Synthetic tracking controller for robot manipulator with flexible joints, dynamics of executive motors and affect of disturbance based on radial basic function (RBF) neural network

2014 ◽  
Author(s):  
Tien Thanh Nguyen ◽  
Hai Van Nguyen ◽  
Tien Vu Hoa
Keyword(s):  
Neural Network ◽  
Robot Manipulator ◽  
Rbf Neural Network ◽  
Basic Function ◽  
Flexible Joints ◽  
Tracking Controller

The Evaluation Model of the Hydropower Project Financing Risk Based on AHP-RS and RBF Neural Network

2011 ◽  
Vol 474-476 ◽  
pp. 2243-2246 ◽  
Author(s):  
Hui Zhao ◽  
Li Ming Chen
Keyword(s):  
Neural Network ◽  
Rough Set Theory ◽  
Rbf Neural Network ◽  
Evaluation Model ◽  
Indicator System ◽  
Basic Function ◽  
Hydropower Project ◽  
Project Financing ◽  
Analytic Hierarchy ◽  
Financing Risk

A evaluation model based on the integration of analytic hierarchy process (AHP)-rough set theory (RS) and radial basic function (RBF) neural network is put forward for grasping the hydropower project financing risk. Firstly, the evaluation indicator system is constructed by AHP, then the evaluation indicators are discretized by RS neural network. And then, RBF neural network is used to evaluate the hydropower project financing risk. In order to grasp this evaluation model better, finally, the paper provides an example to demonstrate the application of this evaluation model.

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.

scholarly journals Trajectory Planning of Robot Manipulator Based on RBF Neural Network

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1207
Author(s):  
Qisong Song ◽  
Shaobo Li ◽  
Qiang Bai ◽  
Jing Yang ◽  
Ansi Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Trajectory Planning ◽  
Polynomial Interpolation ◽  
Robot Manipulator ◽  
Rbf Neural Network ◽  
Tracking Error ◽  
Lyapunov Theory ◽  
Degree Of Freedom ◽  
Quintic Polynomial ◽  
High Degree

Robot manipulator trajectory planning is one of the core robot technologies, and the design of controllers can improve the trajectory accuracy of manipulators. However, most of the controllers designed at this stage have not been able to effectively solve the nonlinearity and uncertainty problems of the high degree of freedom manipulators. In order to overcome these problems and improve the trajectory performance of the high degree of freedom manipulators, a manipulator trajectory planning method based on a radial basis function (RBF) neural network is proposed in this work. Firstly, a 6-DOF robot experimental platform was designed and built. Secondly, the overall manipulator trajectory planning framework was designed, which included manipulator kinematics and dynamics and a quintic polynomial interpolation algorithm. Then, an adaptive robust controller based on an RBF neural network was designed to deal with the nonlinearity and uncertainty problems, and Lyapunov theory was used to ensure the stability of the manipulator control system and the convergence of the tracking error. Finally, to test the method, a simulation and experiment were carried out. The simulation results showed that the proposed method improved the response and tracking performance to a certain extent, reduced the adjustment time and chattering, and ensured the smooth operation of the manipulator in the course of trajectory planning. The experimental results verified the effectiveness and feasibility of the method proposed in this paper.

scholarly journals Design of Picking Robot Manipulator Control System Based on Fuzzy Compensation RBF Neural Network

CONVERTER ◽  
2021 ◽  
pp. 685-692
Author(s):  
Na Wang, Qinghui Meng, Jie Yang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Learning Algorithm ◽  
Robot Manipulator ◽  
Rbf Neural Network ◽  
Nonlinear Approximation ◽  
Basic Structure ◽  
Euler Method ◽  
Important Position ◽  
Manipulator Control

Industrial manipulator occupies a very important position in industrial production. The tracking control of its control system and joint trajectory has always been a research hotspot. But the manipulator is a multi input multi output system, which has the characteristics of nonlinearity and strong coupling. Radial basis function (RBF) neural network has high nonlinear mapping ability. In this paper, the structure characteristics, learning algorithm and application of RBF neural network in manipulator control are analyzed. In this paper, the nonlinear approximation property of RBF neural network is theoretically verified. This paper analyzes the basic structure of picking manipulator system in detail. At the same time, the Lagrange Euler method is used to deduce the dynamic equation of the two degree of freedom series manipulator, and the inertia characteristics, Coriolis force and centripetal force characteristics, heavy torque characteristics are analyzed. The nonlinear system model of manipulator based on S-function is established in MATLAB, and the dynamic model is transformed into the form of second-order differential equation to facilitate the introduction of the designed algorithm.

