Publications

Journal Articles

  1. G. Foderaro, A. Swingler, S. Ferrari, “A Model-Based Approach to Optimizing Ms. Pac-Man Game Strategies in Real Time”, IEEE Trans. on Computational Intelligence and AI in Games, Vol. 9, No. 2, pp. 153-165, June 2017. [PDF]
  2. W. Lu, P. Zhu, S. Ferrari, “A Hybrid-Adaptive Dynamic Programming Approach for the Model-Free Control of Nonlinear Switched Systems,” IEEE Trans. on Automatic Control, Vol. 61, No. 10, pp. 3203-3208, October 2016.  [PDF]
  3. H. Wei, W. Lu, P. Zhu, S. Ferrari, M. Liu, R. Klein, S. Omidshaei, J. P. How, “Information Value in Nonparametric Dirichlet-Process Gaussian-Process (DPGP) Mixture Models,” Automatica, Vol. 74, pp. 360-268, December 2016.  [PDF]
  4. P. Mazumder, D. Hu, I.Ebong, X. Zhang, Z. Xu, and S. Ferrari, “Digital implementation of a virtual insect trained by spike-timing dependent plasticity,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, Vol. 54, pp. 109-117, June 2016.  [PDF]
  5. H. Oh, J. Beck, P. Zhu, M. A. Sommer, S. Ferrari, and T. Egner, “Satisficing in split second decision making is characterized by strategic cue discounting,” Journal of Experimental Psychology: Learning, Memory, and Cognition, Vol. 42, No. 12, pp. 1937–1956, 2016.  [PDF]
  6. G. Foderaro, P. Zhu, H. Wei, T. A. Wettergren, S. Ferrari, “Distributed Optimal Control of Sensor Networks for Dynamic Target Tracking,” IEEE Transactions on Control of Network Systems, (doi: 10.1109/TCNS.2016.2583070), in press.  [PDF]
  7. G. Foderaro, A. Swingler, and S. Ferrari, “A Model-based Approach to Optimizing Ms. Pac-Man Game Strategies in Real Time,” IEEE Transactions on Computational Intelligence and AI in Games, (doi: 10.1109/TCIAIG.2016.2523508), in press.  [PDF]
  8. J. Albertson, T. Harvey, G. Foderaro, P. Zhu, X. Zhou, S. Ferrari, S. M. Amin, M. Modrak, H. Brantley, E. Thoma, “A Mobile Sensing Approach for Regional Surveillance of Fugitive Methane Emissions in Oil and Gas Production,” Environmental Science and Technology, Vol. 50, No. 5, pp. 2487-2497, 2016.  [PDF]
  9. S. Ferrari, G. Foderaro, P. Zhu, and T. Wettergren “Distributed Optimal Control of Multiscale Dynamical Systems: A Tutorial”, Control Systems Magazine, Vol. 36, No. 2, pp. 102-116 2016.  [PDF]
  10. H. Wei, and S. Ferrari, “A Geometric Transversals Approach to Sensor Motion Planning for Tracking Maneuvering Targets,” IEEE Transactions on Automatic Control, Vol. 60, No. 10, pp. 2773-2778, 2015.  [PDF]
  11. W. Lu, G. Zhang, and S. Ferrari, “An Information Potential Approach to Integrated Sensor Path Planning and Control,” in Robotics, IEEE Transactions on, Vol. 30, No. 4, pp. 919-934, 2014.  [PDF]
  12. G. Foderaro, S. Ferrari, T. A. Wettergren, “Distributed Optimal Control for Multi-agent Trajectory Optimization,” Automatica, Vol. 50, No. 1, pp. 149-154, 2014.  [PDF]
  13. K. Rudd, G. Di Muro, and S. Ferrari, “A Constrained Backpropagation Approach for the Adaptive Solution of Partial Differential Equations,” Neural Networks and Learning Systems, IEEE Transactions on, Vol. 25, No. 3, pp. 571-584, 2014.  [PDF]
