Optimisation of an Active Suspension Force Controllerusing Genetic Algorithm for Random Input

2007 ◽  
Vol 57 (5) ◽  
pp. 691-706 ◽  
Author(s):  
M.K. Hada ◽  
A. Menon ◽  
S.Y. Bhave
Keyword(s):  
Genetic Algorithm ◽  
Active Suspension ◽  
Random Input

scholarly journals Proportional-Integral State-Feedback Controller Optimization for a Full-Car Active Suspension Setup using a Genetic Algorithm

2018 ◽  
Vol 51 (4) ◽  
pp. 1-6
Author(s):  
Michiel Haemers ◽  
Stijn Derammelaere ◽  
Clara-Mihaela Ionescu ◽  
Kurt Stockman ◽  
Jasper De Viaene ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
State Feedback ◽  
Active Suspension ◽  
Feedback Controller ◽  
Proportional Integral ◽  
State Feedback Controller

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.

Vehicle Active Suspension with Four-Degrees of Freedom of Optimizing the Value of K Based on Genetic Algorithm

2011 ◽  
Vol 308-310 ◽  
pp. 1673-1678
Author(s):  
Yan Yan Zuo ◽  
Cai Bao Yan ◽  
Nan Yang
Keyword(s):  
Genetic Algorithm ◽  
Optimal Control ◽  
Optimal Control Theory ◽  
Degrees Of Freedom ◽  
Ride Comfort ◽  
Control Method ◽  
Active Suspension ◽  
Obvious Effect ◽  
Comprehensive Performance ◽  
Suspension Model

A vehicle active suspension model with 1 / 2 ，four-degrees of freedom is established and by combining genetic algorithm with optimal control theory，the author presents a new control method of active suspension that is to optimize the value of K controlled by LQG in default of road input based on genetic algorithm and makes a simulation in the environment of Matlab / Simulink. By simulation and analysis，the result indicates that，this method has an obvious effect on improving comprehensive performance of vehicles，such as ride comfort and operate stability and so on.

Design of an Active Suspension Control for a Four DOF Model Using Genetic Algorithm

2005 ◽  
Author(s):  
S. Gosselin-Brisson ◽  
M. Bouazara ◽  
M. J. Richard
Keyword(s):  
Genetic Algorithm ◽  
Degrees Of Freedom ◽  
Real Life ◽  
Active Suspension ◽  
Performance Criterion ◽  
Control Methods ◽  
Frequency Sensitivity ◽  
Suspension Control ◽  
Passive Suspension System ◽  
Real Vehicle

This paper presents the design of an active suspension controller for an automotive vehicle. A four degrees of freedom linear model is used to represent a vehicle with different front and rear characteristics. Filtered road and acceleration inputs are applied to the model to simulate real life use. The performance criterion are filtered to include frequency sensitivity and weighted based on a standard passive suspension system. Independent front and rear controllers are optimised with the genetic algorithm. The controller includes linear gains and frequency dependency to take advantage of these two different control methods. The number of sensors and the order of the filters are limited to facilitate implementation on a real vehicle.

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