Abstract— In this paper we present one of the concepts of vehicle seat vibration isolation, using five fuzzy controllers together with neural network, combining it into a hybrid system. We used fuzzy controllers as a part of a semi-active vehicle suspension system, which consists of semi-active air spring and passive damper. System is implemented between the cabin floor and vehicle seat. Fuzzy controllers are designed to adjust the stiffness of the air spring, in order to isolate the vertical vibrations that are being caused by rough road surfaces. Each controller is tuned for different road type. Which controller is active depends on the output of the neural network, representing one of the four roads. Input data for fuzzy controller are vehicle seat deflection and velocity of the vehicle seat deflection. Measure of the quality of suspension is the SEAT value, which is calculated as a quotient of effective seat vertical acceleration and effective cabin vertical acceleration (ISO 2631-1 standard). Small SEAT-value means good isolation of the seat, regarding the vibrations. This paper presents the results obtained from both the MATLAB simulations and the experiment on the real model.

I. INTRODUCTION

ENTRAL axels of heavy duty trucks and utility vehicles like dampers, tractors, field machinery, etc, are very

little or not at all isolated from vibrations. With the vehicle movement, vertical vibrations are being generated by rough road surfaces. Such vibrations have negative influence on driver, causing ride discomfort and fatigue [1], [2], so they have to be minimized, using a vehicle seat suspension system with damper and air spring. There are three basic concepts used in suspension systems: passive, semi-active and active. The passive suspension has significant limitations in structural applications where broadband disturbances of highly uncertain nature are encountered [1]. To compensate for these limitations, semi-active and active suspension systems are utilized. With semi-active systems better suspension results were obtained [3], [4], [5]. In active suspension concepts, with an additional active force introduced as a part of the suspension subsection the suspension is controlled using different algorithms to make it

Manuscript received April 15, 2009. Omer Tanovic is with the Faculty of Electrical Engineering, Department

of Automatic Control and Electronics, University of Sarajevo, 71000 Sarajevo, Bosnia-Herzegovina (phone: 00387-61-222-976; fax: 00387-33-250-725; e-mail: omer.tanovic@etf.unsa.ba).

Senad Huseinbegovic is with the Faculty of Electrical Engineering, Department of Automatic Control and Electronics, University of Sarajevo, 71000 Sarajevo, Bosnia-Herzegovina (phone: 00387-61-270-417; e-mail: senad.huseinbegovic@etf.unsa.ba).

more responsive to source of disturbances [6], [7], [8]. Because of the complexity and nonlinearity of a vehicle seat model fuzzy control algorithm [9], [10], or neural networks approach [11] could be implemented. In order to realize the full potential of the suspension the controller could have the capability of adapting to changing road environments [12], [13].

In this paper, the performance of a hybrid fuzzy-neural network suspension system is tested. The concept used for vibration isolation is semi-active/passive concept, air spring is semi-active and oil damper is passive. Because of the advantage of fuzzy logic in nonlinear systems the fuzzy control algorithm was implemented. Neural network was used as a part of the system that decides the type of the road the vehicle is driving on, as in [14]. As a measure of the quality of suspension, SEAT value was calculated [2].

Control parameters and all simulations were obtained in MATLAB/Simulink. Results obtained from both the MATLAB simulations and the experiment on the real model are presented.

II. SYSTEM DESCRIPTION

Model we are simulating is a vehicle seat model given in Fig. 1.

A. Vehicle seat model description

The vehicle seat model consists of two parts: seat and under-seat construction. A dummy (weighting 75kg) placed

Hybrid Fuzzy-Neural Network Structure for Vehicle Seat Vibration Isolation

Omer Tanovic, Member, IEEE and Senad Huseinbegovic, Member, IEEE

C

Fig. 1. Vehicle seat model

2009 IEEE International Conference on Control and AutomationChristchurch, New Zealand, December 9-11, 2009

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978-1-4244-4707-7/09/$25.00 ©2009 IEEE 2354

on the seat, simulates a driver. As a part of the under-seat construction semi-active air spring and passive damper were implemented. Also in Fig.1. all available sensors and most important parameters are shown. Hydraulic cylinder generates oscillations on cabin floor, and these oscillations must be isolated in the base frame area. Another important parameter is displacement between seat plate and cabin floor. System given in Fig. 1 can be represented as, [15]:

