Optimized Proportional Integral Derivative Controller of Vehicle Active Suspension System Using Genetic Algorithm

2018 ◽  
Author(s):  
H. Metered ◽  
W. Abbas ◽  
A. S. Emam
Keyword(s):  
Genetic Algorithm ◽  
Active Suspension ◽  
Suspension System ◽  
Proportional Integral Derivative ◽  
Proportional Integral Derivative Controller ◽  
Proportional Integral

Investigation and performance comparison of ride comfort on the created human vehicle road integrated model adopting genetic algorithm optimized proportional integral derivative control technique

2020 ◽  
Vol 234 (2) ◽  
pp. 288-305 ◽  
Author(s):  
Arivazhagan Anandan ◽  
Arunachalam Kandavel
Keyword(s):  
Genetic Algorithm ◽  
Ride Comfort ◽  
Active Suspension ◽  
Absolute Error ◽  
Integrated Model ◽  
Suspension System ◽  
Vehicle Stability ◽  
Head Acceleration ◽  
Proportional Integral Derivative ◽  
Proportional Integral

This context exhaustively investigates the ride comfort performance index on the proposed active suspension vehicle system. Ride comfort in terms of occupants (includes driver and passenger) head acceleration, sprung mass vertical and pitching accelerations is considered. For this examination, a 14-degree-of-freedom human vehicle road integrated system model was extensively developed. Then, an active suspension system composed of a hydraulic actuator and proportional-integral-derivative controller is incorporated into the developed vehicle model to enhance the ride comfort. Besides, the designed controller needs to satisfy other vehicle performance indices like vehicle stability and ride safety. Accordingly, the controller parameters were optimally tunned with the help of genetic algorithm technique, on the basis of integral time absolute error criterion. The objective function was created on the basis of minimizing the integral time absolute error of sprung mass displacement, suspension working space and tire deflection responses. The entire response of human vehicle road integrated model, with the proposed active suspension system and passive suspension system on various random road surfaces (A, B, C, D and E with respect to ISO 8608) with five constant speeds (20, 40, 60, 80 and 100 kmph), was compared via surficial presentation. Furthermore, the comfort measures such as root mean square and vibration dose value from ISO 2631-1 were adopted to evaluate the severity between the occupants via head acceleration response. The simulation results showed that the suggested active suspension system significantly improved the ride comfort with guaranteed vehicle stability and ride safety.

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.

Optimized Fractional-Order Proportional Integral Derivative Controller for Active Vehicle Suspension System Performance Enhancement

2018 ◽  
Vol 10 (4) ◽  
Author(s):  
A.S. Emam ◽  
H. Metered ◽  
A.M. Abdel Ghany
Keyword(s):  
Fractional Order ◽  
Performance Enhancement ◽  
Ride Comfort ◽  
Active Suspension ◽  
Suspension System ◽  
Vehicle Suspension ◽  
Vehicle Stability ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Vehicle Suspension System

In this paper, an optimal Fractional Order Proportional Integral Derivative (FOPID) controller is applied in vehicle active suspension system to improve the ride comfort and vehicle stability without consideration of the actuator. The optimal values of the five gains of FOPID controller to minimize the objective function are tuned using a Multi-Objective Genetic Algorithm (MOGA). A half vehicle suspension system is modelled mathematically as 6 degrees-of-freedom mechanical system and then simulated using Matlab/Simulink software. The performance of the active suspension with FOPID controller is compared with passive suspension system under bump road excitation to show the efficiency of the proposed controller. The simulation results show that the active suspension system using the FOPID controller can offer a significant enhancement of ride comfort and vehicle stability.

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.

Parametric Study on Proportional Integral Derivative Controlled Semi-Active Suspension System

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
E.M Allam ◽  
M.A.A Emam ◽  
Eid.S Mohamed
Keyword(s):  
Active Suspension ◽  
Suspension System ◽  
Proportional Integral Derivative ◽  
Active System ◽  
Proportional Integral ◽  
Quarter Car Model ◽  
Suspension Systems ◽  
Working Space ◽  
Active Suspension Systems ◽  
Quarter Car

This paper presents the effect of the suspension working space, body displacement, body acceleration and wheel displacement for the non-controlled suspension system (passive system) and the controlled suspension system of a quarter car model (semi-active system), and comparison between them. The quarter car passive and semi-active suspension systems are modelled using Simulink. Proportional Integral Derivative controllers are incorporated in the design scheme of semi-active models. In the experimental work, the influence of switchable damper in a suspension system is compared with the passive and semi-active suspension systems.

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