  14. H. Wei and S. Ferrari, “A Geometric Transversals Approach to Analyzing the Probability of Track Detection for Maneuvering Targets,” Computers, IEEE Transactions on, Vol. 63, No. 11, pp. 2633-2646, 2014.  [PDF]
  15. X. Zhang, G. Foderaro, C. Henriquez, A. M. J. VanDongen, S. Ferrari, “A Radial Basis Function Spike Model for Indirect Learning via Integrate-and-Fire Sampling and Reconstruction Techniques,” Advances in Artificial Neural Systems, Article ID 713581, 2012, p.10 (doi: 10.1155/2012/713581).  [PDF]
  16. N. Maheswaranathan, S. Ferrari, A. M. J. VanDongen and C. Henriquez, “Emergent bursting and synchrony in computer simulations of neuronal cultures,” Frontiers in Computational Neuroscience, Vol. 6, No. 15, 2012.  [PDF]
  17. W. Lu, G. Zhang, S. Ferrari, M. Anderson, and R. Fierro “A particle-filter information potential method for tracking and monitoring maneuvering targets using a mobile sensor agent,” The Journal of Defense Modeling and Simulation: Applications, Methodologym Technology, June 2012.  [PDF]
  18. W. Lu, S. Ferrari, R. Fierro, and T. W. Wettergren, “Approximate dynamic programming recurrence relations for a hybrid optimal control problem.” SPIE Defense, Security, and Sensing. International Society for Optics and Photonics, 2012.  [PDF]
  19. S. Ferrari, G. Zhang, and C. Cai, “A Comparison of Information Functions and Search Strategies for Sensor Planning, ” IEEE Transactions on Systems, Man, and Cybernetics – Part B, Vol. 42, No. 1, 2012.  [PDF]
  20. N. Bezzo, R. Fierro, A. Swingler, and S. Ferrari, “Mobile Router Networks: A Disjunctive Programming Approach,” International Journal of Robotics and Automation, Vol. 26, No. 1, pp. 13-25, 2011.  [PDF]
  21. G. Foderaro, V. Raju, and S. Ferrari, “A Model-based Approximate λ-Policy Iteration Approach to Online Evasive Path Planning and the Video Game Ms. Pac-Man,” Journal of Control Theory and Applications, Vol. 9, No. 3, pp. 391-399, 2011.  [PDF]
  22. S. Ferrari and G. Daugherty, “A Q-learning Approach to Automated Unmanned Air Vehicle Demining,” The Journal of Defense Modeling and Simulation, Vol. 9, pp. 83-92, 2011.  [PDF]
  23. S. Ferrari, G. Zhang, and T. Wettergren, “Probabilistic Track Coverage in Cooperative Sensor Networks,” IEEE Transactions on Systems, Man, and Cybernetics – Part B, Vol. 40, No.6, 2010.  [PDF]
  24. K. C. Baumgartner, S. Ferrari, and A. Rao, “Optimal Control of a Mobile Sensor Network for Cooperative Target Detection,” IEEE Journal of Oceanic Engineering, Vol. 34, No. 4, pp. 678-697, 2009.  [PDF]
  25. K. C. Baumgartner, S. Ferrari, and T. Wettergren, “Robust Deployment of Ocean Sensor Networks,” IEEE Sensors Journal, Vol. 9, No. 9, pp. 1029-1048, 2009.  [PDF]
  26. G. Zhang, S. Ferrari, and M. Qian, “Information Roadmap Method for Robotic Sensor Path Planning,” Journal of Intelligent and Robotic Systems, Vol. 56, pp. 69-98, 2009.  [PDF]
  27. S. Ferrari, R. Fierro, B. Perteet, C. Cai, and K. C. Baumgartner, “A Geometric Optimization Approach to Detecting and Intercepting Dynamic Targets Using a Mobile Sensor Network,” SIAM Journal on Control and Optimization, Vol.48, No. 1, pp. 292-320, 2009.  [PDF]
