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.

Development of Active Suspension System for a Pot Hole Using PID Controller

2011 ◽  
Vol 308-310 ◽  
pp. 2266-2270
Author(s):  
Mouleeswaran Senthilkumar
Keyword(s):  
Pid Controller ◽  
Ride Comfort ◽  
Control Method ◽  
Controller Design ◽  
Active Suspension ◽  
Suspension System ◽  
Operating Conditions ◽  
Hydraulic Actuator ◽  
Time Domains ◽  
Spool Valve

This paper describes the development of a controller design for the active control of suspension system, which improves the inherent tradeoff among ride comfort, suspension travel and road-holding ability. The developed design allows the suspension system to behave differently in different operating conditions, without compromising on road-holding ability. The effectiveness of this control method has been explained by data from time domains. Proportional-Integral-Derivative (PID) controller including hydraulic dynamics has been developed. The displacement of hydraulic actuator and spool valve is also considered. The Ziegler – Nichols tuning rules are used to determine proportional gain, reset rate and derivative time of PID controller. Simulink diagram of active suspension system is developed and analysed using MATLAB software. The investigations on the performance of the developed active suspension system are demonstrated through comparative simulations in this paper.

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.

Implementations of PID Controller and its Transient Behaviour in Active Suspension System

2014 ◽  
Vol 895 ◽  
pp. 490-499 ◽  
Author(s):  
Noor Hafizah Amer ◽  
Rahizar Ramli ◽  
Wan Nor Liza Wan Mahadi ◽  
Mohd Azman Zainul Abidin ◽  
Zainab Rasol
Keyword(s):  
Pid Controller ◽  
Ride Comfort ◽  
Research Effort ◽  
Active Suspension ◽  
Suspension System ◽  
The Body ◽  
Pid Controllers ◽  
Active Force ◽  
Transient Behaviour ◽  
Vehicle Suspension System

Advancement in computational technologies has accelerated the research effort in exploring the possibility of semi-active and active suspension. Computational simulations were used widely in the studies of the controller strategies. Among them are PID controllers. Studies from previous work suggested that PID controllers are capable of improving ride comfort and road holding capability. However, very little emphasis is given to examine the whole state of the vehicle suspension system resulted from this implementation. Therefore this study will examine the application of a basic PID controller to an active suspension system (ASS) to determine the requirement of active force that should be delivered in stabilizing the whole system. Two different configurations of electromagnetic suspension system (EMS) will be considered. One variable in the vehicle system will be selected to be the controlled output of PID controller and its effect to overall vehicle state will be observed. In the cases that the PID was able to stabilize the body responses, deterioration was noticed in the wheel responses. While it stabilizes the controlled output, the active force from controller was channelled into deteriorating other vehicle parameters.

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.

Study on Ride Comfort of Automobile Active Suspension

2014 ◽  
Vol 505-506 ◽  
pp. 356-359
Author(s):  
Xiang Chen ◽  
Lin Yan Zhang
Keyword(s):  
Pid Controller ◽  
Ride Comfort ◽  
Fuzzy Controller ◽  
Uncertain System ◽  
Active Suspension ◽  
Step Function ◽  
Fuzzy Pid ◽  
Adaptive Fuzzy ◽  
Fuzzy Pid Controller ◽  
Self Adaptive

A half-vehicle model with 4-DOF was built up. In response to a nonlinear, time delay and uncertain system of automobile suspension, an improved self-adaptive fuzzy PID controller of the active suspension was designed. Taking SANTANA2000s suspension parameter for simulation and taking a step function as the input of simulation road, time-domain simulation in Matlab was finished. The simulation result indicates that the ride comfort was improved obviously by using the active suspension with improved self-adaptive fuzzy PID controller. The time of response reach to steady state was shortened notably. The active suspension with improved self-adaptive fuzzy PID controller is superior to the passive suspension and the simple active suspension with fuzzy controller in improving the ride comfort and maneuverability. All of these advantages are of considerable referential values in development of vehicles active suspension controller.

Performance analysis of MR damper based semi-active suspension system using optimally tuned controllers

2021 ◽  
pp. 095440702110044
Author(s):  
Gurubasavaraju Tharehalli mata ◽  
Vijay Mokenapalli ◽  
Hemanth Krishna
Keyword(s):  
Pid Controller ◽  
Ride Comfort ◽  
Sliding Mode ◽  
Dynamic Performance ◽  
Parametric Method ◽  
Mr Damper ◽  
Active Suspension ◽  
Suspension System ◽  
Sliding Mode Controller ◽  
Road Excitation

This study assesses the dynamic performance of the semi-active quarter car vehicle under random road conditions through a new approach. The monotube MR damper is modelled using non-parametric method based on the dynamic characteristics obtained from the experiments. This model is used as the variable damper in a semi-active suspension. In order to control the vibration caused under random road excitation, an optimal sliding mode controller (SMC) is utilised. Particle swarm optimisation (PSO) is coupled to identify the parameters of the SMC. Three optimal criteria are used for determining the best sliding mode controller parameters which are later used in estimating the ride comfort and road handling of a semi-active suspension system. A comparison between the SMC, Skyhook, Ground hook and PID controller suggests that the optimal parameters with SMC have better controllability than the PID controller. SMC has also provided better controllability than the PID controller at higher road roughness.

Design and Simulation of DC Speed Regulating System Based on Fuzzy Control

2012 ◽  
Vol 220-223 ◽  
pp. 1258-1261
Author(s):  
Li Hong Wang
Keyword(s):  
Pid Control ◽  
Pid Controller ◽  
Control Method ◽  
Dynamic State ◽  
State Function ◽  
Fuzzy Pid ◽  
Fuzzy Pid Controller ◽  
On Line ◽  
Start Process ◽  
The Ideal

PID control is adopted in traditional DC speed regulating system. In start process the current super adjustment value is big. When adding load perturbation and voltage perturbation suddenly, its dynamic state function will be decended. Aimming at this problem, a kind of improved system was put forward. The speed modulator used fuzzy PID controller, according to e and ec, the parameters of the modulator can be modified on line. The current modulator adopted integral separable PID control method. The simulation results indicated that the improved system has better dynamic state function and anti- Rao function. Particularly the start current wave closes to the ideal rectangle wave more. So the responding speed of the system can be sped.

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.

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