scholarly journals Efficient single and dual axis solar tracking system controllers based on adaptive neural fuzzy inference system

2020 ◽  
Vol 32 (7) ◽  
pp. 459-469 ◽  
Author(s):  
Nadia AL-Rousan ◽  
Nor Ashidi Mat Isa ◽  
Mohd Khairunaz Mat Desa
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Tracking System ◽  
Inference System ◽  
Solar Tracking ◽  
Neural Fuzzy

scholarly journals Performance Comparison of the Single Axis and Two-Axis Solar System using Adaptive Neuro-Fuzzy Inference System Controls

2020 ◽  
Vol 190 ◽  
pp. 00005
Author(s):  
Chairul Imron ◽  
Imam Abadi ◽  
Ilham Amirul Akbar ◽  
Jauharotul Maknunah ◽  
Yusilawati Ahmad Nor ◽  
...  
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Tracking System ◽  
Maximum Energy ◽  
Performance Comparison ◽  
Energy Yield ◽  
The Sun ◽  
Inference System ◽  
Solar Tracking ◽  
Neuro Fuzzy

Solar energy is one of the renewable energy that gets more attention from many countries. Solar photo voltaic (PV) takes the right position to get the maximum energy yield. The study was conducted by comparison of performance with two methods of tracking the sun with one axis and two axes by using ANFIS control (Adaptive Neuro-Fuzzy Inference System). The solar tracking system is a system that operates on the sun by using a light sensor and controls the photovoltaic to always perpendicular to the sun by changing the pitch and yaw axis of the sun tracing properties. LDR (Light Dependent Resistor) is one of the light sensors whose resistance changes depending on the intensity of incoming light. Direct current (DC )motor is used as a PV drive panel in a solar tracking system. A two-axis solar tracking system has a greater power output than a tracking system with a single photovoltaic panel that does not use a tracking system (fixed).

scholarly journals A NEURAL FUZZY INFERENCE BASED ADAPTIVE CONTROLLER FOR NONHOLONOMIC ROBOTS

2011 ◽  
pp. 56-65
Author(s):  
Ting Wang ◽  
Fabien Gautero ◽  
Christophe Sabourin ◽  
Kurosh Madani
Keyword(s):  
Control Strategy ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Adaptive Controller ◽  
Reference Trajectory ◽  
Inference System ◽  
Nonholonomic Robots ◽  
Real Robot ◽  
Neuro Controller ◽  
Neural Fuzzy

In this paper, we propose a control strategy for a nonholonomic robot which is based on an Adaptive Neural Fuzzy Inference System. The neuro-controller makes it possible the robot track a desired reference trajectory. After a short reminder about Adaptive Neural Fuzzy Inference System, we describe the control strategy which is used on our virtual nonholonomic robot. And finally, we give the simulations’ results where the robot have to pass into a narrow path as well as the first validation results concerning the implementation of the proposed concepts on real robot.

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>

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