Redundancy Resolution with Periodic Input Disturbance

  • Yinyan Zhang
  • Shuai LiEmail author
  • Xuefeng Zhou
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 265)


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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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.College of Cyber SecurityJinan UniversityGuangzhouChina
  2. 2.School of Information Science and EngineeringLanzhou UniversityLanzhouChina
  3. 3.Guangdong Institute of Intelligent ManufacturingGuangdong Academy of ScienceGuangzhouChina

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