Adaptive Control of Robot Series Elastic Drive Joint Based on Optimized Radial Basis Function Neural Network

2021 ◽  
Author(s):  
Nianfeng Shao ◽  
Qinyuan Zhou ◽  
Chenyang Shao ◽  
Yan Zhao
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis ◽  
Series Elastic

scholarly journals Adaptive control of track tension estimation using radial basis function neural network

2020 ◽  
Author(s):  
Ping-xin Wang ◽  
Xiao-ting Rui ◽  
Hai-long Yu ◽  
Guo-ping Wang ◽  
Dong-yang Chen
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis

scholarly journals Adaptive Control for Dual-Arm Robotic System Based on Radial Basis Function Neural Network 

2020 ◽  
Vol 31 (1) ◽  
pp. 50-59
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Radial Basis Function ◽  
Basis Function ◽  
Robotic System ◽  
Object A ◽  
Radial Basis ◽  
Stability Principle ◽  
Asymptotical Convergence ◽  
Dual Arm

The paper has developed an adaptive control using neural network for controlling a dual-arm robotic system in moving a rectangle object to the desired trajectories. Firstly, the overall dynamics of the manipulators and the object have been derived based on Euler-Lagrangian principle. And then based on the dynamics, a controller has been proposed to achieve the desired trajectories of the grasping object. A radial basis function neural network has been applied to compensate uncertainties of dynamic parameters. The adaptive algorithm has been derived owning to the Lyapunov stability principle to guarantee asymptotical convergence of the closed dynamic system. Finally, simulation work on MatLab has been carried out to reconfirm the accuracy and the effectiveness of the proposed controller.

scholarly journals Application of Nonlinear Adaptive Control in Temperature of Chinese Solar Greenhouses

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1582
Author(s):  
Yonggang Wang ◽  
Yujin Lu ◽  
Ruimin Xiao
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Radial Basis Function ◽  
Control Strategy ◽  
Basis Function ◽  
Adaptive Controller ◽  
Strong Nonlinearity ◽  
Nonlinear Adaptive Control ◽  
Radial Basis ◽  
Adaptive Control Strategy

The system of a greenhouse is required to ensure a suitable environment for crops growth. In China, the Chinese solar greenhouse plays a crucial role in maintaining a proper microclimate environment. However, the greenhouse system is described with complex dynamic characteristics, such as multi-disturbance, parameter uncertainty, and strong nonlinearity. It is difficult for the conventional control method to deal with the above problems. To address these problems, a dynamic model of Chinese solar greenhouses was developed based on energy conservation laws, and a nonlinear adaptive control strategy combined with a Radial Basis Function neural network was presented to deal with temperature control. In this approach, nonlinear adaptive controller parameters were determined through the generalized minimum variance laws, while unmodeled dynamics were estimated by a Radial Basis Function neural network. The control strategy consisted of a linear adaptive controller, a neural network nonlinear adaptive controller, and a switching mechanism. The research results show that the mean errors were 0.8460 and 0.2967, corresponding to a conventional PID method and the presented nonlinear adaptive scheme, respectively. The standard errors of the conventional PID method and the nonlinear adaptive control strategy were 1.8480 and 1.3342, respectively. The experimental results fully prove that the presented control scheme achieves better control performance, which meets the actual requirements.

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