Fault Surface Models of Coal Rake Based on RBF Neural Network

2014 ◽  
Vol 484-485 ◽  
pp. 616-619
Author(s):  
Xiao Li Pan ◽  
Li Hua Mu ◽  
Hui Chen
Keyword(s):  
Neural Network ◽  
Linear Function ◽  
Surface Reconstruction ◽  
High Precision ◽  
Rbf Neural Network ◽  
Coal Extraction ◽  
Coal Surface ◽  
Fault Surface ◽  
Surface Models ◽  
Cloud Of Points

In order to improve the accuracy of prospecting and efficiency of coal extraction, it is necessary to understand the geological construction deeply. Therefore, the reconstruction of fault surface models is highly important. Reconstructe surface from an unorganized cloud of points by using the RBF neural networkcs advantages of approximating no-linear function, and the algorithmcs scheme and analyses were given and the proposed method was applied to the coal surface reconstruction, this neural network can not only approximate the surface with high precision but also has good smoothness.

scholarly journals UAV adaptive trajectory tracking control under unknown time-varying disturbance and input saturation

2021 ◽  
Author(s):  
Yimin Zhou ◽  
Zengwu Tian
Keyword(s):  
Neural Network ◽  
High Precision ◽  
Trajectory Tracking ◽  
Control Algorithm ◽  
Rbf Neural Network ◽  
Input Saturation ◽  
Dynamic Surface Control ◽  
Time Varying ◽  
Dynamic Surface ◽  
Surface Control

Abstract In this paper, the flight control of the Unmanned aerial vehicle (UAV) is discussed with the proposed adaptive dynamic surface control method owing to its underactuated and non-linear characteristics. The proposed control algorithm is based on radial basis function (RBF) neural network and anti-saturation auxiliary system to realize high-precision trajectory tracking under time-varying disturbances and input saturation. First, the nonlinear dynamic model of the UAV with disturbances is established with the aid of rigid body motion theory. With the adoption of the dynamic surface control algorithm, the error surface and the Lyapunov function are defined to design the preliminary control law of the designed controller. Then the RBF neural network is introduced to estimate and compensate the disturbance. Further, an anti - saturation module is designed to tackle the problem of input saturation. By using the Lyapunov stability theory, it is proved that the stability and signal consistency of the closed-loop system are bounded, along with the constrained conditions of the control parameters. Simulation experiments have been performed and the results demonstrate that the proposed control algorithm has high-precision trajectory tracking ability and strong anti-disturbance capability under the input saturation constraint with high control performance.

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.

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