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A Novel Recurrent Neural Network for Robot Control

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Robot Control and Calibration

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

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Abstract

To date, neural networks with high learning ability have been widely used in natural language processing, process control and other fields. In this chapter, a new recurrent neural network (RNN) is proposed to deal with time-varying underdetermined linear systems with disturbances, thereby achieving better control results. The related background of the underdetermined linear system is described in Sect. 3.1. In Sect. 3.2, we introduce the problem description. The theoretical analysis is discussed in Sect. 3.3. The experimental results are presented in Sect. 3.4. Finally, the conclusions and future research work are given in Sect. 3.5.

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Authors and Affiliations

  1. College of Computer and Information Science, Southwest University, Chongqing, Chong Qing, China

    Xin Luo

  2. School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, Chong Qing, China

    Zhibin Li

  3. Department of Computer Science, Lanzhou University, Lanzhou, Gansu, China

    Long Jin

  4. Faculty of Information Technology and Electrical Engineering and VTT-Technical Research Centre of Finland, University of Oulu, Oulu, Oulu, Finland

    Shuai Li

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  1. Xin Luo
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  2. Zhibin Li
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  3. Long Jin
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  4. Shuai Li
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Luo, X., Li, Z., Jin, L., Li, S. (2023). A Novel Recurrent Neural Network for Robot Control. In: Robot Control and Calibration. SpringerBriefs in Computer Science. Springer, Singapore. https://doi.org/10.1007/978-981-99-5766-8_3

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  • DOI: https://doi.org/10.1007/978-981-99-5766-8_3

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  • Online ISBN: 978-981-99-5766-8

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