The partitioned adaptive control and vibration suppression of free-floating space robot with flexible arms in post-impact process are studied. At first, the dynamic model of combination system after flexible space robot system capturing a target system is established based on the collision theory; the impact effect of space robot combination system after capture operation is analyzed at the same time. Secondly, based on the double time scale decomposition theory, the unstable combination system is decomposed into fast system and slow system, representing the rigid motion of the system and the flexible vibration respectively. To satisfy the compute capacity of space-borne computer and modular design concept, the slow system is considered as a set of interconnected subsystems and a decentralized adaptive neural network control scheme is designed. Neural network is applied to approximating the unknown dynamic of the subsystems; an adaptive sliding mode controller is designed to eliminate both interconnection term and approximation error. The control algorithm has a cutting edge in independent control signal and reduced calculation amount. The Linear Quadratic Optimal control scheme is designed for fast system to suppress the elastic vibration of the flexible manipulators. At last, numerical example demonstrates the validity of the proposed composite control scheme.
- Dynamic Systems and Control Division
Partitioned Adaptive Control Based on Neural Network of a Flexible Space Robot After Capture Operation
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Cheng, J, Chen, L, Liang, J, & Ma, W. "Partitioned Adaptive Control Based on Neural Network of a Flexible Space Robot After Capture Operation." Proceedings of the ASME 2018 Dynamic Systems and Control Conference. Volume 1: Advances in Control Design Methods; Advances in Nonlinear Control; Advances in Robotics; Assistive and Rehabilitation Robotics; Automotive Dynamics and Emerging Powertrain Technologies; Automotive Systems; Bio Engineering Applications; Bio-Mechatronics and Physical Human Robot Interaction; Biomedical and Neural Systems; Biomedical and Neural Systems Modeling, Diagnostics, and Healthcare. Atlanta, Georgia, USA. September 30–October 3, 2018. V001T01A011. ASME. https://doi.org/10.1115/DSCC2018-9167
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