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

Tuning Factor the Single Input Fuzzy Logic Controller to Improve the Performances of Depth Control for Underwater Remotely Operated Vehicle

2013 ◽  
Author(s):  
Mohd Shahrieel Mohd Aras ◽  
Anuar Mohamed Kassim ◽  
Alias Khamis ◽  
Shahrum Shah Abdullah ◽  
Muhammad Azhar Abd Aziz
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Remotely Operated Vehicle ◽  
Depth Control ◽  
Single Input

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.

Electric Motor Drive Using Single Input Fuzzy Logic Controller for Husk Extraction in Rice Mill Industry

2021 ◽  
pp. 403-413
Author(s):  
A. Jagadeesh ◽  
K. Deepa ◽  
K. Sireesha
Keyword(s):  
Fuzzy Logic ◽  
Electric Motor ◽  
Fuzzy Logic Controller ◽  
Motor Drive ◽  
Single Input ◽  
Electric Motor Drive

Efficiency optimization of a DSP-based standalone PV system using a stable single input fuzzy logic controller

2015 ◽  
Vol 49 ◽  
pp. 907-920 ◽  
Author(s):  
Maissa Farhat ◽  
Oscar Barambones ◽  
Lassaâd Sbita
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Pv System ◽  
Efficiency Optimization ◽  
Single Input

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. 

Design of a single-input fuzzy logic controller and its properties

1999 ◽  
Vol 106 (3) ◽  
pp. 299-308 ◽  
Author(s):  
Byung-Jae Choi ◽  
Seong-Woo Kwak ◽  
Byung Kook Kim
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Single Input

Design and stability analysis of single-input fuzzy logic controller

2000 ◽  
Vol 30 (2) ◽  
pp. 303-309 ◽  
Author(s):  
Byung-Jae Choi ◽  
Seong-Woo Kwak ◽  
Byung Kook Kim
Keyword(s):  
Fuzzy Logic ◽  
Stability Analysis ◽  
Fuzzy Logic Controller ◽  
Single Input

Design of a Single-input Adaptive Fuzzy Logic Controller using a Switching Hyperplane

2000 ◽  
Vol 4 (5) ◽  
pp. 321-326
Author(s):  
Byung-Jae Choi ◽  
◽  
Seong-Woo Kwak ◽  
Byung Kook Kim ◽  
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Sliding Mode ◽  
Tracking Error ◽  
Operating Conditions ◽  
Pendulum System ◽  
Adaptive Fuzzy ◽  
Inverted Pendulum System ◽  
Adaptive Fuzzy Logic ◽  
Single Input

Most fuzzy logic controllers (FLCs) for minimum phase plants use an UNLP (Upper Negative and Lower Positive) or UPLN type control rule table. This property allows design of a single-input FLC using a sole input fuzzy variable, which is called the SFLC (single-input FLC). It greatly simplifies the design procedure of the conventional FLC and has many advantages. However, it is still difficult to adapt to varying operating conditions. We here design a single-input adaptive fuzzy logic controller (SAFLC) using a switching hyperplane introduced in sliding mode control. In the proposed SAFLC, some parameters of membership functions characterizing linguistic terms of fuzzy rules are adjusted by an adaptive law that directly incorporates linguistic fuzzy control rules into the controller. We also prove that 1) the closed-loop system is globally stable in the sense that all signals involved are bounded and 2) its tracking error converges to zero asymptotically. We perform computer simulation using an inverted pendulum system.

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