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A Computational Approach to Vibration Control of Vehicle Engine-Body Systems

Written By

Hamid Reza Karimi

Submitted: 21 December 2011 Published: 05 September 2012

DOI: 10.5772/50295

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1. Introduction

In recent years, the noise and vibration of cars have become increasingly important [20, 23, 29, 30, 35]. A major comfort aspect is the transmission of engine-induced vibrations through powertrain mounts into the chassis (see Figure 1). Engine and powertrain mounts are usually designed according to criteria that incorporate a trade-off between the isolation of the engine from the chassis and the restriction of engine movements. The engine mount is an efficient passive means to isolate the car chassis structure from the engine vibration. However, the passive means for isolation is efficient only in the high frequency range. However the vibration disturbance generated by the engine occurs mainly in the low frequency range [8, 19, 23, 30]. These vibrations are result of the fuel explosion in the cylinder and the rotation of the different parts of the engine (see Figure 2). In order to attenuate the low frequency disturbances of the engine vibration while keeping the space and price constant, active vibration means are necessary.

A variety of control techniques, such as Proportional-Integral-Derivative (PID) or Lead-Lag compensation, Linear Quadratic Gaussian (LQG), H2, H, μ-synthesis and feedforward control have been used in active vibration systems [1, 3, 4, 10, 11, 15, 24, 26, 31, 32, 34, 35]. The main characteristic of feedforward control is that information about the disturbance source is available and is usually realised with the Filtered-X Least-Mean-Squares (Fx-LMS) algorithms. However, the disturbance source is assumed to be unknown in feedback control, then different strategies of feedback control for vibration attenuation of unknown disturbance exist ranging from classical methods to a more advanced methods. Recently, the performance result obtained by H feedback controller with the result obtained by feedforward controller using Fx-LMS algorithms for vehicle engine-body vibration system was compared in [30, 35].

On the other hand, wavelet theory is a relatively new and an emerging area in mathematical research [2]. It has been applied in a wide range of engineering disciplines such as signal processing, pattern recognition and computational graphics. Recently, some of the attempts are made in solving surface integral equations, improving the finite difference time domain method, solving linear differential equations and nonlinear partial differential equations and modelling nonlinear semiconductor devices [5, 6, 7, 13, 16, 17, 18, 21, 27].

Figure 1.

Front axis of AUDI A 8 from [22, 30] (Werkbild Audi AG).

Figure 2.

Chassis excited by the engine vibration.

Orthogonal functions like Haar wavelets (HWs) [13, 16], Walsh functions [7], block pulse functions [27], Laguerre polynomials [14], Legendre polynomials [5], Chebyshev functions [12] and Fourier series [28], often used to represent an arbitrary time functions, have received considerable attention in dealing with various problems of dynamic systems. The main characteristic of this technique is that it reduces these problems to those of solving a system of algebraic equations for the solution of problems described by differential equations, such as analysis of linear time-invariant, time-varying systems, model reduction, optimal control and system identification. Thus, the solution, identification and optimisation procedure are either greatly reduced or much simplified accordingly. The available sets of orthogonal functions can be divided into three classes such as piecewise constant basis functions (PCBFs) like HWs, Walsh functions and block pulse functions; orthogonal polynomials like Laguerre, Legendre and Chebyshev as well as sine-cosine functions in Fourier series [21].

In the present paper, we, for the first time, introduce a computational solution to the finite-time robust optimal control problem of the vehicle engine-body vibration system based on HWs. To this aim, mathematical model of the engine-body vibration structure is presented such the actuators and sensors used to investigate the robust optimal control are selected to be collocated. Moreover, the properties of HWs, Haar wavelet integral operational matrix and Haar wavelet product operational matrix are given and are utilized to provide a systematic computational framework to find the approximated robust optimal trajectory and finite-time H control of the vehicle engine-body vibration system with respect to a H performance by solving only the linear algebraic equations instead of solving the differential equations. One of the main advantages is solving linear algebraic equations instead of solving nonlinear differential Riccati equation to optimize the control problem of the vehicle engine-body vibration system. We demonstrate the applicability of the technique by the simulation results.

