scholarly journals Intelligent Neuro-Fuzzy Application in Semi-Active Suspension System

10.5772/35491 ◽  
2012 ◽  
Author(s):  
Seiyed Hamid ◽  
Atabak Sarrafan ◽  
Meisam Abbasi ◽  
Amir Ali Akbar Khayyat
Keyword(s):  
Active Suspension ◽  
Suspension System ◽  
Neuro Fuzzy

scholarly journals Adaptive Neuro Fuzzy Inference Controller for Full Vehicle Nonlinear Active Suspension Systems

2010 ◽  
Vol 6 (2) ◽  
pp. 97-106
Author(s):  
A. Aldair ◽  
W. J. Wang
Keyword(s):  
Fuzzy Inference ◽  
Active Suspension ◽  
Suspension System ◽  
The Body ◽  
Full Vehicle ◽  
Road Roughness ◽  
Neuro Fuzzy ◽  
Control Forces ◽  
Vehicle Suspension System ◽  
The Cost

The main objective of designed the controller for a vehicle suspension system is to reduce the discomfort sensed by passengers which arises from road roughness and to increase the ride handling associated with the pitching and rolling movements. This necessitates a very fast and accurate controller to meet as much control objectives, as possible. Therefore, this paper deals with an artificial intelligence Neuro-Fuzzy (NF) technique to design a robust controller to meet the control objectives. The advantage of this controller is that it can handle the nonlinearities faster than other conventional controllers. The approach of the proposed controller is to minimize the vibrations on each corner of vehicle by supplying control forces to suspension system when travelling on rough road. The other purpose for using the NF controller for vehicle model is to reduce the body inclinations that are made during intensive manoeuvres including braking and cornering. A full vehicle nonlinear active suspension system is introduced and tested. The robustness of the proposed controller is being assessed by comparing with an optimal Fractional Order PIλDμ (FOPID) controller. The results show that the intelligent NF controller has improved the dynamic response measured by decreasing the cost function.

Online Adaptive Neuro-Fuzzy Based Full Car Suspension Control Strategy

2015 ◽  
pp. 992-1039
Author(s):  
Laiq Khan ◽  
Shahid Qamar
Keyword(s):  
Degrees Of Freedom ◽  
Ride Comfort ◽  
Sliding Mode ◽  
Active Suspension ◽  
Suspension System ◽  
Car Model ◽  
Car Suspension ◽  
Neuro Fuzzy ◽  
Suspension Control ◽  
Soft Computing Technique

Suspension system of a vehicle is used to minimize the effect of different road disturbances for ride comfort and improvement of vehicle control. A passive suspension system responds only to the deflection of the strut. The main objective of this work is to design an efficient active suspension control for a full car model with 8-Degrees Of Freedom (DOF) using adaptive soft-computing technique. So, in this study, an Adaptive Neuro-Fuzzy based Sliding Mode Control (ANFSMC) strategy is used for full car active suspension control to improve the ride comfort and vehicle stability. The detailed mathematical model of ANFSMC has been developed and successfully applied to a full car model. The robustness of the presented ANFSMC has been proved on the basis of different performance indices. The analysis of MATLAB/SMULINK based simulation results reveals that the proposed ANFSMC has better ride comfort and vehicle handling as compared to Adaptive PID (APID), Adaptive Mamdani Fuzzy Logic (AMFL), passive, and semi-active suspension systems. The performance of the active suspension has been optimized in terms of displacement of seat, heave, pitch, and roll.

Design of active suspension controller for train cars based on sliding mode control, uncertainty observer and neuro-fuzzy system

2015 ◽  
Vol 23 (8) ◽  
pp. 1334-1353 ◽  
Author(s):  
Sy Dzung Nguyen ◽  
Quoc Hung Nguyen
Keyword(s):  
Dynamic Response ◽  
Sliding Mode Control ◽  
Active Control ◽  
Sliding Mode ◽  
Active Suspension ◽  
Suspension System ◽  
Magnetorheological Damper ◽  
Control Force ◽  
Mode Control ◽  
Neuro Fuzzy

This paper focuses on building a controller for active suspension system of train cars in the case that the sprung mass and model error are uncertainty parameters. The sprung mass is always varied due to many reasons such as changing of the passengers and load or impacting of wind on the operating train while an unknown difference between the suspension model used for survey and the real suspension system also always exists. The controller is built based on an adaptive neuro-fuzzy inference system (ANFIS), sliding mode control, uncertainty observer (NFSmUoC) and a magnetorheological damper (MRD) which can be seen as an actuator for applying active force. A nonlinear uncertainty observer (NUO), a sliding mode controller (SMC) together with an inverse model of the MRD are designed in order to calculate the current value by which the MRD creates the required active control force u( t). An ANFIS and measured MR-damper-dynamic-response data sets are used to identify the MRD as an inverse MRD model (ANFIS-I-MRD). Based on dynamic response of the suspension, firstly the active control force u( t) is calculated by NUO and SMC, in which the impact of the uncertainty load on the system is estimated by the NUO. The ANFIS-I-MRD is then used to estimate applied current for the MRD in order to create the calculated active control force to control vertical vibration status of the train cars. Simulation surveys are carried out to evaluate the effectiveness of the proposed method.

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.

Online Adaptive Neuro-Fuzzy Based Full Car Suspension Control Strategy

2014 ◽  
pp. 617-666 ◽  
Author(s):  
Laiq Khan ◽  
Shahid Qamar
Keyword(s):  
Degrees Of Freedom ◽  
Ride Comfort ◽  
Sliding Mode ◽  
Active Suspension ◽  
Suspension System ◽  
Car Model ◽  
Car Suspension ◽  
Neuro Fuzzy ◽  
Suspension Control ◽  
Soft Computing Technique

Suspension system of a vehicle is used to minimize the effect of different road disturbances for ride comfort and improvement of vehicle control. A passive suspension system responds only to the deflection of the strut. The main objective of this work is to design an efficient active suspension control for a full car model with 8-Degrees Of Freedom (DOF) using adaptive soft-computing technique. So, in this study, an Adaptive Neuro-Fuzzy based Sliding Mode Control (ANFSMC) strategy is used for full car active suspension control to improve the ride comfort and vehicle stability. The detailed mathematical model of ANFSMC has been developed and successfully applied to a full car model. The robustness of the presented ANFSMC has been proved on the basis of different performance indices. The analysis of MATLAB/SMULINK based simulation results reveals that the proposed ANFSMC has better ride comfort and vehicle handling as compared to Adaptive PID (APID), Adaptive Mamdani Fuzzy Logic (AMFL), passive, and semi-active suspension systems. The performance of the active suspension has been optimized in terms of displacement of seat, heave, pitch, and roll.

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