Neural network-based reconfiguration control for spacecraft formation in obstacle environmentsZhou, N., Chen, R., Xia, Y., Huang, J. & Wen, G., 1-Apr-2018, In : International Journal of Robust and Nonlinear Control. 28, 6, p. 2442-2456 15 p.
Research output: Contribution to journal › Article › Academic › peer-review
This paper proposes an adaptive formation reconfiguration control scheme based on the leader-follower strategy for multiple spacecraft systems. By taking the predesigned desired velocities and the trajectories as reference signals, a set of coordination tracking controllers is constructed by combining the reconstructed dynamic system and the neural network-based reconfiguration algorithm together. To avoid collisions between spacecraft and obstacles during the formation configuration process, the null space-based behavioral control is integrated into the control design. Since the spacecraft dynamics contains unknown nonlinearity and disturbance, it is challenging to make the system robust to uncertainties and improve the control precision simultaneously. To solve this problem, both the adaptive neural network strategy and the finite-time control theory are employed. Finally, 2 simulation examples are carried out to verify the proposed algorithm, showing that the formation reconfiguration task can be executed successfully while achieving high control precision.
|Number of pages||15|
|Journal||International Journal of Robust and Nonlinear Control|
|Publication status||Published - 1-Apr-2018|
- coordination control, finite-time control, formation reconfiguration, modeling uncertainties, neural network, obstacle avoidance, ATTITUDE COORDINATION CONTROL, FINITE-TIME CONTROL, CONSENSUS CONTROL, ADAPTIVE-CONTROL, TRACKING CONTROL, CONTROL DESIGN, SYNCHRONIZATION, SYSTEMS, STATE