STUDY ON THE EFFECT OF SHIFTING 'ZERO' IN OUTPUT MEMBERSHIP FUNCTION ON FUZZY LOGIC CONTROLLER OF THE ROV USING MICRO-BOX INTERFACING

2015 ◽  
Vol 74 (9) ◽  
Author(s):  
Mohd Shahrieel Mohd Aras ◽  
Fadilah Abdul Azis ◽  
Shahrum Shah Abdullah ◽  
Lee Dai Cong ◽  
Lim Wee Teck ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Membership Function ◽  
Fuzzy Logic Controller ◽  
Control Method ◽  
Remotely Operated Vehicle ◽  
Simple Method ◽  
Depth Control ◽  
Limit Time

This paper investigates the study on the effect of shifting ‘zero’ membership function on Fuzzy Logic Controller (FLC) design of underwater Remotely Operated Vehicle (ROV) for depth control using Micro-box 2000/2000C interfacing based on thruster system. The issues occurred with a ROV design is where the thruster system can easily drain up current from the supply (e.g. battery source or power bank) and this will limit time to using the ROV. FLC do not have a rigid approach to tune it and may cause the process of tuning will be highly time costing. Therefore, a simple method by a study on the effect of shifting zero membership function will act as a one technique to tune the FLC for future references. The ROV Trainer will be developed to test the proposed control method using Micro-box 2000/2000C. The ROV Trainer consists of aluminum box, thrusters, drivers, interface connector, and etc and interfacing with Micro-box act as microcontroller. Fuzzy logic toolbox in MATLAB will be used to study the shifting zero membership function so that the effect of the adjustment can be investigated. The result of this project shows that, by shifting zero membership function of the fuzzy logic controller, the performance of the fuzzy logic controller is normally improved.

scholarly journals A new tuning approach of Single Input Fuzzy Logic Controller (SIFLC) for Remotely Operated Vehicle (ROV) Depth Control

Evergreen ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 651-657
Author(s):  
Fauzal Naim Zohedi ◽  
Mohd Shahrieel Mohd Aras ◽  
Hyreil Anuar Kasdirin ◽  
Mohd Bazli Bahar
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Remotely Operated Vehicle ◽  
Depth Control ◽  
Single Input

scholarly journals Execution of BLDC Motor using Fuzzy Logic Controller on Propulsion Application for Hybrid Vehicle System

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3989-3993
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Control Method ◽  
Torque Ripple ◽  
Dc Motor ◽  
Brushless Dc Motor ◽  
Rotor Speed ◽  
Brushless Dc Motors ◽  
Brushless Dc ◽  
Sensor Control

This research Paper proposes the Brushless DC motors control (BLDC) could accomplish higher execution looking into effectiveness in examination for old brushed DC motor controlling which is difficult to control because it requires a phase for switching circuit. This work proposes a fuzzy logic control for brushless DC motor for axis based on Hall Effect by applying sensor control system and also it produces brushless motor for rearranging the three phase conduction mode model. At long last this paper may be with create efficient control methodologies on enhance driving dynamics on the mechanical dynamic consider of propulsion method. The recommended control method stabilizes those controls services (speeds) done by controller of brushless DC motor drive (BLDC). On behalf of settling 2 wheels also physical favorable circumstances of BLDC motors are associated straight forwardly of the tires by improving the rotor speed. The parameters such as power factor, rotor speed, torque ripple, EMF is compensated & simulation results are tabulated.

scholarly journals Dynamic response improvement of hybrid system by implementing ANN-GA for fast variation of photovoltaic irradiation and FLC for wind turbine

2015 ◽  
Vol 64 (2) ◽  
pp. 291-314 ◽  
Author(s):  
Maziar Izadbakhsh ◽  
Alireza Rezvani ◽  
Majid Gandomkar
Keyword(s):  
Fuzzy Logic ◽  
Wind Speed ◽  
Dynamic Response ◽  
Wind Turbine ◽  
Hybrid System ◽  
Pitch Angle ◽  
Fuzzy Logic Controller ◽  
Control Method ◽  
Control Technique ◽  
Power Curves

Abstract In this paper, dynamic response improvement of the grid connected hybrid system comprising of the wind power generation system (WPGS) and the photovoltaic (PV) are investigated under some critical circumstances. In order to maximize the output of solar arrays, a maximum power point tracking (MPPT) technique is presented. In this paper, an intelligent control technique using the artificial neural network (ANN) and the genetic algorithm (GA) are proposed to control the MPPT for a PV system under varying irradiation and temperature conditions. The ANN-GA control method is compared with the perturb and observe (P&O), the incremental conductance (IC) and the fuzzy logic methods. In other words, the data is optimized by GA and then, these optimum values are used in ANN. The results are indicated the ANN-GA is better and more reliable method in comparison with the conventional algorithms. The allocation of a pitch angle strategy based on the fuzzy logic controller (FLC) and comparison with conventional PI controller in high rated wind speed areas are carried out. Moreover, the pitch angle based on FLC with the wind speed and active power as the inputs can have faster response that lead to smoother power curves, improving the dynamic performance of the wind turbine and prevent the mechanical fatigues of the generator

A new control method based on fuzzy controller, time delay estimation, deep learning, and non-dominated sorting genetic algorithm-III for powertrain mount system

