Hybrid seminar at McGill University or Zoom.

Receding horizon optimal control problems compute the solution at each time step to operate the system on a near-optimal path. However, in many practical cases, the boundary conditions, such as external inputs, constraint equations, or the objective function, vary only marginally from one time step to the next. In this case, recomputing the optimal solution at each time represents a significant burden for real-time applications.

In this talk, we will discuss several approaches to solve perturbed constrained dynamic programs that significantly improves the computational burden when the dynamical equation, the objective function, and/or the constraints are perturbed slightly. The method hinges on determining closed-form expressions for first-order perturbations in the optimal strategy and the Lagrange multipliers of the perturbed constrained dynamic programming problem. We demonstrate through numerical simulations as well as hardware implementations that our algorithms significantly outperforms existing methods. Commercialization activities associated with the theoretical framework developed is also discussed towards the end of the talk.

Bio: Abhishek Gupta received the B.Tech. degree in aerospace engineering from the Indian Institute of Technology, Bombay, Mumbai, India, in 2009, the M.S. degree in applied mathematics from the University of Illinois at Urbana-Champaign (UIUC), Champaign, IL, USA, in 2012, and the M.S. and Ph.D. degrees in aerospace engineering from UIUC, in 2014. He is currently an Associate Professor with the Electrical and Computer Engineering Department, The Ohio State University, Columbus, OH, USA. His research interests include stochastic control theory, probability theory, and game theory with applications to transportation markets, electricity markets, and cybersecurity of control systems. He is the recipient of Lumley Research Award from The Ohio State University in 2019.