Relative Pose Neural Network Control of Autonomous Space Proximity Missions
- Resource Type
- Conference
- Authors
- Sun, Liang; Liu, Yuanji; He, Wei
- Source
- 2019 IEEE 4th International Conference on Advanced Robotics and Mechatronics (ICARM) Advanced Robotics and Mechatronics (ICARM), 2019 IEEE 4th International Conference on. :605-610 Jul, 2019
- Subject
- Robotics and Control Systems
Space vehicles
Biological neural networks
Couplings
Aerospace electronics
Uncertainty
Conferences
- Language
Relative pose motion control of an uncertain controlled chaser approaching to a space target is investigated. A nonlinear control method augmented by neural networks for relative pose tracking is developed. An unknown nonlinear function including mass and inertia uncertainties, unknown couplings and disturbances is approximated by neural networks, and an output vector of neural networks is used as a robust controller to compensate the unknown function. The uniformly ultimately bounded convergence of the system states is proved by Lyapunov stability theory. The effectiveness of the proposed control law is validated by numerical simulations.