scholarly journals Radial Basis Functional Link Network and Hamilton Jacobi Issacs for Force/Position Control in Robotic Manipulation

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Shuhuan Wen ◽  
Junying Yuan ◽  
Jinghai Zhu
Keyword(s):  
Neural Network ◽  
Position Control ◽  
Control Method ◽  
Rbf Neural Network ◽  
Robotic Manipulation ◽  
System Stability ◽  
External Interference ◽  
Functional Link ◽  
Rbf Network ◽  
Radial Basis

This paper works on hybrid force/position control in robotic manipulation and proposes an improved radial basis functional (RBF) neural network, which is a robust relying on the Hamilton Jacobi Issacs principle of the force control loop. The method compensates uncertainties in a robot system by using the property of RBF neural network. The error approximation of neural network is regarded as an external interference of the system, and it is eliminated by the robust control method. Since the conventionally fixed structure of RBF network is not optimal, resource allocating network (RAN) is proposed in this paper to adjust the network structure in time and avoid the underfit. Finally the advantage of system stability and transient performance is demonstrated by the numerical simulations.

Shunt Active Filter Based on Radial Basis Function Neural Network and p-q Power Theory

2017 ◽  
Vol 8 (2) ◽  
pp. 667 ◽  
Author(s):  
Prakash Ch. Tah ◽  
Anup K. Panda ◽  
Bibhu P. Panigrahi
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Control Strategies ◽  
New Combination ◽  
Training Methods ◽  
Power Theory ◽  
Rbf Network ◽  
Radial Basis

In this paper a new combination Radial Basis Function Neural Network and p-q Power Theory (RBFNN-PQ) proposed to control shunt active power filters (SAPF). The recommended system has better specifications in comparison with other control methods. In the proposed combination an RBF neural network is employed to extract compensation reference current when supply voltages are distorted and/or unbalance sinusoidal. In order to make the employed model much simpler and tighter an adaptive algorithm for RBF network is proposed. The proposed RBFNN filtering algorithm is based on efficient  training methods called hybrid learning method.The method  requires a small size network, very robust, and the proposed algorithms are very effective. Extensive simulations are carried out with PI as well as RBFNN controller for p-q control strategies by considering different voltage conditions and adequate results were presented.

Position control for permanent magnet synchronous motor based on neural network and terminal sliding mode control

2020 ◽  
Vol 42 (9) ◽  
pp. 1632-1640
Author(s):  
Wenwu Zhu ◽  
Dongbo Chen ◽  
Haibo Du ◽  
Xiangyu Wang
Keyword(s):  
Neural Network ◽  
Permanent Magnet ◽  
Finite Time ◽  
Position Control ◽  
Control Method ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Permanent Magnet Synchronous Motor ◽  
Synchronous Motor ◽  
Terminal Sliding Mode

A finite-time control strategy is proposed to solve the problem of position tracking control for a permanent magnet synchronous motor servo system. It can guarantee that the motor’s desired position can be tracked in a finite time. Firstly, for the d-axis voltage, a first-order finite-time controller is designed based on the vector control principle. Then, for the q-axis voltage, based on a radial basis function (RBF) neural network, an integral high-order terminal sliding mode controller is designed. Theoretical analysis shows that under the proposed controller, the desired position can be tracked by the motor position in a finite time. Simulation results are given to show that the proposed control method has a strong anti-disturbance ability and a fast convergence performance.

The Study of Precipitation Forecast Model on EMD-RBF Neural Network - A Case Study on Northeast China

2014 ◽  
Vol 641-642 ◽  
pp. 119-122 ◽  
Author(s):  
Xiao Sun ◽  
Shi Fan Qiao ◽  
Ji Ren Xie
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Precipitation Forecast ◽  
Maximum Relative Error ◽  
Precipitation Data ◽  
Rbf Network ◽  
Precipitation Forecasting ◽  
Radial Basis

Based on the principal of forecast of Artificial Neural Network, Radial Basis Function neural network and Radial Basis Function neural network based on EMD were introduced into the field of precipitation forecasting in this article. With the precipitation data of 27 sites from1950-2010, EMD-RBF network was set up, and the difference between the predictive value and the actual precipitation data was discussed. The results showed that the correlation Of EMD-RBF forecast precipitation and actual precipitation is more than 0.9. Of all sites, the maximum relative prediction error of 17 sites is less than 10%, the maximum relative error does not exceed 15%.The EMD-RBF model had good quality on forecasting precision, which provided a new method for precipitation forecasting.

scholarly journals Rainfall Runoff Modeling using Radial Basis Function Neural Network for Sungai Tinjar Catchment, Miri, Sarawak

2009 ◽  
Vol 1 (1) ◽  
pp. 1-7
Author(s):  
Suhaimi S. ◽  
Rosmina A. Bustami
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Rainfall Runoff ◽  
Optimum Result ◽  
Rbf Network ◽  
Linear Relationships ◽  
Radial Basis ◽  
Artificial Neural Network Ann

