Adaptive neural fuzzy control for robot manipulator friction and disturbance compensator

2008 ◽  
Author(s):  
Vu Minh Hung ◽  
Uhn Joo Na
Keyword(s):  
Fuzzy Control ◽  
Robot Manipulator ◽  
Neural Fuzzy

Modelling and control of manipulators with flexible links working on land and underwater environments

Robotica ◽  
2010 ◽  
Vol 29 (3) ◽  
pp. 461-470 ◽  
Author(s):  
Levent Gümüşel ◽  
Nurhan Gürsel Özmen
Keyword(s):  
Fuzzy Control ◽  
Control Method ◽  
Robot Manipulator ◽  
Control Methods ◽  
Control Laws ◽  
Linear Ordinary Differential Equations ◽  
Intelligent Technique ◽  
Wide Range ◽  
Classical Control ◽  
And Control

SUMMARYIn this study, modelling and control of a two-link robot manipulator whose first link is rigid and the second one is flexible is considered for both land and underwater conditions. Governing equations of the systems are derived from Hamilton's Principle and differential eigenvalue problem. A computer program is developed to solve non-linear ordinary differential equations defining the system dynamics by using Runge–Kutta algorithm. The response of the system is evaluated and compared by applying classical control methods; proportional control and proportional + derivative (PD) control and an intelligent technique; integral augmented fuzzy control method. Modelling of drag torques applied to the manipulators moving horizontally under the water is presented. The study confirmed the success of the proposed integral augmented fuzzy control laws as well as classical control methods to drive flexible robots in a wide range of working envelope without overshoot compared to the classical controls.

scholarly journals Inverse Kinematic Solution of 5R Manipulator using ANN and ANFIS

2015 ◽  
Vol 4 (2) ◽  
pp. 109
Author(s):  
Panchand Jha
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Inference System ◽  
Inverse Kinematics ◽  
Fuzzy Inference ◽  
Robot Manipulator ◽  
Ann Model ◽  
Inference System ◽  
Artificial Neural ◽  
Neural Fuzzy

<span>Inverse kinematics of manipulator comprises the computation required to find the joint angles for a given Cartesian position and orientation of the end effector. There is no unique solution for the inverse kinematics thus necessitating application of appropriate predictive models from the soft computing domain. Artificial neural network and adaptive neural fuzzy inference system techniques can be gainfully used to yield the desired results. This paper proposes structured artificial neural network (ANN) model and adaptive neural fuzzy inference system (ANFIS) to find the inverse kinematics solution of robot manipulator. The ANN model used is a multi-layered perceptron Neural Network (MLPNN). Wherein, gradient descent type of learning rules is applied. An attempt has been made to find the best ANN configuration for the problem. It is found that ANFIS gives better result and minimum error as compared to ANN.</span>

scholarly journals Multiple Fuzzy Control of a Robot Manipulator.

1993 ◽  
Vol 59 (557) ◽  
pp. 158-164 ◽  
Author(s):  
Sangho Jin ◽  
Keigo Watanabe ◽  
Masatoshi Nakamura
Keyword(s):  
Fuzzy Control ◽  
Robot Manipulator
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