All dynamical systems are prone to exogenous disturbances, and the uncertainty introduced by these exogenous disturbances propagates along with the system states. More often, the amount of uncertainty in the system grows with time as the system evolves and, consequently, controlling the uncertainty is of paramount interest to maintain a certain level of performance. This is especially true when one needs to design optimal controllers, which are known to be susceptible to modeling errors. Recent advances have it possible to directly quantify and control the uncertainty of a dynamical system. Controlling the uncertainty of a dynamical system implies the ability to control the state distribution over time, a problem that has many applications, including image segmentation, ensemble and swarm control, control of particle beams, neuronal ensembles, and many others — in addition to just reducing the uncertainty in a feedback system.
The objective of this workshop is twofold: the first objective is to report on current advances in the area of uncertainty quantification and control to enable resilient and robust operation of dynamical systems and swarms of robots; the second objective is to bring together - in the same room - outstanding researchers from leading institutions who have contributed on this topic over the years.
The target audience includes graduate students and researchers in control, robotics, computer scientists, physicists and engineers working on the modeling and control of uncertain systems. The topics covered in the talks of this session will be particularly useful to students and researchers working on stochastic optimal control, stochastic model predictive control, and trajectory optimization problems for uncertain systems (e.g., autonomous robotic systems operating in dynamic environments).