( ) 0)( =−+−+ KSKSStot zzkzzczm (1)

where mtot is the mass of the seat together with the dummy,

Sz is the seat acceleration, c is the damping coefficient of

the oil damper, Sz is the seat velocity, Kz is the cabin

velocity, k is the coefficient of stiffness of the air spring, Sz

is the seat position and Kz is the cabin position.

Difference ( )KS zz − represents displacement between

cabin floor and seat plate ( )Azδ , and difference ( )KS zz −

represents velocity of that displacement ( )Az . In stationary

position, displacement is 13cm, and maximal deflection must be kept within the range [-5cm,5cm], regardless of other parameters. This limit is a result of the structure of the system, and represents a mechanical limit. In a case of a limit overrun system damage could happen.

A. Control system description

Control system is shown in Fig.2. It consists of two subsystems: neural network and five controllers. Neural network subsystem is supposed to decide the road type and forward the corresponding signal to fuzzy control subsystem. Switch block activates output of one of the five fuzzy controllers based on the neural network subsystem output signal (integer from 1 to 5). Each one of five controllers (except for the Fuzzy_controller_universal) is tuned to give

the best performance for a different road type. Four road types were considered. Fifth controller is universal controller tuned for arbitrary signal, in the case when neural network system cannot identify exact road type.

B. Measure of the quality

Measure of the quality of suspension is the SEAT value. It is calculated as a quotient of effective seat vertical acceleration Seffz and effective cabin vertical acceleration

Keffz (ISO 2631-1 standard), [6].

Keff

Seff

z

zSEAT = (2)

Small SEAT value represents good vibration isolation.

Based on the VDI recommendations 2057 the measured acceleration signals are being filtered. The recommendations present the weighting factors determined on the basis of the sensitivity of humans to different vibration frequencies. The SEAT value is then calculated, based on the ISO standard 2631-1, as the ratio between the filtered effective seat acceleration and the filtered effective cabin acceleration.

III. ROAD RECOGNITION

System used for the road recognition is supposed to decide the road type the vehicle is driving on, and to provide the corresponding signal as an output. As we can see from Fig.1. five different sensors are available and measures from those sensors could be used as neural network inputs. Input signals that we have used are four signals provided by Lehrstuhl für Regelungstechnik, Friedrich-Alexander Universität, Erlangen – Nürnberg. Those signals represent agricultural wheeled tractors and field machinery - measurement of whole-body vibration of the operator (em1, em3, em4 and em5 signals), for four different road types. In the real system we cannot provide the measurement of the position of the cabin floor (i.e. measurement of whole-body vibration of the operator). Instead of that we can measure the acceleration of the vertical movement of the cabin floor. That means we will have to differentiate twice the four signals provided to get the acceleration, and use that as the input for our control system. Neural network was trained as in [14].

IV. FUZZY CONTROL

System variables that were used for the calculation of a control are: deflection ( Azδ ), and deflection velocity ( Az ).

Reasons why those variables are the right ones for the synthesis of the controller are:

− Force in the oil damper is proportional to the square of the deflection velocity ( Az );

− Force of the air spring is a function of a deflection ( Azδ ).

Fuzzy controller was designed using FIS editor of a MATLAB. It is a zero order Sugeno-type fuzzy controller,

Fig. 2. Control system

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with two inputs and one output [18], [19]. Inputs are cabin floor deflection ( Azδ ), and deflection velocity ( )Az . Fuzzy

input sets for Fuzzy_controller_em3 inputs and corresponding membership functions are presented in Fig. 3.

TABLE I RULE BASE

Azδ Az SVO

High neg ON

Low neg OFF

High pos OFF

Low pos ON

OK neg ON

OK pos ON

- vneg OFF

- vpos OFF

Output of the fuzzy controller is the openness of the air spring valve (valve_op or SVO ). Membership functions are

constants because of the zero order Sugeno-type fuzzy

controller. For the output SVO , two membership functions

are taken, with values 1 and 0 (for ON and OFF, respectively).

