Research on Adaptive Reaction Null Space Planning and Control Strategy Based on VFF–RLS and SSADE–ELM Algorithm for Free-Floating Space Robot

With the increase of on-orbit maintenance and support requirements, the application of a space manipulator is becoming more promising. In actual operation, the strong coupling of the free-floating space robot itself and the unknown disturbance of the contact target caused a major challenge to the ro...

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Vydáno v:Electronics (Basel) Ročník 8; číslo 10; s. 1111
Hlavní autoři: Ye, Xin, Dong, Zheng-Hong, Hong, Jia-Cai
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.10.2019
Témata:
Adaptation
Adaptive control
Artificial neural networks
Construction planning
Control algorithms
Control methods
Dynamic models
Evolutionary algorithms
Evolutionary computation
Machine learning
Motion planning
Neural networks
Parameter identification
Path planning
Path tracking
Planning
Robot control
Robots
Robust control
Space robots
Strategy
ISSN:2079-9292, 2079-9292
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Abstract With the increase of on-orbit maintenance and support requirements, the application of a space manipulator is becoming more promising. In actual operation, the strong coupling of the free-floating space robot itself and the unknown disturbance of the contact target caused a major challenge to the robot base posture control. Traditional Reaction Null Space (RNS) motion planning and control methods require the construction of precise dynamic models, which is impossible in reality. In order to solve this problem, this paper proposes a new Adaptive Reaction Null Space (ARNS) path planning and control strategy for the contact of free-floating space robots with unknown targets. The ARNS path planning strategy is constructed by the Variable Forgetting Factor Recursive Least Squares (VFF–RLS) algorithm. At the same time, a robust adaptive control strategy based on the Strategy Self-Adaption Differential Evolution–Extreme Learning Machine (SSADE–ELM) algorithm is proposed to track the dynamic changes of the planned path. The algorithm enables us to intelligently learn and compensate for the unknown disturbance. Then, this paper constructs a robust controller to compensate model uncertainty. A striking feature of the proposed strategy is that it does not require an accurate system model or any information about unknown attributes. This design can dynamically implement RNS path tracking performance. Finally, through simulation and experiment, the proposed algorithm is compared with the existing methods to prove its effectiveness and superiority.
AbstractList With the increase of on-orbit maintenance and support requirements, the application of a space manipulator is becoming more promising. In actual operation, the strong coupling of the free-floating space robot itself and the unknown disturbance of the contact target caused a major challenge to the robot base posture control. Traditional Reaction Null Space (RNS) motion planning and control methods require the construction of precise dynamic models, which is impossible in reality. In order to solve this problem, this paper proposes a new Adaptive Reaction Null Space (ARNS) path planning and control strategy for the contact of free-floating space robots with unknown targets. The ARNS path planning strategy is constructed by the Variable Forgetting Factor Recursive Least Squares (VFF–RLS) algorithm. At the same time, a robust adaptive control strategy based on the Strategy Self-Adaption Differential Evolution–Extreme Learning Machine (SSADE–ELM) algorithm is proposed to track the dynamic changes of the planned path. The algorithm enables us to intelligently learn and compensate for the unknown disturbance. Then, this paper constructs a robust controller to compensate model uncertainty. A striking feature of the proposed strategy is that it does not require an accurate system model or any information about unknown attributes. This design can dynamically implement RNS path tracking performance. Finally, through simulation and experiment, the proposed algorithm is compared with the existing methods to prove its effectiveness and superiority.
Author Hong, Jia-Cai
Ye, Xin
Dong, Zheng-Hong
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CitedBy_id crossref_primary_10_1016_j_actaastro_2024_08_015
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Snippet With the increase of on-orbit maintenance and support requirements, the application of a space manipulator is becoming more promising. In actual operation, the...
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SubjectTerms Adaptation
Adaptive control
Artificial neural networks
Construction planning
Control algorithms
Control methods
Dynamic models
Evolutionary algorithms
Evolutionary computation
Machine learning
Motion planning
Neural networks
Parameter identification
Path planning
Path tracking
Planning
Robot control
Robots
Robust control
Space robots
Strategy
Title Research on Adaptive Reaction Null Space Planning and Control Strategy Based on VFF–RLS and SSADE–ELM Algorithm for Free-Floating Space Robot
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