Learning and Control

Optimization and control theory deal with different aspects of the following problem: given that certain parameters or inputs for a static or dynamic system are available for design,  how should they be selected such that the resulting “closed loop” or optimized system has the desired properties? The mathematics of such an intuitive problem that seems to be at the heart of all engineering sciences, rather surprising, span a lot of distinct mathematical disciplines, from analysis and optimization, to algebra and combinatorics. Our group’s research has worked on the applications of optimization theory to problems in feedback design and estimation.

                  

More recently, we have been examining the areas of data-guided control and online optimization both for single and distributed systems. This research aims to systematically combine key aspects of data science, machine learning, and control to address theoretical and applied aspects of how to go from data to models and decisions. Applications of such data-guided decision-making in aerospace, energy optimization, transportation, recovery for infrastructure networks, and biological networks are currently of great interest.

Project Sponsors: NSF, ONR, Boeing, ARO/MURI

Recent Publications:

  • A. D. González, A. Chapman, L. Dueñas-Osorio, M. Mesbahi and R. M. D’Souza, Efficient Infrastructure Restoration Strategies using the Recovery Operator, Computer-Aided Civil and Infrastructure Engineering (to appear).
  • S. Vasisht and M. Mesbahi, A Data-driven Approach for UAV Tracking Control, ASME Dynamic Systems and Control Conference, 2017.
  • M. Hudoba de Badyn and M. Mesbahi, Large Scale Distributed Kalman Filtering via an Optimization Approach, IFAC World Congress, 2017.
  • D. Meng, R. Eghbali, M. Fazel, and M. Mesbahi, Online Algorithms for Network Formation, IEEE Conference on Decision and Control, 2016.
  • S. Hosseini, A. Chapman, and M. Mesbahi, Online distributed optimization on dynamic networks, IEEE Transactions on Automatic Control, 61 (11): 3545 – 3550, 2016
  • D. Meng, M. Fazel, and M. Mesbahi, Proximal alternating direction method of multipliers for distributed optimization on weighted graphs, IEEE Conference on Decision and Control, 2015.
  • S. Hosseini, A. Chapman, and M. Mesbahi, “Online Distributed Optimization via Dual Averaging,”. In Proc. of the 52nd IEEE Conference on Decision and Control, 2013.
  • S. Hosseini and M. Mesbahi, “Energy aware aerial surveillance for a long endurance solar-powered UAV,” AIAA Guidance, Navigation and Control Conference., 2013.
  • S. Hosseini, R. Dai, and M. Mesbahi, “Optimal path planning and power allocation for a long endurance solar-powered UAV,” American Control Conference, 2013.