The rule base used in the semi-active suspension system can be represented by the following Table I with fuzzy terms derived by modeling the designer’s knowledge and experience. The abbreviations used in Table I correspond to:

• Low … Low value • OK … Normal value • High … High value • vneg … Very negative • neg … Negative • pos … Positive • vpos … Very positive

The linguistic control rules of the fuzzy logic controller, obtained from the table above, used in such case are as follows:

R1: IF ( Azδ is High) and ( Az is neg) THEN ( SVO is ON)

V. MATLAB SIMULATION RESULTS

For a simulation we used already available Simulink® model of a vehicle seat [15], [16], [17]. Signals presented are: acceleration of a cabin floor ( Kz or ddzK) and deflection

( Azδ ). Time of simulation was 25s. System was excited with

already mentioned em1, em3, em4 and em5 signals.

(a)

(b)

Fig. 4. em1 signal: (a) input signal ddzK (acceleration of cabin floor), (b) deflection Azδ

SEAT value calculated for the simulation time of 25 seconds, for the em1 signal is SEAT=0.5482. That means cca 54% of vertical vibrations were transmitted on driver. Maximal deflection is around 3.6cm so the condition of deflection limitation has been achieved.

SEAT value for the em3 signal is SEAT=0.4486. That means cca 45% of vertical vibrations were transmitted on driver. The condition of the deflection limitation is satisfied again (although at one moment maximal deflection is equal to -4.9cm).

-0.05 -0.04 -0.03 -0.02 -0.01 0 0.01 0.02 0.03 0.04 0.050

0.2

0.4

0.6

0.8

1

Input 1 - Deflection

Low

OK

High

(a)

-0.6 -0.4 -0.2 0 0.2 0.4 0.60

0.2

0.4

0.6

0.8

1

Input 1 - Velocity

vneg

negpos

vpos

(b)

Fig. 3. (a) Fuzzy input set for Azδ , (b) Fuzzy input set for Az

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(a)

(b)

Fig. 5. em3 signal: (a) input signal ddzK (acceleration of cabin floor), (b) deflection Azδ

(a)

(b)

Fig. 6. em4 signal: (a) input signal ddzK (acceleration of cabin floor), (b) deflection Azδ

SEAT value for the em4 signal is SEAT=0.425. That means cca 42% of vertical vibrations were transmitted on driver. The condition of the deflection limitation is satisfied, and maximal deflection does not exceed 1.5cm. Such results are caused by better road quality.

(a)

(b)

Fig. 7. em5 signal: (a) input signal ddzK (acceleration of cabin floor), (b) deflection Azδ

SEAT value for the em5 signal is SEAT=0.3051. That means cca 30% of the vertical vibrations were transmitted on driver. Maximum seat deflection is 3.8cm, so the deflection is within recommended range.

VI. REAL MODEL SIMULATION RESULTS

Vibration isolation system was tested on the real vehicle seat model. The experimental line with vehicle seat, hydraulic cylinder, damper, air spring and the rest of necessary equipment has been set up in the laboratory at the Lehrstuhl für Regelungstechnik (Control techniques

Department) of Friedrich-Alexander University Erlangen-Nürnberg, as shown in Fig 8. MATLAB Simulink and D-Space Control Desk application have been used in order to accomplish a “Hardware-in-the-loop Simulation”.

Fig. 8. Experimental line for “Hardware-in-the-loop” Simulation

Input signal for cabin floor oscillations are the same ones

that have been used for MATLAB simulations, em1, em3, em4 and em5 signals. Acceleration sensors have been set up to measure cabin floor and seat acceleration, so it is easy to calculate SEAT value. Also, a sensor has been implemented between the seat and cabin floor to measure the relative seat deflection. Time signals of acceleration of cabin floor ddzK

and deflection Azδ are presented in Figs. 9 – 12.