2019 ◽  
Vol 26 (13-14) ◽  
pp. 1187-1198 ◽  
Author(s):  
Li-Xin Guo ◽  
Dinh-Nam Dao
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Deep Learning ◽  
Fuzzy Logic Controller ◽  
Control Method ◽  
Time Delay Estimation ◽  
Delay Estimation ◽  
Proportional Integral Derivative ◽  
Proportional Integral Derivative Controller ◽  
Proportional Integral

This article presents a new control method based on fuzzy controller, time delay estimation, deep learning, and non-dominated sorting genetic algorithm-III for the nonlinear active mount systems. The proposed method, intelligent adapter fractions proportional–integral–derivative controller, is a smart combination of the time delay estimation control and intelligent fractions proportional–integral–derivative with adaptive control parameters following the speed range of engine rotation via the deep neural network with the optimal non-dominated sorting genetic algorithm-III deep learning algorithm. Besides, we proposed optimal fuzzy logic controller with optimal parameters via particle swarm optimization algorithm to control reciprocal compensation to eliminate errors for intelligent adapter fractions proportional–integral–derivative controller. The control objective is to deal with the classical conflict between minimizing engine vibration impacts on the chassis to increase the ride comfort and keeping the dynamic wheel load small to ensure the ride safety. The results of this control method are compared with that of traditional proportional–integral–derivative controller systems, optimal proportional–integral–derivative controller parameter adjustment using genetic algorithms, linear–quadratic regulator control algorithms, and passive drive system mounts. The results are tested in both time and frequency domains to verify the success of the proposed optimal fuzzy logic controller–intelligent adapter fractions proportional–integral–derivative control system. The results show that the proposed optimal fuzzy logic controller–intelligent adapter fractions proportional–integral–derivative control system of the active engine mount system gives very good results in comfort and softness when riding compared with other controllers.

Attitude control of a spacecraft using an extended self-organizing fuzzy logic controller

1987 ◽  
Vol 201 (2) ◽  
pp. 97-106 ◽  
Author(s):  
S Daley ◽  
K F Gill
Keyword(s):  
Fuzzy Logic ◽  
Attitude Control ◽  
Fuzzy Logic Controller ◽  
The Self ◽  
Extended Self ◽  
Simple Method ◽  
Excellent Control ◽  
High Degree ◽  
Self Organizing

A simple method for extending the range of sensitivity of the self-organizing fuzzy logic controller (SOC) is proposed. The performance of the resulting controller is studied through its application to the attitude control of a flexible satellite. It is found that the extended SOC can provide excellent control and also possesses a high degree of robustness.

DEPTH CONTROL OF AN UNDERWATER REMOTELY OPERATED VEHICLE USING NEURAL NETWORK PREDICTIVE CONTROL

2015 ◽  
Vol 74 (9) ◽  
Author(s):  
Mohd Shahrieel Mohd Aras ◽  
Shahrum Shah Abdullah ◽  
Ahmad Fadzli Nizam Abdul Rahman ◽  
Norhaslinda Hasim ◽  
Fadilah Abdul Azis ◽  
...  
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Fuzzy Logic Controller ◽  
Pi Controller ◽  
System Response ◽  
Linear Quadratic ◽  
Remotely Operated Vehicle ◽  
Depth Control ◽  
Linear Quadratic Regulation ◽  
Neural Network Predictive Control

This paper investigates the depth control of an unmanned underwater remotely operated vehicle (ROV) using neural network predictive control (NNPC). The NNPC is applied to control the depth of the ROV to improve the performances of system response in terms of overshoot. To assess the viability of the method, the system was simulated using MATLAB/Simulink by neural network predictive control toolbox. In this paper also investigates the number of data samples (1000, 5000 and 10,000) to train neural network. The simulation reveals that the NNPC has the better performance in terms of its response, but the execution time will be increased. The comparison between other controller such as conventional PI controller, Linear Quadratic Regulation (LQR) and fuzzy logic controller also covered in this paper where the main advantage of NNPC is the fastest system response on depth control. 

Hybrid Anti-Windup Fuzzy PI Controller Based Direct Torque Control of Three Phase Induction Motor

2014 ◽  
Vol 573 ◽  
pp. 155-160
Author(s):  
A. Pandian ◽  
R. Dhanasekaran
Keyword(s):  
Fuzzy Logic ◽  
Induction Motor ◽  
Fuzzy Logic Controller ◽  
Control Method ◽  
Fuzzy Controller ◽  
Direct Torque Control ◽  
Operating Conditions ◽  
Torque Control ◽  
Constant Speed ◽  
Three Phase

This paper presents improved Fuzzy Logic Controller (FLC) of the Direct Torque Control (DTC) of Three-Phase Induction Motor (IM) for high performance and torque control industrial drive applications. The performance of the IM using PI Controllers and general fuzzy controllers are meager level under load disturbances and transient conditions. The FLC is extended to have a less computational burden which makes it suitable for real time implementation particularly at constant speed and torque disturbance operating conditions. Hybrid control has advantage of integrating a superiority of two or more control techniques for better control performances. A fuzzy controller offers better speed responses for startup and large speed errors. If the nature of the load torque is varied, the steady state speed error of DTC based IM drive with fuzzy logic controller becomes significant. To improve the performance of the system, a new control method, Hybrid fuzzy PI control is proposed. The effectiveness of proposed method is verified by simulation based on MATLAB. The proposed Hybrid fuzzy controller has adaptive control over load toque variation and can maintain constant speed.

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