Proportional–integral-derivative controller with inlet derivative filter fine-tuning of a double-pendulum gantry crane system by a multi-objective genetic algorithm

2019 ◽  
Vol 52 (3) ◽  
pp. 527-548 ◽  
Author(s):  
Mahmoud H. Abdel-razak ◽  
Atef A. Ata ◽  
Khaled T. Mohamed ◽  
Eman H. Haraz
Keyword(s):  
Genetic Algorithm ◽  
Fine Tuning ◽  
Gantry Crane ◽  
Double Pendulum ◽  
Proportional Integral Derivative ◽  
Proportional Integral Derivative Controller ◽  
Proportional Integral ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Derivative Filter

Multi-objective genetic algorithm fractional-order PID controller for semi-active magnetorheologically damped seat suspension

2015 ◽  
Vol 23 (8) ◽  
pp. 1248-1266 ◽  
Author(s):  
S Gad ◽  
H Metered ◽  
A Bassuiny ◽  
AM Abdel Ghany
Keyword(s):  
Genetic Algorithm ◽  
Fractional Order ◽  
Ride Comfort ◽  
Mr Damper ◽  
Suspension System ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Multi Objective ◽  
Seat Suspension ◽  
Multi Objective Genetic Algorithm

Recently, fractional-order proportional–integral–derivative (FOPID) controllers are demonstrated as a general form of the classical proportional–integral–derivative (PID) using fractional calculus. In FOPID controller, the orders of the derivative and integral portions are not integers which offer more flexibility in succeeding control objectives. This paper proposes a multi-objective genetic algorithm (MOGA) to optimize the FOPID controller gains to enhance the ride comfort of heavy vehicles. The usage of magnetorheological (MR) damper in seat suspension system provides considerable benefits in this area. The proposed semi-active control algorithm consists of a system controller that determines the desired damping force using a FOPID controller tuned using a MOGA, and a continuous state damper controller that calculates the input voltage to the damper coil. A mathematical model of a six degrees–of–freedom seat suspension system incorporating human body model using an MR damper is derived and simulated using Matlab/Simulink software. The proposed semi–active MR seat suspension is compared to the classical PID, optimum PID tuned using genetic algorithm (GA) and passive seat suspension systems for predetermined chassis displacement. System performance criteria are examined in both time and frequency domains, in order to verify the success of the proposed FOPID algorithm. The simulation results prove that the proposed FOPID controller of MR seat suspension offers a superior performance of the ride comfort over the integer controllers.

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.

scholarly journals Design optimization of multi-objective proportional–integral–derivative controllers for enhanced handling quality of a twin-engine, propeller-driven airplane

2020 ◽  
Vol 12 (6) ◽  
pp. 168781402092317
Author(s):  
Mohsen Rostami ◽  
Joon Chung ◽  
Hyeong Uk Park
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Proportional Integral Derivative ◽  
Optimum Result ◽  
Proportional Integral ◽  
Multi Objective ◽  
Directional Motion ◽  
The Stability ◽  
Handling Quality

Herein, the design optimization of multi-objective controllers for the lateral–directional motion using proportional–integral–derivative controllers for a twin-engine, propeller-driven airplane is presented. The design optimization has been accomplished using the genetic algorithm and the main goal was to enhance the handling quality of the aircraft. The proportional–integral–derivative controllers have been designed such that not only the stability of the lateral–directional motion was satisfied but also the optimum result in longitudinal trim condition was achieved through genetic algorithm. Using genetic algorithm optimization, the handling quality was improved and placed in level 1 from level 2 for the proposed aircraft. A comprehensive sensitivity analysis to different velocities, altitudes and centre of mass positions is presented. Also, the performance of the genetic algorithm has been compared to the case where the particle swarm optimization tool is implemented. In this work, the aerodynamic coefficients as well as the stability and control derivatives were predicted using analytical and semi-empirical methods validated for this type of aircraft.

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.

Sign in / Sign up

Export Citation Format

Share Document