(a)

(b)

Fig. 9. em1 signal: (a) input signal ddzK (acceleration of cabin floor), (b) deflection Azδ

SEAT value for the em1 signal is SEAT=0.49. Maximal

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deflection is around 3.5cm so the condition of deflection limitation has been achieved.

(a)

(b)

Fig. 10. em3 signal: (a) input signal ddzK (acceleration of cabin floor), (b) deflection Azδ

SEAT value for the em3 signal is SEAT=0.384. The condition of the deflection limitation is satisfied again (although maximal deflection is close to allowed upper limit of 5cm).

(a)

(b)

Fig. 11. em4 signal: (a) input signal ddzK (acceleration of cabin floor), (b) deflection Azδ

SEAT value for the em4 signal is SEAT=0.393. The seat deflection is within the expected range, and maximal deflection does not exceed 2cm.

(a)

(b)

Fig. 12. em5 signal: (a) input signal ddzK (acceleration of cabin floor), (b) deflection Azδ

SEAT value for the em5 signal is SEAT=0.30. The seat

deflection is within the expected range, but it’s maximal values are near the boundary values.

As we can see from the results the SEAT value is slightly better than in the MATLAB simulations, but still comparable. The reason for that could be taken from the fact that in the MATLAB simulations we had to differentiate the input signal (vertical movement of the cabin floor) to get the acceleration of the cabin floor, and in the real model we have a sensor that gives signal of acceleration of the cabin floor.

Also SIMULINK model of the vehicle seat used in the simulations is an approximation model, so there have to be some difference between that model and the real system.

VII. CONCLUSION

In this paper, the adequacy of using fuzzy systems in semi-active vibration isolation system was verified. Sugeno-type controllers need minimum processing time and power, and are ideal for real-time applications. Fuzzy controllers can be easily modified for different vehicles or different road surfaces, without changing the main control concept.

From the simulation results we can see that it is possible to isolate more than 60 percent of vibrations for all input signals except for the em1 signal, when we have got SEAT value of 0.49. But it has to be emphasized that seat deflection has been within the allowed limits, for every road type (or for all input signals). Comparing results with those obtained with the system with one passive and one semi-active element [3], for the em3 input signal, we can see a substantial improvement. In the system with semi-active air spring and passive damper [3], the SEAT value was 45 percent, therefore our system managed to isolate additional 7 percent of vibrations. Also comparing results with those from [5], we have obtained the same SEAT value as in the system with semi-active air spring and semi-active damper, although our system has passive damper.

We have not considered problems affecting the quality of the results, such as: absence of deflection velocity sensor, and friction force in air spring. The main problem of semi-active concept of vibration isolations is that the spring force can be generated only in the direction of the velocity of spring bellows. This problem can be solved by using the external power source and active concept of vibration isolation. So future work should include extension to systems with active or semi-active damper and/or active air spring, to eliminate the influence of mentioned problems. Also in this paper we limited our work to isolation of vertical vibrations, completely excluding the influence of lateral vibrations which, although minor, have some effect on a driver.

ACKNOWLEDGMENT

Authors would like to thank Dr. habil. Christoph Wurmthaler and Dipl.-Ing. Kühnlein Alexander from the Lehrstuhl für Regelungstechnik (Control techniques Department), Friedrich-Alexander University, Erlangen –

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Nürnberg, for their help and courtesy during authors’ research work in Erlangen. Authors also thank Dr. Zikrija Avdagi from the Faculty of Electrical Engineering, University of Sarajevo, Sarajevo, for his help and support in completing this research work.

REFERENCES

[1] S. J. Klooster “Vibration Suppression and Safety Seat Motion Design of a Hyper-Active Seat”, Master thesis, School of Mechanical Engineering Georgia Institute of Technology, March 2004

[2] International Standards Organization, Mechanical vibration and shock—Evaluation of human exposure to whole-body vibration—Part 1: General requirements. ISO 2631-1: 1997 standard.