  28. C. Cai and S. Ferrari, “Information-Driven Sensor Path Planning by Approximate Cell Decomposition,” IEEE Transactions on Systems, Man, and Cybernetics – Part B, Vol. 39, No. 3, pp. 672-689, 2009.  [PDF]
  29. S. Ferrari and C. Cai, “Information-Driven Search Strategies in the Board Game of CLUE®,” IEEE Transactions on Systems, Man, and Cybernetics – Part B, Vol. 39, No. 3, pp. 607-625, 2009.  [PDF]
  30. S. Ferrari, “Multi-Objective Algebraic Synthesis of Neural Control Systems by Implicit Model Following,” IEEE Transactions on Neural Networks, Vol. 20, No. 3, pp. 406-419, 2009.  [PDF]
  31. S. Ferrari, J. E. Steck, and R. Chandramohan, “Adaptive Feedback Control by Constrained Approximate Dynamic Programming,” IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics, Vol. 38, No. 4, pp. 982-987, 2008.  [PDF]
  32. K. C. Baumgartner and S. Ferrari, “A Geometric Approach to Analyzing Track Coverage in Sensor Networks”, IEEE Transactions on Computer, Vol. 57, No. 8, pp. 1113-1128, 2008.  [PDF]
  33. K. C. Baumgartner, S. Ferrari, and G. Palermo, “Constructing Bayesian Networks for Criminal Profiling from Limited Data,” Knowledge-Based Systems, Vol. 21, No. 7, pp. 563-572, 2008.  [PDF]
  34. S. Ferrari, K. C. Baumgartner, G. Palermo, R. Bruzzone, and M. Strano, “Network Models of Criminal Behavior: Comparing Bayesian and Neural Networks for Decision Support in Criminal Investigations,” IEEE Control Systems Magazine, Vol. 28, No. 4, pp. 65-77, 2008.  [PDF]
  35. S. Ferrari and M. Jensenius, “A Constrained Optimization Approach to Preserving Prior Knowledge During Incremental Training,” IEEE Transactions on Neural Networks, Vol. 19, No. 6, 2008.  [PDF]
  36. S. Ferrari and A. Vaghi, “Demining Sensor Modeling and Feature-level Fusion by Bayesian Networks,” IEEE Sensors Journal, Vol. 6, No. 2, pp. 471-483, 2006.  [PDF]
  37. S. Ferrari and R. F. Stengel, “Smooth Function Approximation by Neural Networks,” IEEE Transactions on Neural Networks, Vol. 16, No. 1, pp. 24-38, 2005.  [PDF]
  38. S. Ferrari and R. F. Stengel, “On-line Adaptive Critic Flight Control,” Journal of Guidance, Control, and Dynamics, Vol. 27, No. 5, pp. 777-786, 2004.  [PDF]
  39. S. Ferrari and R. F. Stengel, “Classical/Neural Synthesis of Nonlinear Control Systems,” Journal of Guidance, Control, and Dynamics, Vol. 25, No. 3, pp. 442-448, 2002.  [PDF]

Books and Book Chapters

  • S. Ferrari and T. A. Wettergren, Information-driven Planning and Control, (under contract) CRC Press, to appear in 2016.
  • S. Ferrari, K. Rudd, and G. Di Muro, “A Constrained Backpropagation (CPROP) Approach to Function Approximation and Approximate Dynamic Programming,” Reinforcement Learning and Approximate Dynamic Programming for Feedback Control, Eds. Frank Lewis and Derong Liu, 2012.
  • S. Ferrari and R. F. Stengel (2004), “Model-based Adaptive Critic Designs,” Learning and Approximate Dynamic Programming, J. Si, A. Barto, W. Powell, Eds., John Wiley and Sons.  [PDF]
  • Y. Crispin and S. Ferrari, “Adaptive Control of Chaos Induced Capsizing of a Ship,” in Intelligent Engineering Systems through Artificial Neural Networks, Vol. 5, Fuzzy Logic and Evolutionary Progr., C.H. Dagli et. al, Eols, ASME Press, NY, 1995.

