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Carnegie Mellon Robotics Institute

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Abstract

A neural network approach to online learning control and real-time implementation for a flexible space robot manipulator is presented. Motivation for and system development of the Self-Mobile Space Manipulator (SM/sup 2/) are discussed. The neural network learns control by updating feedforward dynamics based on feedback control input. Implementation issues associated with online training strategies are addressed, and a simple stochastic training scheme is presented. A recurrent neural network architecture with improved performance is proposed. By using the proposed learning scheme, the manipulator tracking error is reduced by 85% compared to conventional PID control. The approach possesses a high degree of generality and adaptability in various applications and will be a valuable method in learning control for robots working in unstructured environments.

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Associated Project(s): Self-Mobile Space Manipulator

Text Reference

R.T. Newton and Yangsheng Xu, "Neural network control of a space manipulator," IEEE Control Systems, Vol. 13, No. 6, December, 1993, pp. 14-22.

BibTeX Reference

@article{Xu_1993_2345,
   author = "R.T. Newton and Yangsheng Xu",
   title = "Neural network control of a space manipulator",
   journal = "IEEE Control Systems",
   pages = "14-22",
   month = "December",
   year = "1993",
   volume = "13",
   number = "6",
}

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