[3] V. Marinkovic, “Improvement of Vibration Isolation for Vehicle Seats by Adjusting the Stiffness of the Air Spring, using Fuzzy Controllers”, XX International Symposium ICAT, Sarajevo, Oct. 2005

[4] J. Wagner, and X. Liu, „Nonlinear Modeling and Control of Automotive Vibration Isolation Systems”, Proc. of the American Control Conference, Chicago, Illionis, June 2000, pp. 564-568.

[5] S. Huseinbegovic, O. Tanovic, “Adjusting Stiffness of Air Spring and Damping of Oil Damper Using Fuzzy Controller for Vehicle Seat Vibration Isolation”, International Siberian Conference on Control and Communications SIBCON 2009, Tomsk, Russia, March 2009.

[6] K. Nevala, and M. Jarviluoma, „An Active Vibration Damping System of a Driver’s Seat for Off-Road Vehicles”, in Proc. of 4th Annual Conference of Mechatronics and Machine Vision in Practice, Toowoomba, Australia, 1997, pp. 38-43.

[7] Y. Sam, J. Osman, and M. Ghani, „Active suspension control: Performance comparison using proportional integral sliding mode and linear quadratic regulator methods“, Proc. of IEEE Conference on Control Applications, CCA 2003, Istanbul, Turkey, 2003, pp 274-278.

[8] J. Sun, and Q. Yang, “Decreasing Vibration of Vehicle Using Combined Suspension System”, IEEE Conference on Robotics, Automation and Mechatronics, Chengdu, China, Sept. 2008,

[9] J. Sun, Q. Yang, Y. Dong, and Y. Zhang, „LMS Adaptive Fuzzy Control for Vehicle Active Suspension System”, Proc. of IEEE Intelligent Transportation Systems Conference, Shanghai, China, 2003, 1372-1377.

[10] J. Sun, and Q. Yang, “Design of Adaptive Fuzzy Controller for Active Suspension System”, IEEE International Conference on Industrial Technology, ICIT '04, Tunisia, Dec. 2004, pp. 1096-1099.

[11] Z. Avdagic, S. Cernica, and S. Konjicija, “Reducing Vibration of the Seat with Semi-active Damper by Using the Artificial Neural Networks”, in Proc. of International Joint Conference on Neural Networks, Orlando, Florida, USA, August 2007.

[12] I. Fialho, and G. J. Balas, „Adaptive Vehicle Suspension Design Using LPV Methods”, in Proc. of the 37th IEEE Conference on Decision & Control, Tampa, Florida, USA, December 1998, pp. 469-474.

[13] I. Fialho, and G. J. Balas, „Road Adaptive Active Suspension Design Using Linear Parameter-Varying Gain-Scheduling”, IEEE Trans. On Control Systems Technology, Vol. 10, No. 1, January 2002, pp. 43-54.

[14] O. Tanovic, S. Huseinbegovic, and B. Lacevic, “Road Type Recognition Using Neural Networks for Vehicle Seat Vibration Damping”, in Proc. ISSPIT 2008 Conf., Sarajevo, Dec. 2008.

[15] N. Pascual, “Nonlinear Modelling of a Vehicle Seat”, Student Thesis, Friedrich-Alexander University Erlangen – Nürnberg, Department for Control (Lehrstuhl für Regelungstechnik), March 2002.

[16] D. I. O. Nwokah and Y. Hurmuzlu, “Mechanical Systems Design Handbook, Modeling, Measurement and Control”, CRC Press, 2002.

[17] F. Antritter, A. Kühnlein, K. Schmidt, “Close to Reality Simulation of Vehicle Seat with Semi-active Damper” (“Realisierungsnahe Simulation eines Fahrzeugsitzes mit semi-aktiver Dämpfung“), VDI report No. 1828, AUTOREG 2004, VDI Verlag GmbH, Düsseldorf 2004, pp. 185-194

[18] H. T. Nguyen, N. R. Prasad, C. L. Walker, and E. A. Walker, “A First Course in Fuzzy and Neural Control”, CRC Press, New York, 2004.

[19] J. Jantzen, “Foundations of fuzzy control”, John Wiley & Sons, New York, 2007.

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