Peer-Reviewed Conference Proceedings

  1. T. S. Clawson, S. B. Fuller, R. J. Wood, S. Ferrari “A Blade Element Approach to Modeling Aerodynamic Flight of an Insect-scale Robot,” Proc. of the American Control Conference, Seattle, WA, May 2017, in press. [PDF]
  2. P. Zhu, J. Morelli, S. Ferrari, “Value Function Approximation for the Control of Multiscale Dynamical Systems,” Proc. of the IEEE Conference on Decision and Control, Las Vegas, NV, pp. 5471-5477, December 2016. [PDF]
  3. T. S. Clawson, S. Ferrari, S. B. Fuller, R. J. Wood, “Spiking Neural Network (SNN) Control of a Flapping Insect-scale Robot,” Proc. of the IEEE Conference on Decision and Control, Las Vegas, NV, pp. 3381-3388, December 2016. [PDF]
  4. V. Hernandez-Bennetts, A. J. Lilienthal, E. Schaernicht, S. Ferrari, and J. Albertson, “Integrated Simulation of Gas Dispersion and Mobile Sensing Systems,” Workshop on Realistic, Rapid and Repeatable Robot Simulation (R4SIM) at RSS, Rome, Italy, July 2015.  [PDF]
  5. P. Zhu, H. Wei, W. Lu, and S. Ferrari, “Multi-kernel probability distribution regressions,” Proc. of the International Joint Conference on Neural Networks (IJCNN), pp. 1-7, 2015.  [PDF]
  6. D. Hu, X. Zhang, Z. Xu, S. Ferrari, and P. Mazumder, “Digital implementation of a spiking neural network (SNN) capable of spike-timing-dependent plasticity (STDP) learning,” Proc. of the IEEE 14th International Conference on Nanotechnology (IEEE NANO), pp. 873-876, 2014.
  7. H. Wei, W. Lu, P. Zhu, G. Huang, J. Leonard, S. Ferrari, “Optimized visibility motion planning for target tracking and localization,” Intelligent Robots and Systems, IEEE/RSJ International Conference on, vol., no., pp.76-82, 14-18 Sept. 2014.  [PDF]
  8. H. Wei, W. Lu, P. Zhu, S. Ferrari, R.H. Klein, S. Omidshafiei, J.P. How, “Cicana control for learning nonlinear target dynamics via Bayesian nonparametric Dirichlet-process Gaussian-process (DP-GP) models,” Intelligent Robots and Systems, IEEE/RSJ International Conference on, vol., no., pp.95-102, 14-18 Sept. 2014.  [PDF]
  9. X. Zhang, Z. Xu, C. Henriquez, and S. Ferrari, “Spike-Based Indirect Training of a Spiking Neural Network (SNN)-Controlled Virtual Insect,” Proc. of the IEEE Conference on Decision and Control, Florence, Italy, December 2013.  [PDF]
  10. W. Lu and S. Ferrari, “An Approximate Dynamic Programming Approach for Model-free Control of Switched Systems,” Proc. of the IEEE Conference on Decision and Control, Florence, Italy, December 2013.  [PDF]
  11. H. Wei, W. Ross, S. Varisco, P. Krief, and S. Ferrari, “Modeling of Human Driver Behavior via Receding Horizon and Artificial Neural Network Controllers,” Proc. of the IEEE Conference on Decision and Control, Florence, Italy, December 2013.  [PDF]
  12. K. Rudd, G. Foderaro, and S. Ferrari, “A Generalized Reduced Gradient Method for the Optimal Control of Multiscale Dynamical Systems,” Proc. of the IEEE Conference on Decision and Control, Florence, Italy, December 2013.  [PDF]
  13. A. Swingler and S. Ferrari, “On the Duality of Robot and Sensor Path Planning,”Proc. of the IEEE Conference on Decision and Control, Florence, Italy, December 2013.  [PDF]
