Performance improvement of robot manipulator control using an on-line neural network compensator

1999 ◽  
Vol 213 (1) ◽  
pp. 49-60
Author(s):  
S K Tso ◽  
Y H Fung ◽  
N L Lin
Keyword(s):  
Neural Network ◽  
Performance Improvement ◽  
Robot Manipulator ◽  
On Line ◽  
Manipulator Control ◽  
Neural Network Compensator

scholarly journals Fuzzy-neural network compensator for Robot manipulator controlled by PD-like fuzzy system

2012 ◽  
Vol 8 (8) ◽  
pp. 35-44
Author(s):  
Turki Abdalla ◽  
Basil Jasim
Keyword(s):  
Neural Network ◽  
Fuzzy System ◽  
Fuzzy Neural Network ◽  
Robot Manipulator ◽  
Fuzzy Neural ◽  
Error Space ◽  
Reference Trajectories ◽  
Neural Network Compensator ◽  
Input Error ◽  
Computed Torque

In this paper, high tracking performance control structure for rigid robot manipulator is proposed. PD-like Sugano type fuzzy system is used as a main controller, while fuzzy-neural network (FNN) is used as a compensator for uncertainties by minimizing suitable function. The output of FNN is added to the reference trajectories to modify input error space, so that the system robust to any change in system parameters. The proposed structure is simulated and compared with computed torque controller. The simulation study has showed the validity of our structure, also showed its superiority to computed torque controller.

scholarly journals Design of Picking Robot Manipulator Control System Based on Fuzzy Compensation RBF Neural Network

CONVERTER ◽  
2021 ◽  
pp. 685-692
Author(s):  
Na Wang, Qinghui Meng, Jie Yang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Learning Algorithm ◽  
Robot Manipulator ◽  
Rbf Neural Network ◽  
Nonlinear Approximation ◽  
Basic Structure ◽  
Euler Method ◽  
Important Position ◽  
Manipulator Control

Industrial manipulator occupies a very important position in industrial production. The tracking control of its control system and joint trajectory has always been a research hotspot. But the manipulator is a multi input multi output system, which has the characteristics of nonlinearity and strong coupling. Radial basis function (RBF) neural network has high nonlinear mapping ability. In this paper, the structure characteristics, learning algorithm and application of RBF neural network in manipulator control are analyzed. In this paper, the nonlinear approximation property of RBF neural network is theoretically verified. This paper analyzes the basic structure of picking manipulator system in detail. At the same time, the Lagrange Euler method is used to deduce the dynamic equation of the two degree of freedom series manipulator, and the inertia characteristics, Coriolis force and centripetal force characteristics, heavy torque characteristics are analyzed. The nonlinear system model of manipulator based on S-function is established in MATLAB, and the dynamic model is transformed into the form of second-order differential equation to facilitate the introduction of the designed algorithm.

scholarly journals ANN Based Inverse Dynamic Model of the 6-PGK Parallel Robot Manipulator

2015 ◽  
Vol 11 (1) ◽  
pp. 90 ◽  
Author(s):  
Liviu Moldovan ◽  
Horațiu-Ștefan Grif ◽  
Adrian Gligor
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Robot Manipulator ◽  
Parallel Robot ◽  
Model Estimation ◽  
Inverse Dynamic ◽  
Inverse Dynamic Model ◽  
The Neural Network ◽  
On Line ◽  
Artificial Neural

<p>This paper presents an inverse dynamic model estimation based on an artificial neural network of a complete new parallel robot manipulator prototype 6- PGK with six degrees of freedom, built at Petru Maior University of Tirgu-Mures. The model estimation of the parallel robot manipulator is performed with a feedforward artificial neural network. In the control engineering domain there are control structures that need the direct or inverse model of the process for ensuring the process control at the imposed performances. Usually, the determination of the direct/inverse mathematical model is a difficult or impossible task to be achieved. In these cases different non-parametric or parametric, off-line or on-line identification methods are used. A solution that may support the on-line parametric methods is represented by the feedforward artificial neural networks. By implementing feedforward artificial neural networks as a nonlinear autoregressive model with exogenous inputs, the authors investigate the possibility of choosing the optimum parameters that characterize the neural network so that it approximates as better as possible the model of the 6-PGK prototype robot. Finally an innovative algorithm is developed for obtaining the optimal configuration parameters set of the feedforward artificial neural network. The proposed algorithm helps in setting the optimal parameters of the neural network that offer high opportunities to provide satisfactory identification of the robot model. Experimental results obtained by a structure derived from the proposed solution demonstrate a good approximation related to the studied system, which is characterized by nonlinearities and high complexity.</p>

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