scholarly journals Design of Adaptive Kinematic Controller Using Radial Basis Function Neural Network for Trajectory Tracking Control of Differential-Drive Mobile Robot

2019 ◽  
Vol 19 (4) ◽  
pp. 349-359
Author(s):  
Tran Quoc Khai ◽  
Young-Jae Ryoo
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Radial Basis Function ◽  
Trajectory Tracking ◽  
Basis Function ◽  
Tracking Control ◽  
Trajectory Tracking Control ◽  
Differential Drive ◽  
Radial Basis

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.

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.

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