Adaptive Finite-time Synergetic Control of Delta Robot based on Radial basis Function Neural Networks

2021 ◽  
Author(s):  
Phu-Cuong Pham ◽  
Yong-Lin Kuo
Keyword(s):  
Neural Networks ◽  
Radial Basis Function ◽  
Basis Function ◽  
Finite Time ◽  
Sliding Mode ◽  
Radial Basis ◽  
Delta Robot ◽  
Synergetic Control ◽  
Uncertainty Parameters ◽  
Radial Basis Function Approximation

Abstract This paper proposes a novel robust proportional derivative adaptive non-singular synergetic control (PDATS) for the delta robot system. A proposal radial basis function approximation neural networks (RBF) compensates for external disturbances and uncertainty parameters. To counteract the chattering noise of the low-resolution encoder, a second-order sliding mode (SOSM) observer in the feedback loop showed the ability to obtain the angular velocity estimations. The stability of the PDATS approach is proven using the Lyapunov stability theory. Both the simulation and experiment result effectiveness and performances of the PDATS controller in trajectory; pick and place operations of a parallel delta robot. The characteristics of the controller demonstrate that the proposed method can effectively reduce external disturbance and uncertainty parameters of the robot by a convergent finite-time, and provide higher accuracy in comparison with finite-time synergetic control and PD control.

scholarly journals Neural adaptive sliding mode controller for unmanned surface vehicle steering system

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401879552 ◽  
Author(s):  
Lili Wan ◽  
Yixin Su ◽  
Huajun Zhang ◽  
Yongchuan Tang ◽  
Binghua Shi
Keyword(s):  
Neural Networks ◽  
Sliding Mode Control ◽  
Radial Basis Function ◽  
Basis Function ◽  
Sliding Mode ◽  
Steering System ◽  
Sliding Mode Controller ◽  
Unmanned Surface Vehicle ◽  
Adaptive Sliding Mode Controller ◽  
Radial Basis

Unmanned surface vehicle has the properties such as complexity, nonlinearity, time variability, and uncertainty, which lead to the difficulty of obtaining a precise kinematics model. A neural adaptive sliding mode controller for the unmanned surface vehicle steering system is developed based on the sliding mode control technique and the radial basis function neural network. In the new approach, two parallel radial basis function neural networks are used to reduce the influence of the system uncertainties and eliminate the dependency of the controller on the precise kinematics model of the system. Among these two radial basis function neural networks, one is used to approximate the unknown nonlinear yaw dynamics and the other is used to adjust the control gain as well as realize the variable gain sliding mode control. The weights of the two neural networks are trained online using the sliding surface variable and the control, where the Lyapunov method is used to derive the adaptive laws to ensure the stability of the whole closed-loop system. The proposed adaptive controller is suitable for the steering control at different cruising speeds with bounded external disturbances. The simulation results show that the proposed controller has a good control performance regarding the smooth control, fast response, and high accuracy.

Dual feedforward neural networks based synchronized sliding mode controller for cooperative manipulator system under variable load and uncertainties

2020 ◽  
Vol 234 (19) ◽  
pp. 3859-3872
Author(s):  
Jin Wang ◽  
Anbang Zhai ◽  
Fan Xu ◽  
Haiyun Zhang ◽  
Guodong Lu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Radial Basis Function ◽  
Basis Function ◽  
Sliding Mode ◽  
Feedforward Neural Networks ◽  
Internal Force ◽  
Variable Load ◽  
Sliding Mode Controller ◽  
Radial Basis

The problem of simultaneous position and internal force control is discussed with cooperative manipulators system under variable load and dynamic uncertainties in this study. A position synchronized sliding mode controller is proposed in the presence of variable load, as well as modeling uncertainties, joint friction, and external disturbances. To deal with the complex situation brought by variable load, virtual synchronization coupled errors are introduced for internal force tracking control and joint synchronization in the meantime. Dual feedforward neural networks are adopted, where a radial basis function-neural network based dynamic compensator and a radial basis function-neural network based internal force estimator are established, respectively, so that precise dynamic knowledge and force measurement are out of demand through their cooperation. Together with simulation studies and analysis, the position and internal force errors are shown to converge asymptotically to zero. Using Lyapunov stability approach, the proposed controller is proven to be robust in face of variable external load and the aforementioned uncertainties.

Thermal analysis of a moving fin using the radial basis function approximation

Heat Transfer ◽  
2021 ◽  
Author(s):  
Maryam Fallah Najafabadi ◽  
Hossein Talebi Rostami ◽  
Khashayar Hosseinzadeh ◽  
Davood Domiri Ganji
Keyword(s):  
Thermal Analysis ◽  
Radial Basis Function ◽  
Basis Function ◽  
Function Approximation ◽  
Radial Basis ◽  
Radial Basis Function Approximation
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