Robust Adaptive Control for Robotic Manipulators Based on RBFNN

Bang Sheng Xing; Wen Hui Zhang

doi:10.4028/www.scientific.net/AMM.397-400.1477

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A Method of Line Structured Light Vision System Calibration Based on Stereo Vision
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Design of Automatic Tracking System Based on FPGA and Binocular Vision
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The Design of the Intelligent EOD Manipulator with Five DOF
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Development of Quality Detecting System for Micro Holes by ECM Based on Machine Vision Technology
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Robotic Curve-Shaped Seam Tracking System Using Vision Sensor
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Fuzzy Control for Underactuated Manipulator
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A Novel Texture Spectrum Descriptor
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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 397-400Robust Adaptive Control for Robotic Manipulators...

Robust Adaptive Control for Robotic Manipulators Based on RBFNN

Abstract:

The rigid robotic manipulators is used in the mining industry more and more widely. An adaptive robust control algorithm of robotic manipulators based on radial basis function neural network (RBFNN) is proposed by the paper. Neural network controller is used to adaptive learn and compensate the unknown system, approach errors as disturbance are eliminated by robust controller. The weight adaptive laws on-line based on Lyapunov theory is designed. The robust controller was proposed based on H theory. Above these assured the stability of the whole system, and L2 gain also was less than the index. This control scheme possesses great control accuracy and dynamic function. The simulation results show that the presented neural network control algorithm is effective.

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Periodical:

Applied Mechanics and Materials (Volumes 397-400)

Pages:

1477-1481

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.397-400.1477

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Online since:

September 2013

Authors:

Bang Sheng Xing, Wen Hui Zhang

Keywords:

Adaptive Control, Neural Network (NN), Robotic Manipulators, Robust Control

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