An ANFIS controller also designed and a comparison
between proposed controller, ANFIS controller and open loop model
had made with different types of disturbance.
Suspension system is considered one of the most important components of modern
automobiles as it is the responsible for the vehicle’s stability, balance and safety. The
presence of robust controller is very necessary in order to ensure full interac
tion between
suspension components and making accurate decisions at the right time. This paper
proposes to design an Extended Adaptive Neuro Fuzzy Inference System (EANFIS)
controller for suspension system in quarter car model. The proposed controller is used as
decision maker In order to contribute in absorbing shocks caused by bumpy roads, and to
prevent vibrations from reaching the cockpit. Furthermore, it provides stability and
coherence required to reduce the discomfort felt by passengers, which arises from road
roughness, which in turn, improve the road handling. The MATLAB Simulink is used to
simulate the proposed controller with the controlled model and to display the responses of
the controlled model under different types of disturbance. In addition, a comparison
between EANFIS controller, Fuzzy controller and open loop model (passive suspension)
was done with different types of disturbance on order to evaluate the performance of the
proposed model. Controller has shown excelled performance in terms of reducing
displacements, velocity and acceleration.
In this work ,we try to predict the disadvantages will be caused by active suspension
system on indirect tire pressure monitoring systems (TPMS) which rely on analysis either
rotational speed signals of wheels or vehicle body vibration signals.
Ac
tive suspension systems that aim to increase passengers comfort apply some
additional vertical loads on vehicle's wheels which may cause difference in its rotational
speed due to the resulting load difference. also it may change the vibration level of the
vehicle during driving so the usage of vibration signals for monitoring tire pressure could
be difficult or impossible . in both cases the performance of TPMS may be changed.
This study showed that TPMS which rely on rotational speed signals will still able
to work and the effects of active suspension system will confine on the quality of the
resulting warnings while the TPMS which depend on vibration analysis will conk if the
analyzed vibration data was the vertical acceleration signals of wheels .
In this paper, a problem of ride comfort enhancement in a moving
vehicle was introduced and controlled by damping force to deduce
vibration caused by road profile. Sliding mode control was used to
give the damping force in two degree of freedom su
spension
system. A mechanic model of suspension system was given,
dampers and springs were used for passive damping to reduce
chattering and sliding mode control for semi-active control with
proposed method by using supervised fuzzy logic control for
chattering decreasing was designed.
A simulation with the given initial conditions was designed using
Matlab/Simulink. By computing of root mean square error we got
that the proposed method gave the best responses with the smallest
chattering compared with traditional mechanical damping and
sliding mode control. All results plotted using Matlab/Simulink.
One ofa car's suspension system functions is to isolate vibrations resulting from road on the driver and ensure a comfortable ride. But the design of control systems for semi-active suspension systems is difficult because of the non-linearity of the
constituent elements of these systems which make the researches related to it characterized by complexity. So in order to improve the performance of semi-active suspension systems without bearing the effort of designing a model based controller, a control system is designed using self-organizing fuzzy controller based on the principle of delay-in-penalty to control a semi-active suspension system which uses a magneto rheological damper. The controller tries to enhance system performance using the desired response as it is described in the penalty table. The fuzzy logic controller is based on two inputs namely sprung mass velocity and unsprung mass velocity. Using a quarter car model with 2 degree-of-freedom the system is modeled and simulated in MATLAB &Simulink® and the results are compared to the widely used sky-hook strategy. the simulation showed the ability of the self-organizing fuzzy controller to provide good results in minimizing sprung mass acceleration in variousroad profiles compared to sky-hookstrategy.
