Fuzzy direct adaptive control for robotic manipulators

Gao, Daoxiang; Wang, Qingchun; Lu, Dunmin

doi:10.1109/icarcv.2010.5707772

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Cited by 3 publications

References 19 publications

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Stable Adaptive Fuzzy Control of Wastewater Treatment Plants

Wang¹

2015

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Stable Adaptive Fuzzy Control of Wastewater Treatment Plants

Wang¹

2015

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A New Neural Network Based Sliding Mode Adaptive Controller for Tracking Control of Robot Manipulator

Pathak¹,

Buragohain²

2021

IJEAT

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In this paper a New RBF Neural Network based Sliding Mode Adaptive Controller (NNNSMAC) for Robot Manipulator trajectory tracking in the presence of uncertainties and disturbances is introduced. The research offers a learning with minimal parameter (LMP) technique for robotic manipulator trajectory tracking. The technique decreases the online adaptive parameters number in the RBF Neural Network to only one, lowering computational costs and boosting real-time performance. The RBFNN analyses the system's hidden non-linearities, and its weight value parameters are updated online using adaptive laws to control the nonlinear system's output to track a specific trajectory. The RBF model is used to create a Lyapunov function-based adaptive control law. The effectiveness of the designed NNNSMAC is demonstrated by simulation results of trajectory tracking control of a 2 dof Robotic Manipulator. The chattering effect has been significantly reduced.

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Sliding Mode with Adaptive Control of Robot Manipulator Trajectory Tracking using Neural Network Approximation

Pathak¹,

Buragohain²

2021

IJEAT

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This paper briefly discusses about the Robust Controller based on Adaptive Sliding Mode Technique with RBF Neural Network (ASMCNN) for Robotic Manipulator tracking control in presence of uncertainities and disturbances. The aim is to design an effective trajectory tracking controller without any modelling information. The ASMCNN is designed to have robust trajectory tracking of Robot Manipulator, which combines Neural Network Estimation with Adaptive Sliding Mode Control. The RBF model is utilised to construct a Lyapunov function-based adaptive control approach. Simulation of the tracking control of a 2dof Robotic Manipulator in the presence of unpredictability and external disruption demonstrates the usefulness of the planned ASMCNN.

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Fuzzy direct adaptive control for robotic manipulators

Cited by 3 publications

References 19 publications

Stable Adaptive Fuzzy Control of Wastewater Treatment Plants

Stable Adaptive Fuzzy Control of Wastewater Treatment Plants

A New Neural Network Based Sliding Mode Adaptive Controller for Tracking Control of Robot Manipulator

Sliding Mode with Adaptive Control of Robot Manipulator Trajectory Tracking using Neural Network Approximation

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