A Randomized approach to the H2/H? Control Problem via Q-parameterization

We show that the mixed H2/H? control problem can be efficiently solved using randomized algorithms. Q-parameterization provides a mechanism to search over all stabilizing controllers, and thus gives us the ability to search for H2 minimizing controllers, while still providing stability robustness. Finally, we are able to show that we can get results comparable to a more traditional approach such as gradient search, but in addition, we can solve more complex problems. With very little modification, we are able to deal with multiple objectives, plant uncertainty, and fixed order controllers

Model-Based Control Using Koopman Operators

This paper explores the application of Koopman operator theory to the control of robotic systems. The operator is introduced as a method to generate data-driven models that have utility for model-based control methods. We then motivate the use of the Koopman operator towards augmenting model-based control. Specifically, we illustrate how the operator can be used to obtain a linearizable data-driven model for an unknown dynamical process that is useful for model-based control synthesis. Simulated results show that with increasing complexity in the choice of the basis functions, a closed-loop controller is able to invert and stabilize a cart- and VTOL-pendulum systems. Furthermore, the specification of the basis function are shown to be of importance when generating a Koopman operator for specific robotic systems. Experimental results with the Sphero SPRK robot explore the utility of the Koopman operator in a reduced state representation setting where increased complexity in the basis function improve open- and closed-loop controller performance in various terrains, including sand.Comment: 8 page