Kinematic Control of Redundant Manipulators Using Neural Networks

Shuai Li, Yunong Zhang, Long Jin

Research output: Journal article publicationJournal articleAcademic researchpeer-review

199 Citations (Scopus)


Redundancy resolution is a critical problem in the control of robotic manipulators. Recurrent neural networks (RNNs), as inherently parallel processing models for time-sequence processing, are potentially applicable for the motion control of manipulators. However, the development of neural models for high-accuracy and real-time control is a challenging problem. This paper identifies two limitations of the existing RNN solutions for manipulator control, i.e., position error accumulation and the convex restriction on the projection set, and overcomes them by proposing two modified neural network models. Our method allows nonconvex sets for projection operations, and control error does not accumulate over time in the presence of noise. Unlike most works in which RNNs are used to process time sequences, the proposed approach is model-based and training-free, which makes it possible to achieve fast tracking of reference signals with superior robustness and accuracy. Theoretical analysis reveals the global stability of a system under the control of the proposed neural networks. Simulation results confirm the effectiveness of the proposed control method in both the position regulation and tracking control of redundant PUMA 560 manipulators.
Original languageEnglish
Article number7499812
Pages (from-to)2243-2254
Number of pages12
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number10
Publication statusPublished - 1 Oct 2017


  • Kinematic control
  • neural network
  • nonconvex set
  • recurrent neural networks (RNNs)
  • redundant manipulator
  • robot arm

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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