CMAC neural network method with application to kinematics control of a redundant manipulator

Yangmin Li, Sio Hong Leong

Research output: Journal article publicationJournal articleAcademic researchpeer-review


The inverse kinematics problems of redundant manipulators have been investigated for many years. The conventional method of solving this problem is through applying the Jacobian Pseudoinverse Algorithm, which is effective and able to resolve the redundancy for a redundant manipulator. However, computational effort makes it not suitable for real time control. Recently, neural networks have been widely used in robotic control because they are fast, fault-tolerant and able to learn. In this paper, we will present the application of CMAC (Cerebellar Model Articulation Controller) neural network for solving the inverse kinematics problems in real time. Simulations have been carried out for a five-link manipulator in order to evaluate the performance of the CMAC neural network. Through computer simulation, we found CMAC NN method is especially suitable for real time control of robots and solving nonlinear function approximation problem.
Original languageEnglish
Pages (from-to)7-14
Number of pages8
JournalInternational Journal for Engineering Modelling
Issue number1-4
Publication statusPublished - 1 Dec 2001
Externally publishedYes


  • Inverse kinematic problem
  • Neural network method
  • Real time control
  • Redundant manipulator

ASJC Scopus subject areas

  • Food Science
  • Applied Mathematics


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