257 citations · 623 across the 37 of their papers we have counts for
39 papers
Tracking-in-range Formulations for Numerical Optimal Control
Nikilesh Ramesh, Eric C. Kerrigan, Yuanbo Nie
In contrast to set-point tracking which aims to reduce the tracking error between the tracker and the reference, tracking-in-range problems only focus on whether the tracker is wit…
Active Flow Control for Bluff Body Drag Reduction Using Reinforcement Learning with Partial Measurements
Chengwei Xia, Junjie Zhang, Eric C. Kerrigan +1
Active flow control for drag reduction with reinforcement learning (RL) is performed in the wake of a 2D square bluff body at laminar regimes with vortex shedding. Controllers para…
Automatic scenario generation for efficient solution of robust optimal control problems
Marta Zagorowska, Paola Falugi, Edward O'Dwyer +1
Existing methods for nonlinear robust control often use scenario-based approaches to formulate the control problem as large nonlinear optimization problems. The optimization proble…
Solving optimal control problems with non-smooth solutions using an integrated residual method and flexible mesh
Lucian Nita, Eric C. Kerrigan, Eduardo M. G. Vila +1
Solutions to optimal control problems can be discontinuous, even if all the functionals defining the problem are smooth. This can cause difficulties when numerically computing solu…
Fast and accurate method for computing non-smooth solutions to constrained control problems
Lucian Nita, Eduardo M. G. Vila, Marta A. Zagorowska +4
Introducing flexibility in the time-discretisation mesh can improve convergence and computational time when solving differential equations numerically, particularly when the soluti…
Automatic Scenario Generation for Robust Optimal Control Problems
Marta Zagorowska, Paola Falugi, Edward O'Dwyer +1
Existing methods for nonlinear robust control often use scenario-based approaches to formulate the control problem as nonlinear optimization problems. Increasing the number of scen…