  14. D. Zielinski, R. McMahan, W. Lu, and S. Ferrari, “Intercept Tags: Enhancing Intercept-based Systems,” Proc. of the 19th ACM Symposium on Virtual Reality Software and Technology, Singapore, October 2013.  [PDF]
  15. P. Cruz, R. Fierro, W. Lu, S. Ferrari, and T. A. Wettergren, “Maintaining Robust Connectivity in Heterogeneous Robotic Networks,” Proc. of SPIE, Conference on Unmanned Systems Technology, Session on Intelligent Behaviors, Baltimore, MD, April 2013.  [PDF]
  16. D. Zielinski, R. McMahan, W. Lu, and S. Ferrari, “ML2VR: Providing MATLAB Users an Easy Transition to Virtual Reality and Immersive Interactivity,” IEEE Virtual Reality Conference, Orlando, FL, March 2013.  [PDF]
  17. H. Wei, W. Lu, and S. Ferrari, “An Information Value Function for Nonparametric Gaussian Processes,” Proc. Neural Information Processing Systems Conference, Lake Tahoe, NV, December 2012.  [PDF]
  18. G. Foderaro, S. Ferrari, and M. Zavlanos, “A Decentralized Kernel Density Estimation Approach to Distributed Robot Path Planning,” Proc. Neural Information Processing Systems Conference, Lake Tahoe, NV, December 2012.
  19. G. Foderaro, A. Swingler, and S. Ferrari, “A Model-based Cell Decomposition Approach to Online Pursuit-Evasion Path Planning and the Video Game Ms. Pac-Man,” Proc. IEEE Conference on Computational Intelligence and Games, Granada, Spain, September 2012, pp. 281-287.  [PDF]
  20. G. Zhang, S. Ferrari, and W. Lu, “A Comparison of Information Theoretic Functions for Tracking Maneuvering Targets,” invited paper, Proc. IEEE Statistical Signal Processing Workshop (SSP), Ann Arbor, MI, August 2012, pp.149-152.  [PDF]
  21. D. Tolic, R. Fierro, and S. Ferrari, “Optimal Self-Triggering for Nonlinear Systems via Approximate Dynamic Programming,” Proc. IEEE Multi-Conference on Systems and Control (MSC), IEEE International Conference on Control Applications (CCA), Dubrovnik, Croatia, October 2012, pp. 879-884.  [PDF]
  22. W. Lu, S. Ferrari, R. Fierro, and T.Wettergren, “Approximate Dynamic Programming (ADP) Recurrence Relationships for a Hybrid Optimal Control Problem,” invited paper, Proc. of SPIE, Vol. 8387 83870C-2, Unmanned Systems Technology XIII, Session on Intelligent Behaviors, Baltimore, MD, April 2012.  [PDF]
  23. W. Lu, H. Wei, and S. Ferrari, “A Kalman-Particle Filter for Estimating the Number and State of Multiple Targets,” Proc. International Conference on Management Sciences and Information Technology, Changsha, China, July 2012.  [PDF]
  24. S. Ferrari, M. Anderson, R. Fierro, and W. Lu, “Cooperative Navigation for Heterogeneous Autonomous Vehicles via Approximate Dynamic Programming,” invited paper, Proc. of the IEEE Conference on Decision and Control, Orlando, FL, December 2011, pp. 121-127.  [PDF]
  25. G. Foderaro, V. Raju, and S. Ferrari, “A Cell Decomposition Approach to Online Evasive Path Planning and the Video Game Ms. Pac-Man,” invited paper, Proc. IEEE Multi-Conference on Systems and Control (MSC), Denver, CO, September 2011.  [PDF]
  26. W. Lu, G. Zhang, S. Ferrari, R. Fierro, and I. Palunko, “An information potential approach for tracking and surveilling multiple moving targets using mobile sensor agents,” Proc. SPIE Conference, Unmanned Systems Technology XIII, Orlando, FL, 2011.  [PDF]
