Iterative learning control of spacecraft proximity operations based on confidence level
This paper addresses the problem of trajectory tracking control for small free-flyer spacecraft in proximity operations. The proposed approach consists in a current output-feedback iterative learning control law that combines a causal output feedback and a non-caucal feed- forward learning component. A new confidence level factor is introduced in the algorithm to carefully, and iteratively, transition learning from a conservative to a confident process. Simulations and experiments performed at Carleton University's Spacecraft Robotics and Control Laboratory demonstrate the performance of the new confidence-based learning approach in a spacecraft robotic inspection maneuver scenario. Results indicate that the iterative learning controller is successful at achieving accurate tracking performance for a fast trajectory without saturating the low-thurst actuators onboard the spacecraft.
|Conference||AIAA Guidance, Navigation, and Control Conference, 2017|
Ulrich, S, & Hovell, K. (Kirk). (2017). Iterative learning control of spacecraft proximity operations based on confidence level. In AIAA Guidance, Navigation, and Control Conference, 2017.