Modelling and simulation of a fuzzy PID controller for active suspension system

2010 ◽  
Author(s):  
Dong-Kai Shen ◽  
Xue-qin Ling ◽  
Jie Liu ◽  
Hao Wang
Keyword(s):  
Pid Controller ◽  
Active Suspension ◽  
Suspension System ◽  
Modelling And Simulation ◽  
Fuzzy Pid ◽  
Fuzzy Pid Controller

Design of Fuzzy-PID Controller for Ride Comfort Enhancement of a Half-Vehicle with Active Suspension System

2013 ◽  
Vol 313-314 ◽  
pp. 382-386
Author(s):  
Wen Kui Lan ◽  
Er Dong Ni
Keyword(s):  
Pid Controller ◽  
Passive Control ◽  
Ride Comfort ◽  
Control Method ◽  
Active Suspension ◽  
Suspension System ◽  
The Body ◽  
Fuzzy Pid ◽  
Vehicle Model ◽  
Fuzzy Pid Controller

A fuzzy-PID controller is developed and applied to the active suspension system for the ride comfort enhancement of a half-vehicle model. A four degree-of-freedom vehicle model with active suspension system is proposed, which focused on the passenger’s ride comfort performance, and a fuzzy-PID controller is developed by incorporating the fuzzy logic control mechanism into the modifications of the PID structure. The performance of the proposed controller has been verified by comparing it with passive control method in MATLAB/Simulink. The simulation results indicate that the developed fuzzy-PID controller enhances the ride comfort performance of the vehicle active suspension system by reducing the body acceleration and pitch angle significantly.

Influence of Fuzzy-PID Controller on Semi-Active Suspension System Performance Using Magnetorheological Damper Fuzzy Model

2014 ◽  
Vol 663 ◽  
pp. 243-247 ◽  
Author(s):  
Mohammadjavad Zeinali ◽  
Saiful Amri Mazlan ◽  
Mohd Azizi Abdul Rahman
Keyword(s):  
Pid Controller ◽  
Fuzzy Model ◽  
Active Suspension ◽  
Suspension System ◽  
Magnetorheological Damper ◽  
Fuzzy Pid ◽  
Improve Performance ◽  
Fuzzy Pid Controller ◽  
Neuro Fuzzy ◽  
Better Than

Semi-active suspension system is a promising device to improve performance of the suspension system by using optimal controller for magnetorheological damper. The importance of magnetorheological damper is the capability to control the semi-active suspension system by adjusting the input current exerted to the coil of wire to produce magnetic field. In this paper, a fuzzy-PID controller has been applied in a quarter car semi-active suspension system to examine the performance of the system. The whole suspension system is modelled in Simulink environment/MATLAB software in which a neuro-fuzzy model of magnetorheological damper is utilized as a mathematical model of the damper. A disturbance profile is utilized to evaluate performance of the system. Simulation results show that the proposed semi-active suspension system has successfully absorbed disturbances much better than PID controller. In addition, the accuracy of the magnetorheological damper model influences the performance of the semi-active suspension system.

A GSA-based adaptive fuzzy PID-controller for an active suspension system

2016 ◽  
Vol 33 (6) ◽  
pp. 1659-1667 ◽  
Author(s):  
Chun-Tang Chao ◽  
Ming-Tang Liu ◽  
Juing-Shian Chiou ◽  
Yi-Jung Huang ◽  
Chi-Jo Wang
Keyword(s):  
Pid Controller ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Suspension System ◽  
Fuzzy Pid ◽  
Content Type ◽  
Fuzzy Pid Controller ◽  
Fast Learning ◽  
Automobile Suspension ◽  
Hybrid Fuzzy Pid

Purpose – The purpose of this paper is to propose a novel design for determining the optimal hybrid fuzzy PID-controller of an active automobile suspension system, employing the gravitational search algorithm (GSA). Design/methodology/approach – The hybrid fuzzy PID-controller structure is an improvement to fuzzy PID-controller by incorporating a fast learning PID-controller. Findings – The GSA can adjust the parameters of the PID-controller to achieve the optimal performance. Research limitations/implications – The GSA may have the advantage of quick convergence, but the required computation may be intensive. Practical implications – The simulation results demonstrate the effectiveness of the proposed approach on active automobile suspension system. Originality/value – In order to demonstrate the theoretical guarantee of the proposed method, comparisons with particle swarm optimization or other methods has also been carried out.

Active Suspension Control Based on Particle Swarm Optimization

2020 ◽  
Vol 13 (1) ◽  
pp. 60-78
Author(s):  
Shaobin Lv ◽  
Guoqiang Chen ◽  
Jun Dai
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Ride Comfort ◽  
Control Strategies ◽  
Particle Swarm ◽  
Active Suspension ◽  
Fuzzy Pid ◽  
Swarm Optimization ◽  
Fuzzy Pid Controller ◽  
Suspension Control

Background: The active suspension can be adjusted in real time according to the change of road condition and vehicle state to enhance the performance of active suspension that has received widespread attention. Suspension control strategies and actuators are the key issues of the active suspension, and are the main research directions for active suspension patents. Objective: The numerical analysis method is proposed to study the performance characteristics of the active suspension controlled by different controllers. Methods: The active suspension control model and control strategy based on particle swarm optimization are established, and two active suspensions controlled by the sliding mode controller and the fuzzy PID controller are proposed. Moreover, two active suspension systems are optimized by particle swarm optimization. Results: The results of the analysis show that the performance of the active suspension is significantly improved compared with the passive suspension when the vehicle runs on the same road. The ride comfort of the active suspension controlled by the fuzzy PID controller has the best adaptive performance when the vehicle runs on different grade roads or white noise roads. The active suspension controlled by the fuzzy PID controller has the best ride comfort. Conclusion: A good control strategy can effectively improve the performance of the active suspension. To improve the performance of the active suspension, it can be controlled by utilizing different control strategies. The results lay a foundation for the active suspension experiments, the dynamic analysis and the optimization design of suspension structure.

scholarly journals MODELLING OF ACTIVE SUSPENSION SYSTEM FOR QUARTER CAR (PID CONTROL, MATLAB)

2021 ◽  
Vol 5 (10) ◽  
Author(s):  
Danish Saifi ◽  
Pramod Kumar
Keyword(s):  
Pid Control ◽  
Pid Controller ◽  
Active Suspension ◽  
Suspension System ◽  
Modelling And Simulation ◽  
Pid Controllers ◽  
Proportional Integral Derivative ◽  
Control Loop ◽  
Proportional Integral ◽  
Fine Control

We are discussing active suspension in this research. It also includes an actuator or controller (ECU), wheels and body. The rider feels comfort in travelling due to the use of these types of suspension. Because it controls vertical moments or moves of the wheels and stable rider or passenger. It is most important in the automobile industries. There are many types of controllers used for fine control to vibration caused by wheels. E.g., PID controllers, it stands for Proportional Integral Derivative. PID controller provides better simultaneous vibration of the output of the control loop. It also used for improving the performance of the suspension system. We can do modelling and simulation carried out in MATLAB software for active suspension.

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