Artificial Neural Network (ANN) is a very useful data modelling tool that is able to capture and represent complex input and output relationships. The advantage of ANN lies in its ability to represent both linear and non-linear relationships and in its ability to learn these relationships directly from the data being modelled. Modeling of rainfall runoff relationship is important in view of the many uses of water resources such as hydropower generation, irrigation, water supply and flood control.This study is to purposefully develop a rainfall runoff model for Sg. Tinjar with outlet at Long Jegan using Radial Basis Function (RBF) Neural Network. Training and simulation was done using Matlab 6.5.1 software with varying parameters to obtain the optimum result. Further, the results were compared to simulation done with Multilayer Percepteron model. The RBF network developed in this study has successfully modelled rainfall runoff relationship in Sungai Tinjar Catchment in Miri, Sarawak with an accuracy of about 98.3%.

A Novel Control Scheme Based on Improved RBF Neural Network

2012 ◽  
Vol 182-183 ◽  
pp. 1313-1317
Author(s):  
Xiao Zhu Xie ◽  
Xing Lai Guan
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Control Method ◽  
Rbf Neural Network ◽  
Rbf Network ◽  
Network Algorithm ◽  
Control Scheme ◽  
Control Command ◽  
The Matrix ◽  
Neural Network Algorithm

A novel control scheme based on an improved RBF neural network and PID control method is proposed. When used the RBF network in the PID controller, if RBF network provides the parameter to revise PID while it does not have training finished, the controller will oscillated or even to diverge. As the structure is more complex, and the adjustment speed is slower, the improvements are made to the standard RBF neural network trained algorithm. Uses the matrix operation substitute the iterative algorithm, may avoid the above question effectively. Finally, choosing a certain type of PID controller, the improved RBF neural network algorithm is used to design the control law for control command tracking, the simulation results show that the improved RBF neural network algorithm can avoid oscillated and diverge.

Direct Adaptive Control for a Certain Class of Nonlinear Systems Using Modified Radial Basis Function Neural Network

Volume 1 ◽  
2004 ◽  
Author(s):  
Hsuan-Ju Chen ◽  
Rongshun Chen
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Radial Basis Function ◽  
Basis Function ◽  
Controller Design ◽  
Rbf Neural Network ◽  
Design Parameters ◽  
State Variables ◽  
Rbf Network ◽  
Radial Basis

This paper proposes a direct adaptive controller for SISO affine nonlinear systems using Gaussian radial basis function (RBF) neural network (NN). The exact plant model is not necessary for composing the controller. If the plant is SISO, of affine form, without zero dynamics, and all the state variables are available, the controller is applicable under several mild assumptions. In this paper, the Gaussian RBF network (GRBFN) is modified to include pre-scale weights as its parameters for the input variables, which are also adapted in the control law. Pre-scaling the inputs is equivalent to extending or contracting the spectrum of the approximated function. With the modification, the spectrum along each coordinate of the domain can be scaled separately for approximating. The adaptation of the nonlinear parameters, including the variances, centers, and pre-scaling weights, are derived. Appropriate modification techniques are applied to the adaptation laws to ensure the robustness. The stability is analyzed with Lyapunov’s Theory. From the analysis, the effect of the controller design parameters is also examined. A simulation of an inverted pendulum control is demonstrated to show the effectiveness.

Research on Intelligent Diagnosis Technology of Transformer Fault

2013 ◽  
Vol 385-386 ◽  
pp. 589-592
Author(s):  
Hong Qi Wu ◽  
Xiao Bin Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Intelligent Diagnosis ◽  
Rbf Network ◽  
Working Principle ◽  
Research On Application ◽  
Transformer Fault

In order to improve the diagnosis rates of transformer fault, a research on application of RBF neural network is carried out. The structure and working principle of radial basis function (RBF) neural network are analyzed and a three layer RBF network is also designed for transformer fault diagnosis. It is proved by MATLAB experiment that RBF neural network is a strong classifier which is used to diagnose transformer fault effectively.

Fault Diagnosis Analysis of Rotor System Based on RBF Neural Network and Dynamic Systems

2012 ◽  
Vol 460 ◽  
pp. 127-130
Author(s):  
Song He Zhang ◽  
Yue Gang Luo ◽  
Bin Wu ◽  
Bing Cheng Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rbf Neural Network ◽  
Amplitude Spectrum ◽  
Rates Of Convergence ◽  
Rotor System ◽  
Rbf Network ◽  
Characteristic Quantity ◽  
Learning Rates ◽  
Fault Characteristic

The RBF network was applied in the rotor system to realize the fault diagnosis aiming the mapping complexity between fault symptoms and fault patterns. It can overcome the problems of low learning rates of convergence and falling easily into part minimums in BP algorithm, and improve the precision of diagnosis. The normalized values of seven frequency ranges in amplitude spectrum were used as the fault characteristic quantity, the RBF network was trained to diagnose the faults of rotor system. The results show that RBF neural network is a valid method of diagnosis of mechanical failure.

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