Model-Based Control Using Koopman Operators
It addresses control challenges for robotic systems, but the approach appears incremental as it builds on existing Koopman operator methods.
This paper tackles the problem of controlling robotic systems by applying Koopman operator theory to generate data-driven models for model-based control, showing that increasing basis function complexity enables stabilization of cart- and VTOL-pendulum systems in simulations and improves controller performance on a Sphero SPRK robot in various terrains like sand.
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.