The rest of this paper is organized as fallows. Section 2 introduces properties of the HWs. Mathematical model of the engine-body vibration structure is stated in Section 3. Algebraic solution of the engine-body system is given in Section 4 and Haar wavelet-based optimal trajectories and robust optimal control are presented in Sections 5 and 6, respectively. Simulation results of the robust optimal control of the vehicle engine-body vibration system are shown in Section 7 and finally the conclusion is discussed.

The notations used throughout the paper are fairly standard. The matricesIr, 0rand 0r×s are the identity matrix with dimension r×r and the zero matrices with dimensions r×r andr×s, respectively. The symbol and tr(A) denote Kronecker product and trace of the matrixA, respectively. Also, operator vec(X) denotes the vector obtained by putting matrix X into one column. Finally, given a signalx(t), x(t)2denotes the L2 norm ofx(t); i.e.,


2. Properties of Haar Wavelets

Properties of HWs, which will be used in the next sections, are introduced in this section.

2.1. Haar Wavelets (HWs)

The oldest and most basic of the wavelet systems is named Haar wavelet that is a group of square waves with magnitude of.±1. in the interval [0,1) [6]. In other words, the HWs are defined on the interval [0,1) as


and ψi(t)=ψ1(2jtk) for i1 and we write i=2j+k for j0and0k<2j. We can easily see that the ψ0(t) and ψ1(t) are compactly supported, they give a local description, at different scalesj, of the considered function.

2.2. Function approximation

The finite series representation of any square integrable function y(t) in terms of an orthogonal basis in the interval[0,1), namelyy^(t), is given by


where a:=[a0a1am1]T and Ψm(t):=[ψ0(t)ψ1(t)ψm1(t)]T for m=2j and the Haar coefficients ai are determined to minimize the mean integral square error ε=01(y(t)aTΨm(t))2dt and are given by


Remark 1. The approximation error, Ξy(m):=y(t)y^(t), is depending on the resolution m and is approaching zero by increasing parameter of the resolution.

The matrix Hmcan be defined as


where imti<i+1m and using (2), we get


The integration of the vector Ψm(t) can be approximated by


where the matrix


represents the integral operator matrix for PCBFs on the interval [0,1) at the resolutionm. For HWs, the square matrix Pm satisfies the following recursive formula [13]:


with P1=12 and Hm1=1mHmTdiag(r) where the matrix Hm defined in (4) and also the vector r is represented by


form>2. For example, at resolution scalej=3, the matrices H8 and P8 are represented as




for further information see [13, 25].

2.3. The product operational matrix

In the study of time-varying state-delayed systems, it is usually necessary to evaluate the product of two Haar function vectors [13]. Let us define


where Rm(t) satisfies the following recursive formula






Moreover, the following relation is important for solving optimal control problem of time-varying state-delayed system:


where a˜1=a0 and




Figure 3.

The sketch of engine-body vibration system


3. Mathematical model description

A schematic of the vehicle engine-body vibration structure is shown in Figure 3. The actuator and sensor used to this control framework are selected to be collocated, since this arrangement is ideal to ensure the stability of the closed loop system for a slightly damped structure [26]. In our study, only the bounce and pitch vibrations in the engine and body are considered [35]. The engine with mass Meand inertia moment Ie is mounted in the body by the engine mounts ke andce. The front mount is the active mount, the output force of which can be controlled by an electric signal. The active mount consists of a main chamber where an oscillating mass (inertia mass) is moving up and down. The inertia mass is driven by an electro-magnetic force generated by a magnetic coil which is controlled by the input current.

The vehicle body with mass Mb and inertia moment Ib is supported by front and rear tires, each of which is modeled as a system consisting of a spring kb and a damping devicecb. Therefore, a four degree-of-freedom vibration suspension model shown in Figure 3 can be described by the following equations


where the states x1(t),x2(t),x3(t) and x4(t) are the bounces and pitches of the engine and body, respectively, where displacement of the chassis (x2(t))is usually taken as an output. Input force, f(t), is used as the active force to compensate the vibration transmitted to vehicle body. Moreover, engine disturbance de(t) can be the excitation, generated by the motion up/down of the different parts inside the engine;

The system Eq. (14) can be represented in the following state-space form


where x(t)4 is the state; f(t)is the control input; de(t)is the disturbance input which belongs toL2[0,); and z(t)3 is the controlled output withC11×4, C21×4and C3 is a positive scalar. The state-space matrices are also defined as


Taking displacement of the chassis (x2(t)) as an output then a comparison of the displacement response respect to the input force f(t) and the external disturbance de(t) in the frequency range up to 1 KHz is depicted in Figure 4a) and 4b). Three relevant modes occur around the frequencies 1, 5 and 9 Hz, respectively, which represent the dynamics of the main degrees of freedom (DOFs) of the system.


