scholarly journals Fault Tolerant Control Using Proportional-Integral-Derivative Controller Tuned by Genetic Algorithm

2011 ◽  
Vol 7 (8) ◽  
pp. 1187-1193 ◽  
Author(s):  
Kanthalakshmi
Keyword(s):  
Genetic Algorithm ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Proportional Integral Derivative ◽  
Proportional Integral Derivative Controller ◽  
Proportional Integral

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.

Comparative Study of Fuzzy PID and PID controller optimized with Spider Monkey Optimization for a Robotic Manipulator System

2019 ◽  
Vol 12 ◽  
Author(s):  
Alka Agrawal ◽  
Vishal Goyal ◽  
Puneet Mishra
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Optimization Technique ◽  
Absolute Error ◽  
Robotic Manipulator ◽  
Spider Monkey ◽  
Proportional Integral Derivative ◽  
Proportional Integral Derivative Controller ◽  
Proportional Integral ◽  
Spider Monkey Optimization

Background: Robotic manipulator system has been useful in many areas like chemical industries, automobile, medical fields etc. Therefore, it is essential to implement a controller for controlling the end position of a robotic armeffectively. However, with the increasing non-linearity and the complexities of a robotic manipulator system, a conventional Proportional-Integral-Derivative controller has become ineffective. Nowadays, intelligent techniques like fuzzy logic, neural network and optimization algorithms has emerged as an efficient tool for controlling the highly complex non-linear functions with uncertain dynamics. Objective: To implement an efficient and robustcontroller using Fuzzy Logic to effectively control the end position of Single link Robotic Manipulator to follow the desired trajectory. Methods: In this paper, a Fuzzy Proportional-Integral-Derivativecontroller is implemented whose parameters are obtainedwith the Spider Monkey Optimization technique taking Integral of Absolute Error as an objective function. Results: Simulated results ofoutput of the plants controlled byFuzzy Proportional-Integral-Derivative controller have been shown in this paper and the superiority of the implemented controller has also been described by comparing itwith the conventional Proportional-Integral-Derivative controller and Genetic Algorithm optimization technique. Conclusion: From results, it is clear that the FuzzyProportional-Integral-Derivativeoptimized with the Spider monkey optimization technique is more accurate, fast and robust as compared to the Proportional-Integral-Derivativecontroller as well as the controllers optimized with the Genetic algorithm techniques.Also, by comparing the integral absolute error values of all the controllers, it has been found that the controller optimized with the Spider Monkey Optimization technique shows 99% better efficacy than the genetic algorithm technique.

Proportional Integral Derivative Based Fault Tolerant Control for a Gas Turbine

2010 ◽  
Vol 132 (11) ◽  
Author(s):  
Marino Sanchez-Parra ◽  
Cristina Verde ◽  
Dionisio A. Suarez
Keyword(s):  
Gas Turbine ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Combined Cycle ◽  
Fault Detection And Isolation ◽  
Proportional Integral Derivative ◽  
Switching Scheme ◽  
Isolation System ◽  
Proportional Integral ◽  
Mechanical Faults

This paper presents a fault tolerant control switching scheme with embedded fault detection and isolation system for the gas turbine of a combined cycle power plant. Mechanical faults of the turbogenerator are considered as case study. Previous to the control design, the detection and isolation capabilities of the gas turbine are studied by applying structural analysis to a first principles based gas turbine model. As a result, a new sensor was suggested to improve the detectability and isolability with respect to mechanical faults and sensors faults. Furthermore, the active fault tolerant control developed is based on stabilizing families of proportional integral derivative (PID) controllers, which are tuned off-line and the plant switching scheme preserves the stability of the whole closed-loop system, thanks to a careful selection of controller according to fault conditions. Simulation results with nonlinear model show the potential of the procedure.

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