Permanent Deformation Process of Asphalt Concrete Pavement Based on Neural Network Modeling

2013 ◽  
Vol 341-342 ◽  
pp. 1486-1490
Author(s):  
Fu Cheng Yin ◽  
Guang Chun Zhou
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Rbf Neural Network ◽  
Permanent Deformation ◽  
Basic Function ◽  
Neural Network Modeling ◽  
Output Data ◽  
Pavement Layers ◽  
Asphalt Concrete Pavement

This paper numerically simulates the deflection response of layers on the cross section of a medium-strength subgrade (MFC) flexible pavement under repeating load, by a radial basic function (RBF) neural network model. The RBF modeling focuses on the functional relationship between the local points in the top deflection curves of pavement layers. The input and output data of the RBF model utilizes the last deflection profiles on the tops of four layers in the test. The deflection curve of the pavement surface is set as the input data since its developing process can been watched and measured in the test. The deflection curves of the other three layers are as the output data, because their deflection process was invisible in the test. Thus, the deflection process of the pavement layers invisible in the test can be simulated by the trained RBF neural network model, which results in a further analysis based on the obtained simulation data.

DESIGN, FABRICATION, AND PREDICTIVE MODEL OF A 1-DOF TRANSLATIONAL FLEXIBLE BEARING FOR HIGH PRECISION MECHANISM

2015 ◽  
Vol 39 (3) ◽  
pp. 419-429 ◽  
Author(s):  
Thanh-Phong Dao ◽  
Shyh-Chour Huang
Keyword(s):  
Neural Network ◽  
Stress Concentration ◽  
Linear Regression ◽  
High Precision ◽  
Rbf Neural Network ◽  
Basic Function ◽  
Practical Applications ◽  
Design Variables ◽  
The Relationship ◽  
Predictive Efficiency

Flexible bearing is significantly associated with high precision manipulators, actuators, and positioning stages. In this paper, a flexible bearing is designed for such applications. The life of a flexible bearing is very sensitively influenced by the stress concentration. The Taguchi method is applied to find the best combination of design variables to reduce the stress concentration. Multivariable linear regression (MLR) is established to model the relationship between the design variables and the stress response. In addition, to enhance the predictive efficiency for predicting, a radial basic function (RBF) neural network is used for this relationship. The effectiveness of all models is compared using statistical methods. It is evident that the relationship derived from RBF neural network is more accurate than that derived from MLR models. The confirmation experiments are conducted to verify the predicted results. The combined methodology in this paper is likely be used for various practical applications.

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

Research on Selecting Real Estate Project Based on PCA and RBF Neural Network

2011 ◽  
Vol 225-226 ◽  
pp. 162-165
Author(s):  
Hui Zhao ◽  
Li Ming Chen
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Real Estate ◽  
Rbf Neural Network ◽  
Principal Component ◽  
Component Analysis ◽  
Basic Function ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Project

A new method based on the integration of principal component analysis (PCA) and radial basic function (RBF) neural network is put forward for selecting the real estate project. Firstly, principal component analysis (PCA) is used to reduce the evaluation index dimensions. And then, radial basic function (RBF) neural network is used to evaluate the real estate projects. In order to grasp this method better, finally, the paper provides a case to demonstrate the application of this method in selecting the real estate project. The case has shown that the method applied to select the real estate project is feasible and reliable.

scholarly journals Combined Neural Network and PD Adaptive Tracking Controller for Ship Steering System

2017 ◽  
Vol 13 (1) ◽  
pp. 59-66 ◽  
Author(s):  
Abdul-Basset Al- Hussein
Keyword(s):  
Neural Network ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Approximation Error ◽  
System Stability ◽  
Stability And Convergence ◽  
Control Law ◽  
Adaptive Tracking ◽  
Uncertain Dynamics ◽  
Tracking Controller

In this paper, a combined RBF neural network sliding mode control and PD adaptive tracking controller is proposed for controlling the directional heading course of a ship. Due to the high nonlinearity and uncertainty of the ship dynamics as well as the effect of wave disturbances a performance evaluation and ship controller design is stay difficult task. The Neural network used for adaptively learn the uncertain dynamics bounds of the ship and their output used as part of the control law moreover the PD term is used to reduce the effect of the approximation error inherited in the RBF networks. The stability of the system with the combined control law guaranteed through Lyapunov analysis. Numeric simulation results confirm the proposed controller provide good system stability and convergence.

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