scholarly journals Real-Time-Position Prediction Algorithm for Under-Actuated Robot Manipulator Using of Artificial Neural Network

10.5772/10197 ◽  
2010 ◽  
Author(s):  
Ahmed Azlan Mat Isa ◽  
Hayder M.A.A.Al-Assadi ◽  
Ali Taqi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Real Time ◽  
Robot Manipulator ◽  
Prediction Algorithm ◽  
Time Position ◽  
Position Prediction ◽  
Artificial Neural

scholarly journals Development of a real-time-position prediction algorithm for under-actuated robot manipulator by using of artificial neural network

2011 ◽  
Vol 225 (8) ◽  
pp. 1991-1998 ◽  
Author(s):  
H M A A Al-Assadi ◽  
A A Mat Isa ◽  
Ali T Hasan ◽  
Z A Rahman ◽  
B Heimann
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Algorithm ◽  
Robot Manipulator ◽  
Angular Position ◽  
Prediction Algorithm ◽  
System Modelling ◽  
Active Joint ◽  
Passive Joint ◽  
Artificial Neural

An adaptive learning algorithm using an artificial neural network (ANN) has been proposed to predict the passive joint position of under-actuated robot manipulator. In this approach, a specific ANN model has been designed and trained to learn a desired set of joint angular positions for the passive joint from a given set of input torque and angular position for the active joint over a certain period of time. Trying to overcome the disadvantages of many used techniques in the literature, the ANNs have a significant advantage of being a model-free method. The learning algorithm can directly determine the position of its passive joint, and can, therefore, completely eliminate the need for any system modelling. Even though it is very difficult in practice, data used in this study were recorded experimentally from sensors fixed on robot’s joints to overcome the effect of kinematics uncertainties present in the real world such as ill-defined linkage parameters and backlashes in gear trains. An ANN was trained using the experimentally obtained data and then used to predict the path of the passive joint that is positioned by the dynamic coupling of the active joint. The generality and efficiency of the proposed algorithm are demonstrated through simulations of an under-actuated robot manipulator; finally, the obtained results were successfully verified experimentally.

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>

Artificial Neural Network Based Forecasting of Power Under Real Time Monitoring Environment

2021 ◽  
Author(s):  
Muhammad Zilal Bin Ab Hamid Pahmi ◽  
Afida Ayob ◽  
Shaheer Ansari ◽  
Mohamad Hanif Md Saad ◽  
Aini Hussain
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Real Time ◽  
Real Time Monitoring ◽  
Artificial Neural
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