4. Algebraic solution of system equations

In this section, we study the problem of solving the second-order differential equations of the engine-body system (14) in terms of the input control and exogenous disturbance using HWs and develop appropriate algebraic equations.

Based on HWs definition on the interval time[0,1], we need to rescale the finite time interval [0,Tf] into [0,1] by consideringt=Τfσ; normalizing the system Eq. (15) with the time scale would be as follows


Now by integrating the system above in an interval[0,σ], we obtain


By using the Haar wavelet expansion (2), we express the solution of Eq. (15), input force f(σ) and engine disturbance de(σ) in terms of HWs in the forms


whereX4×m,F1×m and De1×m denote the wavelet coefficients ofx(σ), f(σ)andde(σ), respectively. The initial conditions of x(0) and x˙(0) are also represented by x(0)=X0Ψm(σ) andx˙(0)=X¯0Ψm(σ), where the matrices {X0,X¯0}4×m are defined, respectively, as


Therefore, using the wavelet expansions (18)-(20), the relation (17) becomes


For calculating the matrixX, we apply the operator vec(.) to Eq. (23) and according to the property of the Kronecker product, i.e.vec(ABC)=(CTA)vec(B), we have:


Solving Eq. (24) for vec(X) leads to


where the matrices {Δ1,Δ2}4m×m and{Δ3,Δ4}4m×4m are defined as


Consequently, using (25) and (26) and the properties of the Kronecker product, the solution of system (15) is


and it is also clear that to find the approximated solution of the system, we have to calculate the inverse of the matrix Τf(PmTC)+Τf2(Pm2TK)+ImM with dimension 4m×4monly once.

Figure 4.

Displacement of the chassis respect to f(t) (a) and de(t) (b).


5. Optimal control design

The control objective is to find the optimal control f(t) with respect to a quadratic cost functional approximately such acts as the active force to compensate the vibration transmitted to vehicle body. The quadratic cost functional weights the states and their derivatives with respect to time in the cost function as follows:


whereS1:4×4, S2:4×4, Q1:4×4and Q2:4×4 are positive-definite matrices and R is a positive scalar. We can rewrite the cost function (28) as follows:


where S˜=diag(S1,S2) and Q˜=diag(Q1,Q2)with the time scalet=Τfσ.

From (15) and the relationx˙(σ)=X¯Ψm(σ), where X¯:4×m denotes the wavelet coefficients of x˙(σ) after its expansion in terms of HFs, we read


where Xaug=[XΤf1X¯] and


Remark 2. By substituting x˙(σ)=X¯Ψm(σ) intox(σ)x(0)=0σx˙(t)dt, we have:


and using (4), we readXX0=X¯Pm. Then, by applying the operator of vec(.) and according to the properties of Kronecker product in Appendix A1, we obtain


By substituting the definition (31) in (33) and using the properties of the operator tr(.) in Appendix A1, the cost function (28) is given by


where the matrices Πm1:8m×8m and Πm2:m×m are defined as

Πm1=MfTS˜+Τf(MTQ˜)andΠm2=RΤfMm, respectively,

and the matrices Mm:m×m and Mmf:m×m are defined as

Mm:=01Ψm(σ)ΨmT(σ)dσandMmf:=Ψm(1)ΨmT(1), respectively.

It is clear that the cost function of J(.) is a function ofimσi<i+1m, then for finding the optimal control law, which minimizes the cost functionalJ(.), the following necessary condition should be satisfied


By consideringvec(Xaug), which is a function ofvec(F), and using the properties of derivatives of inner product of Kronecker product in Appendix A2, we find


Then the wavelet coefficients of the optimal control law will be in vector form as


Consequently, the optimal vectors of vec(X) and vec(F) are found, respectively, in the following forms




Finally, the Haar function-based optimal trajectories and optimal control are obtained approximately from Eq. (27) andf(t)=ΨmT(t)vec(F).


