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Redundancy Resolution with Periodic Input Disturbance

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Deep Reinforcement Learning with Guaranteed Performance

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 265))

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Abstract

Input disturbances and physical constraints are important issues in the kinematic control of redundant manipulators. In this chapter, we present a novel recurrent neural network to simultaneously address the periodic input disturbance, joint angle constraint, and joint velocity constraint, and optimize a general quadratic performance index. The presented recurrent neural network applies to both regulation and tracking tasks. Theoretical analysis shows that, with the presented neural network, the end-effector tracking and regulation errors asymptotically converge to zero in the presence of both input disturbance and the two constraints. Simulation examples and comparisons with an existing controller are also presented to validate the effectiveness and superiority of the presented controller.

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Author information

Authors and Affiliations

  1. College of Cyber Security, Jinan University, Guangzhou, China

    Yinyan Zhang

  2. School of Information Science and Engineering, Lanzhou University, Lanzhou, China

    Shuai Li

  3. Guangdong Institute of Intelligent Manufacturing, Guangdong Academy of Science, Guangzhou, China

    Xuefeng Zhou

Authors
  1. Yinyan Zhang
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  2. Shuai Li
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  3. Xuefeng Zhou
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Correspondence to Shuai Li .

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Zhang, Y., Li, S., Zhou, X. (2020). Redundancy Resolution with Periodic Input Disturbance. In: Deep Reinforcement Learning with Guaranteed Performance. Studies in Systems, Decision and Control, vol 265. Springer, Cham. https://doi.org/10.1007/978-3-030-33384-3_7

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