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Intelligent Control Using Dynamic Neural Networks with Robotic Applications

  • Liang Jin
  • Madan M. Gupta
  • Peter N. Nikiforuk
Part of the International Series in Intelligent Technologies book series (ISIT, volume 3)

Abstract

The intelligent control of systems with complex, unknown, and high nonlinear dynamics, such as robotic systems, chemical engineering processes and space systems, has become a topic of considerable importance during recent years. The most concurrent advances in the area of artificial neural networks (ANNs) have provided the potential for dealing with such a challenging task. In this chapter, some new schemes of dynamic recurrent neural networks (DRNNs) are proposed to design robust learning control systems for a general class of multi-input and multi-output (MIMO) nonlinear systems with unknown dynamics. The detailed structure of the DRNNs and their learning capability are first discussed. The synthesis and design methods for the purposes of regulation, tracking and model reference control are then conducted. Based on the DRNNs approaches presented in this chapter, a new torque control scheme for robot manipulators is developed. The potentials of this scheme are demonstrated extensively by simulation studies.

Keywords

Feedback Controller Robotic Application Dynamic Controller Adaptive Control System Dynamic Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Kluwer Academic Publishers 1995

Authors and Affiliations

  • Liang Jin
    • 1
  • Madan M. Gupta
    • 1
  • Peter N. Nikiforuk
    • 1
  1. 1.Intelligent Systems Research Laboratory College of EngineeringUniversity of SaskatchewanSaskatchewanCanada

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