Fuzzy Sliding Mode Controller with RBF Neural Network for Robotic Manipulator Trajectory Tracking

2006 ◽  
pp. 527-532
Author(s):  
Ayca Gokhan Ak ◽  
Galip Cansever
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Robotic Manipulator ◽  
Sliding Mode Controller ◽  
Fuzzy Sliding Mode

Fuzzy Sliding Mode Control for a Three-Links Spatial Robot Based on RBF Neural Network

2011 ◽  
Vol 141 ◽  
pp. 303-307 ◽  
Author(s):  
Sheng Bin Hu ◽  
Min Xun Lu
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Fuzzy Controller ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Sliding Mode Controller ◽  
System A ◽  
Radial Basis ◽  
Fuzzy Sliding Mode

To achieve the tracing control of a three-links spatial robot, a adaptive fuzzy sliding mode controller based on radial basis function neural network is proposed in this paper. The exponential sliding mode controller is divided into two parts: equivalent part and exponential corrective part. To realize the control without the model information of the system, a radial basis function neural network is designed to estimate the equivalent part. To diminish the chattering, a fuzzy controller is designed to adjust the corrective part according to sliding surface. The simulation studies have been carried out to show the tracking performance of a three-links spatial robot. Simulation results show the validity of the control scheme.

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

2021 ◽  
Vol 11 (2) ◽  
pp. 12-16
Author(s):  
Monisha Pathak ◽  
◽  
Mrinal Buragohain ◽  
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Rbf Neural Network ◽  
Adaptive Controller ◽  
Robotic Manipulator ◽  
Trajectory Tracking Control ◽  
Chattering Effect

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.

Trajectory tracking control of robot manipulator based on RBF neural network and fuzzy sliding mode

2017 ◽  
Vol 22 (S3) ◽  
pp. 5799-5809 ◽  
Author(s):  
Fei Wang ◽  
Zhi-qiang Chao ◽  
Lian-bing Huang ◽  
Hua-ying Li ◽  
Chuan-qing Zhang
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Rbf Neural Network ◽  
Trajectory Tracking Control ◽  
Fuzzy Sliding Mode

Neural Network Sliding Mode Control of 5DOFs Robotic Manipulator

2014 ◽  
Vol 898 ◽  
pp. 514-520 ◽  
Author(s):  
Chang Kai Xu ◽  
Ming Li ◽  
Jian Yin
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Learning Algorithm ◽  
Rbf Neural Network ◽  
Dynamic Performance ◽  
Robotic Manipulator ◽  
Terminal Sliding Mode ◽  
Mode Control ◽  
The Stability

In this paper, a neural network sliding mode controller for a kind of 5DOFs robotic manipulator is proposed. A radial basis function (RBF) neural network is used as an estimator to approximate uncertainties of the system. The learning algorithm of the neural network improves the performance of the system. A globle terminal sliding mode control (GTSMC) is designed to guarantee the stability and improve the dynamic performance of the robotic manipulator. Simulation results show that the proposed NNSMC strategy is effective to ensure the robustness and dynamic performance of the 5DOFs robotic manipulator.

Fuzzy Sliding Mode Controller for a Flexible Single-Link Robotic Manipulator

2005 ◽  
Vol 11 (2) ◽  
pp. 295-314 ◽  
Author(s):  
B. A. Bazzi ◽  
N. G. Chalhoub
Keyword(s):  
Fuzzy Inference ◽  
Sliding Mode ◽  
Angular Displacement ◽  
Robotic Manipulator ◽  
Sliding Mode Controller ◽  
Control Parameters ◽  
Single Link ◽  
Structural Deformations ◽  
Fuzzy Sliding Mode ◽  
Inference Systems

Two robust non-linear controllers have been developed in this study to control the rigid and flexible motions of a single-link robotic manipulator. The controllers consist of a conventional sliding mode controller (CSMC) and a fuzzy sliding mode controller (FSMC). The effects of fuzzy-tuning some of the CSMC control parameters on the overall performance of the arm have been investigated in this study. Furthermore, the proposed FSMC, whose parameters are determined by fuzzy inference systems, has been designed herein based on two Lyapunov functions. The rationale is to considerably reduce the momentum of the system before entering the boundary layer neighboring the sliding surface. This will significantly attenuate the structural deformations of the arm. The digital simulations have demonstrated that the structural deformations, incurred by the beam at the onset of its movement, can be significantly reduced by fuzzy-tuning some of the control parameters. Furthermore, the results have illustrated the superiority of the FSMC over the CSMC in producing a less oscillatory and more accurate response of the angular displacement at the base joint, in damping out the unwanted vibrations of the beam, and in requiring significantly smaller control torques.

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