  27. G. Foderaro, C. Henriquez, and S. Ferrari, “Indirect Training of a Spiking Neural Network for Flight Control via Spike-Timing-Dependent Synaptic Plasticity,” Proc. IEEE Conference on Decision and Control, Atlanta, GA, 2010, pp. 911-917.  [PDF]
  28. G. Foderaro and S. Ferrari, “Necessary Conditions for Optimality for a Distributed Optimal Control Problem,” Proc. IEEE Conference on Decision and Control, Atlanta, GA, 2010, pp. 4831- 4838.  [PDF]
  29. B. Bernard and S. Ferrari, “A Geometric Transversals Approach to Track Coverage of Maneuvering Targets,” Proc. IEEE Conference on Decision and Control, Atlanta, GA, 2010, pp. 1243-1249.  [PDF]
  30. A. Swingler and S. Ferrari, “A Cell Decomposition Approach to Cooperative Path Planning and Collision Avoidance,” Proc. IEEE Conference on Decision and Control, Atlanta, GA, 2010, pp. 6329-6336.  [PDF]
  31. S. Ferrari and G. Daugherty, “A Q-Learning Approach to Automated Unmanned Air Vehicle (UAV) Demining,” Proc. SPIE Conference on Security and Sensing, Orlando, FL, 2010.  [PDF]
  32. S. Ferrari, G. Foderaro, and A. Tremblay “A Probability Density Function Approach to Distributed Sensors Path Planning,” Proc. IEEE Conference on Robotics and Automation, Anchorage, Alaska, 2010, pp. 432-439.  [PDF]
  33. S. Ferrari and G. Foderaro, “A Potential Field Approach to Finding Minimum-Exposure Paths in Wireless Sensor Networks,” Proc. IEEE Conference on Robotics and Automation, Anchorage, Alaska, 2010, pp. 335-341.  [PDF]
  34. W. Lu, G. Zhang, and S. Ferrari, “A Randomized Hybrid System Approach to Coordinated Robotic Sensor Planning,” Proc. IEEE Conference on Decision and Control, Atlanta, GA, 2010, pp. 3857-3864.  [PDF]
  35. S. Ferrari, R. Fierro, and D. Tolic, “A Geometric Optimization Approach to Tracking Maneuvering Using a Heterogeneous Mobile Sensor Network,” Proc. IEEE Conference on Decision and Control, Shanghai, China, December 2009.  [PDF]
  36. G. Zhang and S. Ferrari, “An Adaptive Artificial Potential Function Approach for Geometric Sensing,” Proc. IEEE Conference on Decision and Control, Shanghai, China, December 2009.  [PDF]
  37. G. Di Muro and S. Ferrari, “Penalty Function Method for Exploratory Adaptive-Critic Neural Network Control,” Proc. Mediterranean Conference on Control and Automation (MED’09), Thessaloniki, Greece, January 2009, pp. 1410-1414.  [PDF]
  38. D. Tolic, R. Fierro, and S. Ferrari, “Cooperative multi-target tracking via hybrid modeling and geometric optimization,” Proc. Mediterranean Conference on Control and Automation (MED’09), Thessaloniki, Greece, January 2009, pp.440-445.  [PDF]
  39. G. Di Muro and S. Ferrari, “A Constrained Backpropagation Approach to Solving Partial Differential Equations in Nonstationary Environments,” Proc. International Joint Conference on Neural Networks, Atlanta, GA, 2009.  [PDF]
  40. R. Fierro, S. Ferrari, and C. Cai, “An Information-Driven Framework for Motion Planning in Robotic Sensor Networks: Complexity and Experiments,” Proc. IEEE Conference for Decision and Control, Cancun, Mexico, 2008, pp. 483-489.  [PDF]
  41. C. Cai, and S. Ferrari, “A Q-Learning Approach to Developing an Automated Computer Player for the Board Game of CLUE®,” Proc. International Joint Conference on Neural Networks, Hong Kong, 2008, pp. 2347-2353.  [PDF]
