Adaptive radial basis function neural network based tracking control of Van der Pol oscillator

2017 ◽  
Author(s):  
Abasin Ulasyar ◽  
Haris Sheh Zad ◽  
Adil Zohaib ◽  
Syed Shahzad Hussain
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Tracking Control ◽  
Van Der Pol Oscillator ◽  
Van Der Pol ◽  
Radial Basis

scholarly journals Improved neural network-based adaptive tracking control for manipulators with uncertain dynamics

2020 ◽  
Vol 17 (4) ◽  
pp. 172988142094756
Author(s):  
Dong-hui Wang ◽  
Shi-jie Zhang
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Tracking Control ◽  
Robot Manipulators ◽  
External Disturbance ◽  
Tracking Error ◽  
Adaptive Tracking ◽  
Uncertain Dynamics ◽  
Radial Basis

In this article, a robust adaptive tracking controller is developed for robot manipulators with uncertain dynamics using radial basis function neural network. The design of tracking control systems for robot manipulators is a highly challenging task due to external disturbance and the uncertainties in their dynamics. The improved radial basis function neural network is chosen to approximate the uncertain dynamics of robot manipulators and learn the upper bound of the uncertainty. The adaptive law based on the Lyapunov stability theory is used to solve the uniform final bounded problem of the radial basis function neural network weights, which guarantees the stability and the consistent bounded tracking error of the closed-loop system. Finally, the simulation results are provided to demonstrate the practicability and effectiveness of the proposed method.

scholarly journals RBFNN Design Based on Modified Nearest Neighbor Clustering Algorithm for Path Tracking Control

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8349
Author(s):  
Dongxi Zheng ◽  
Wonsuk Jung ◽  
Sunghoon Kim
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Radial Basis Function ◽  
Basis Function ◽  
Tracking Control ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Path Tracking ◽  
Test Accuracy ◽  
Radial Basis

Radial basis function neural networks are a widely used type of artificial neural network. The number and centers of basis functions directly affect the accuracy and speed of radial basis function neural networks. Many studies use supervised learning algorithms to obtain these parameters, but this leads to more parameters that need to be determined, thereby making the system more complex. This study proposes a modified nearest neighbor-based clustering algorithm for training radial basis function neural networks. The calculation of this clustering algorithm is not large, and it can adapt to varying densities. Furthermore, it does not require researchers to set parameters based on experience. Simulation proves that the clustering algorithm can effectively cluster samples and optimize the abnormal samples. The radial basis function neural network based on modified nearest neighbor-based clustering has higher accuracy in curve fitting than the conventional radial basis function neural network. Finally, the path tracking control based on a radial basis function neural network of a magnetic microrobot is investigated, and its effectiveness is verified through simulation. The test accuracy and training accuracy of the radial basis function neural network was improved by 23.5% and 7.5%, respectively.

Neural tracking control of surface vehicles with lumped uncertainties

2020 ◽  
pp. 095965182097355
Author(s):  
Ying Zheng
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Trajectory Tracking ◽  
Basis Function ◽  
Tracking Control ◽  
Trajectory Tracking Control ◽  
Time Control ◽  
Neural Network Controller ◽  
Radial Basis ◽  
The Stability

In this article, an adaptive radial basis function neural network scheme for trajectory tracking control of surface vehicles is proposed. Under complex uncertainties, the proposed controller is designed by combining radial basis function neural network and finite-time control algorithm. Using the novel controller, the stability of accurate trajectory tracking can be ensured and the robustness of control system can be improved. Theoretical proof is proposed by Lyapunov function that the radial basis function neural network controller can make surface vehicle to accurately track desire trajectory steadily. Simulation studies conducted on a prototype CyberShip II demonstrate remarkable performance of proposed control scheme.

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