Radial basis function neural network control of an XY micropositioning stage without exact dynamic model

2009 ◽  
Author(s):  
Qingsong Xu ◽  
Yangmin Li
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Dynamic Model ◽  
Basis Function ◽  
Network Control ◽  
Neural Network Control ◽  
Radial Basis

Adaptive neural network control for stabilizing sphere of floated inertial platform using rotation vector

2020 ◽  
Vol 234 (6) ◽  
pp. 1209-1224
Author(s):  
Xibin Bai ◽  
Lijun Zhang ◽  
Shifeng Zhang ◽  
Hong Cai ◽  
Huabo Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Dynamic Model ◽  
Basis Function ◽  
Network Control ◽  
Rotation Vector ◽  
Hydrodynamic Drag ◽  
State Variables ◽  
Adaptive Neural Network Control ◽  
Radial Basis

This paper presents a neural network adaptive control scheme for the sphere three-dimensional stabilization of the floated inertial platform in consideration of the shell rotation and unknown attributions. Firstly, based on the analysis of the moments acting on the sphere, the dynamic model of the rotational sphere is established in view of the unknown attributions including mass distribution, hydrodynamic drag, electric brush friction, disturbances etc. Secondly, a radial basis function neural network is designed to identify the real dynamic model. Thirdly, the rotation vector from current attitude to the reference attitude is selected as the input of the controller according to the Euler rotation theorem. An updating law of the control parameters is derived from the radial basis function neural network. The adaptive controller is designed using this rotation vector and the updating law. This method addresses the problem of different dimensions between the two state variables of the dynamic model, which is derived from the sphere attitude expressed by a quaternion. Finally, simulation examples are provided to verify the effectiveness and robustness of the designed controller.

Endurance Brake Classification Control Based on the Generalized Growth and Pruning Radial Basis Function Neural Network

2018 ◽  
Vol 9 (5) ◽  
Author(s):  
Peilong Shi ◽  
Man Yu ◽  
Xiongwen Lu ◽  
Xuan Zhao
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Simulation Method ◽  
Network Control ◽  
Velocity Variation ◽  
Radial Basis ◽  
The Difference ◽  
Automatic Matching ◽  
Eddy Current Retarder

Because of the difference of working principles and arrangements of endurance braking systems, including engine brake, exhaust brake and eddy current retarder, it is difficult to match braking manually more than two types of endurance braking systems working simultaneously on long downhill. Meanwhile, manipulating control on different slopes will distract the driver's attention and cause driving fatigue. Aiming at this problem, the endurance brake classification control strategy is proposed, setting the deceleration, road slope and the difference of current speed and target speed as an input and the endurance brake classification as an output variable. Considering velocity variation is related to these factors with strong nonlinear characteristics, Generalized Growth and Pruning Radial Basis Function neural network control is used to estimate the input deceleration. Tests were conducted to verify the accuracy of simulation model. Variable slopes are researched through simulation method. The results show that the system designed to achieve automatic matching control can effectively decelerate and keep the truck running stably.

scholarly journals Research on Angle and Stiffness Cooperative Tracking Control of VSJ of Space Manipulator Based on LESO and NSFAR Control

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 893 ◽  
Author(s):  
Ye ◽  
Hong ◽  
Dong
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Dynamic Model ◽  
Basis Function ◽  
State Feedback ◽  
Input Saturation ◽  
Variable Stiffness ◽  
State Feedback Control ◽  
Space Manipulator ◽  
Radial Basis

With the increase in on-orbit maintenance and support requirements, the application of space manipulator is becoming more promising. However, how to control the vibration generated by the space manipulator has been a difficult problem to be solved. The advent of variable stiffness joint (VSJ) has brought about a dawn in solving this problem. But how to achieve coordinated control of joint angle and stiffness is still a problem to be solved, especially when considering system model parameter uncertainty, unknown disturbance and control input saturation. In order to realize the controllable attenuation of the vibration of the space flexible manipulator based on the variable stiffness joint, the dynamic model of the variable stiffness joint was constructed. Then the linear transformation and feedback linearization method are used to transform its complex nonlinear dynamic model system into a pseudo-linear system containing aggregate disturbance and input saturation constraints. This paper constructs a linear extended state observer (LESO) for estimating the state of unknown systems in pseudo-linear systems. Based on the idea of state feedback control, a Neural State Feedback Adaptive Robust (NSFAR) control is constructed by using Radial Basis Function Neural Network. The adaptive input saturation compensation control law is also designed by using Radial Basis Function Neural Network to deal with the input saturation compensation problem. The ultimate uniform bounded stability of the constructed system is proved by the stability analysis based on Lyapunov function. Finally, the effectiveness and superiority of the constructed tracking algorithm are verified by compared simulation and semi-physical experiment.

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