Hall, Box 90300, Durham, NC 27708
  • Email Address: silvia.ferrari@duke.edu

Education

  • Ph.D. Princeton University, 2002
  • M.A. Princeton University, 1999
  • B.S. Embry-Riddle Aeronautical University, 1997

Awards, Honors, and Distinctions

  • Faculty Early Career Development (CAREER) Program. National Science Foundation. 2005

In the News

Representative Publications

  • Yang, H; Jing, D; Tarokh, V; Bewley, G; Ferrari, S, Flow parameter estimation based on on-board measurements of air vehicle traversing turbulent flows, Aiaa Scitech 2021 Forum (2021), pp. 1-10 [abs].
  • Doerr, B; Linares, R; Zhu, P; Ferrari, S, Random finite set theory and centralized control of large collaborative swarms, Journal of Guidance, Control, and Dynamics, vol 44 no. 3 (2021), pp. 505-521 [10.2514/1.G004861] [abs].
  • Tilmon, B; Jain, E; Ferrari, S; Koppal, SJ, Fast Foveating Cameras for Dense Adaptive Resolution, Ieee Transactions on Pattern Analysis and Machine Intelligence (2021) [10.1109/TPAMI.2021.3071588] [abs].
  • Zhu, P; Liu, C; Ferrari, S, Adaptive Online Distributed Optimal Control of Very-Large-Scale Robotic Systems, Ieee Transactions on Control of Network Systems (2021) [10.1109/TCNS.2021.3097306] [abs].
  • Ferrari, S; Wettergren, T; Linares, R; Legrand, K, Guest Editorial Special Issue on Control of Very-large Scale Robotic (VLSR) Networks, Ieee Transactions on Control of Network Systems (2021) [10.1109/TCNS.2021.3097667] [abs].