scholarly journals Adaptive force/ position control for dual-arm system based on neural network radial basis function without using a force sensor

2020 ◽  
pp. 1-7
Author(s):  
Luu Thi Hue, Duong Minh Duc, Pham Thuc Anh Nguyen Pham
Keyword(s):  
Neural Network ◽  
Adaptive Learning ◽  
Position Control ◽  
Learning Algorithm ◽  
Contact Point ◽  
Force Sensor ◽  
Object A ◽  
Contact Points ◽  
Radial Basis ◽  
Dual Arm

The paper has developed an adaptive algorithm using neural network for controlling dual-arm robotic system in stable holding a rectangle object and moving it to track the desired trajectories. Firstly, an overall dynamic of the system including the dual-arm robot and the object is derived based on Euler-Lagrangian principle. Then based on the dynamics, a controller has proposed to achieve the desired trajectories of the holding object. A radial basis neural network has been applied to compensate uncertainties of system parameters. The adaptive learning algorithm has been derived owning to Lyapunov stability principle to guarantee asymptotical convergence of the closed loop system. Besides, force control at contact point is implemented without the measurements of forces and moments at contact points. Finally, simulation work on Matlab has been carried out to confirm the accuracy and the effectiveness of the proposed controller.

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.

Adaptive Learning Algorithm for Cerebellar Model Articulation Controller: Neural Network Based Hybrid-Type Controller—Part I

2000 ◽  
Author(s):  
Magdy Mohamed Abdelhameed ◽  
Sabri Cetinkunt
Keyword(s):  
Neural Network ◽  
Control System ◽  
Adaptive Learning ◽  
Learning Algorithm ◽  
Cerebellar Model Articulation Controller ◽  
Neural Network Learning ◽  
Network Learning ◽  
Hybrid Type ◽  
Cmac Neural Network ◽  
Learning Technique

Abstract Cerebellar model articulation controller (CMAC) is a useful neural network learning technique. It was developed two decades ago but yet lacks an adequate learning algorithm, especially when it is used in a hybrid- type controller. This work is intended to introduce a simulation study for examining the performance of a hybrid-type control system based on the conventional learning algorithm of CMAC neural network. This study showed that the control system is unstable. Then a new adaptive learning algorithm of a CMAC based hybrid- type controller is proposed. The main features of the proposed learning algorithm, as well as the effects of the newly introduced parameters of this algorithm have been studied extensively via simulation case studies. The simulation results showed that the proposed learning algorithm is a robust in stabilizing the control system. Also, this proposed learning algorithm preserved all the known advantages of the CMAC neural network. Part II of this work is dedicated to validate the effectiveness of the proposed CMAC learning algorithm experimentally.

scholarly journals An Adaptive Learning Rate for RBFNN Using Time-Domain Feedback Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Syed Saad Azhar Ali ◽  
Muhammad Moinuddin ◽  
Kamran Raza ◽  
Syed Hasan Adil
Keyword(s):  
Neural Networks ◽  
Radial Basis Function ◽  
Adaptive Learning ◽  
Basis Function ◽  
Learning Algorithm ◽  
Time Series Prediction ◽  
Learning Rate ◽  
Theoretical Development ◽  
Feedback Analysis ◽  
Radial Basis

Radial basis function neural networks are used in a variety of applications such as pattern recognition, nonlinear identification, control and time series prediction. In this paper, the learning algorithm of radial basis function neural networks is analyzed in a feedback structure. The robustness of the learning algorithm is discussed in the presence of uncertainties that might be due to noisy perturbations at the input or to modeling mismatch. An intelligent adaptation rule is developed for the learning rate of RBFNN which gives faster convergence via an estimate of error energy while giving guarantee to thel2stability governed by the upper bounding via small gain theorem. Simulation results are presented to support our theoretical development.

An adaptive learning algorithm for a wavelet neural network

2005 ◽  
Vol 22 (5) ◽  
pp. 235-240 ◽  
Author(s):  
Yevgeniy Bodyanskiy ◽  
Nataliya Lamonova ◽  
Iryna Pliss ◽  
Olena Vynokurova
Keyword(s):  
Neural Network ◽  
Adaptive Learning ◽  
Learning Algorithm ◽  
Wavelet Neural Network
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