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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. An overview of the motivation and system development of the self-mobile space modulator (SM/sup 2/) is given. 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 single stochastic training scheme is presented. A recurrent neural network architecture with improved performance is proposed. Using the proposed learning scheme, the manipulator tracking error is reduced by 85% compared to that of conventional proportional-integral-derivative (PID) control. The approach possesses a high degree of generality and adaptability to various applications. It will be a valuable learning control method for robots working in unconstructed 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, "Real-Time Implementation of Neural Network Learning Control in a Flexible Space Manipulator," IEEE International Conference on Robotics and Automation (ICRA '93), May, 1993, pp. 135 - 141.

BibTeX Reference

@inproceedings{Xu_1993_1358,
   author = "R.T. Newton and Yangsheng Xu",
   title = "Real-Time Implementation of Neural Network Learning Control in a Flexible Space Manipulator",
   booktitle = "IEEE International Conference on Robotics and Automation (ICRA '93)",
   pages = "135 - 141",
   month = "May",
   year = "1993",
   volume = "1",
}

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