  42. G. Di Muro, and S. Ferrari, “A Constrained-Optimization Approach to Training Neural Networks for Smooth Function Approximation and System Identification,” Proc. International Joint Conference on Neural Networks, Hong Kong, 2008, pp.2354-2360.  [PDF]
  43. S. Ferrari, B. Mehta, G. Di Muro, A. M.J. VanDongen, and C. Henriquez, “Biologically Realizable Reward-Modulated Hebbian Training for Spiking Neural Networks,” Proc. International Joint Conference on Neural Networks, Hong Kong, 2008, pp. 1781-1787.  [PDF]
  44. C. Cai, and S. Ferrari, “Bayesian Network Modeling of Acoustic Sensor Measurements,” Proc. IEEE Sensors Conference, Atlanta, GA, 2007, pp. 345-348.  [PDF]
  45. S. Ferrari, C. Cai, R. Fierro, and B. Perteet, “A Multi-Objective Optimization Approach to Detecting and Tracking Dynamic Targets in Pursuit-Evasion Games,” Proc. American Control Conference, New York, NY, 2007, pp. 5316-5321.  [PDF]
  46. K. C. Baumgartner and S. Ferrari, “Optimal Placement of a Moving Sensor Network for Track Coverage,” Proc. American Control Conference, New York, NY, 2007, pp. 4040-4046.  [PDF]
  47. C. Cai and S. Ferrari, “Comparison of Information-Theoretic Functions for Decision Support in Sensor Fusion and Classification,” American Control Conference, New York, NY, 2007, pp. 63-133.  [PDF]
  48. R. Chandramohan, J. Steck, and S. Ferrari, “On the Development of an Adaptive Critic Reconfigurable Flight Controller,” Infotech@Aerospace, April 2007.
  49. C. Cai and S. Ferrari, “On the Development of an Intelligent Computer Player for CLUE®: A Case Study in Preposterior Decision Analysis,” Proc. American Control Conference, Minneapolis, MN, 2006, pp. 4350- 4355.  [PDF]
  50. S. Ferrari, “Track Coverage in Sensor Networks,” Proc. American Control Conference, Minneapolis, MN, 2006, pp.2053-2059.  [PDF]
  51. S. Ferrari and M. Jensenius, “Robust and Reconfigurable Flight Control by Neural Networks,” AIAA 2005-7037, Infotech@Aerospace, Arlington, VA, September 2005.  [PDF]
  52. B. K. Crews, S. Ferrari, and C. G. Salfati, “Bayesian Network Modeling of Offender Behavior for Criminal Profiling,” Proc. IEEE Conference for Decision and Control, Seville, Spain, 2005, pp. 2702-2709.  [PDF]
  53. M. Qian and S. Ferrari, “Control of Distributed Sensors by Dynamic Bayesian Networks,” Proc. SPIE Symposium on Smart Structures and Materials, San Diego, CA, 2005, pp. 85-96.
  54. A. Vaghi and S. Ferrari, “Sensor Network Management by a Graphical Model Approach,” Proc. European Conference on Structural Control, Vienna, Austria, July 2004.
  55. S. Ferrari and R. F. Stengel, “An Adaptive Critic Global Controller,” Proc. American Control Conference, Anchorage, AK, 2002, pp. 2665- 2670.  [PDF]
  56. S. Ferrari and R. F. Stengel, “Algebraic Training of a Neural Network,” Proc. American Control Conference, Arlington, VA, 2001, pp. 1605-1610.  [PDF]
  57. S. Ferrari and R. F. Stengel, “Classical/Neural Synthesis of Nonlinear Control Systems,” Proc. AIAA Guidance, Navigation, and Control Conference, Denver, CO, August 2000.  [PDF]
  58. Y. Crispin and S. Ferrari, “Model-Reference Adaptive Control of Chaos in Periodically Forced Dynamical Systems,” Proc. AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, Bellevue, WA, September 1996.  [PDF]