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Information Technology Journal
  Year: 2009 | Volume: 8 | Issue: 6 | Page No.: 895-902
DOI: 10.3923/itj.2009.895.902
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Using Immune Network in Nonlinear System Identification for a 3D Parallel Robot

Pin-Chang Chen

Nonlinear system identification can improve control performance significantly, especially when the system dynamic behaviors are unknown and with great nonlinearity. The concept of immune network simulated the concentration of a set of antibodies. The immune system has the following features: self-organizing, memory, recognition, adaptive and ability of learning. Therefore, immune network could be applied to nonlinear system identification and provided various feasible system models with robust and adaptive characteristics. In this study, a new type of 3D parallel robot arm manipulator with human interface and the parallel motion control of a platform manipulator actuated by three AC servomotors are introduced. To comprehensively realize the performance of the parallel robot, the immune network which theoretically derived for the application of quantified and graphical performance synthesis is presented. Thus, the capability of this parallel robot in its applications as well as its future research and development are approached. The findings of this study should contribute positively to the practice of using immune network to improve the nonlinear system identification and develop a system model with robust and adaptive characteristics.
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How to cite this article:

Pin-Chang Chen , 2009. Using Immune Network in Nonlinear System Identification for a 3D Parallel Robot. Information Technology Journal, 8: 895-902.

DOI: 10.3923/itj.2009.895.902






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