6. Robust optimal control design

In this section, an optimal state feedback controller is to be determined computationally such that the following requirements are satisfied:

  1. the closed-loop system is asymptotically stable;

  2. under zero initial condition, the closed-loop system satisfies z(t)2<γde(t)2for any non-zero de(t)[0,) where γ>0 is a prescribed scalar.

The control objective is to find the approximated robust optimal control f(t) with H performance such f(t) acts as the active force to compensate the vibration transmitted to vehicle body, i.e. guarantees desired L2 gain performance. Next, we shall establish the H performance of the system (15) under zero initial condition. To this end, we introduce


It is well known that a sufficient condition for achieving robust disturbance attenuation is that the inequality J<0 for every de(t)L2[0,) [33, 36]. Therefore, we will establish conditions under which


From (15), the Eq. (40) can be represented as


wheret=Τfσ, S˜=diag(S1,S2)andC˜=diag(C1TC1,C2TC2).

Using the relationx˙(σ)=X¯Ψm(σ), we read


where Xaug=[XΤf1X¯] and


Moreover, according to Remark 2 in [18], the following relation is already satisfied between vec(X) and vec(X¯)


By using the definition (44) in Eq. (45), we have


Using the property of the Kronecker product,, (AC)(DB)=ADCBandvec(ABC)=(CTA)vec(B), we can write (42) as


where the matricesΠm18m×8m, Πm2m×mare defined as Πm1=MmfS˜+Τf2(MmC˜) andΠm2=Τf2Mm, respectively.

It is easy to show that the worst-case disturbance in Eq. (47) occurs when


By substituting Eq. (48) into Eq. (47) we obtain


Minimizing the right-hand side of Eq. (49) results in the algebraic relation between wavelet coefficients of the robust optimal control and of the optimal state trajectories in the following closed form


As a result we have


Consequently, if there exists positive scalar γ to the matrix inequality


then inequality (41) is concluded.

From the relations above we obtain the robust optimal vectors of vec(X) and vec(F) after some matrix calculations, respectively, in the following forms




Finally, the Haar wavelet-based robust optimal trajectories and robust optimal control are obtained approximately from Eq. (27) andf(t)=ΨmT(t)vec(F), respectively.


7. Numerical results

In this section the proposed computational methodology is applied to the vehicle engine-body vibration system (15) such the exogenous disturbance de(t) is assumed to be a Sin(.)function at the frequency of10Hz. The system parameters, used for the design and simulation are given in Tables 1 and 2 in the Appendix B. Table 3 in the Appendix gives the pole-zero locations of 8th –order model of the vehicle engine-body vibration system. It is clear that the vehicle engine-body vibration system is unstable and has the nonminimum phase property. The objective is to find the approximated robust optimal displacement of the chassis and robust optimal input force with H performance using HWs at the finite time interval[0,1]. Moreover, the matrices {S1,S1}4×4 and the vectorsC1, C2and the scalar C3 in the controlled output z(t) in Eq. (15) are chosen asS1=S2=04,C1=[0,1,1,2] , C2=[3,1,0,1]andC3=1.

Figure 5.

Comparison of displacement of the chassis found by HWs at resolution level j=5 (solid) and by analytical solution (dashed).

To compare the approximate solutionsx2(t)andf(t), found by HWs, to the analytical solution found by Theorem 1 in the Appendix C, we choose the performance bound and the resolution level equal to3.15 and5, respectively, i.e. γ=3.15andj=5. The time curves found are plotted in Figures 5 and 6. It is clear that the effect of the engine disturbance is attenuated onto the displacement of the chassis as the output as well. In other words, f(t)compensates the vibration transmitted to the chassis. Compare the Haar wavelet based solutions to the continuous solutions using the differential Riccati equation, the approximate solutions (53) and (54) deliver both, robust control f(t) and state trajectory x(t) in one step by solving linear algebraic equations instead of solving nonlinear differential Riccati equation, while accuracy can easily be improved by increasing the resolution levelj.

Figure 6.

Comparison of input force found by HWs at resolution level j=5 (solid) and by analytical solution (dashed).


8. Conclusion

This paper presented the modelling of engine-body vibration structure to control of bounce and pitch vibrations using HWs. To this aim, the Haar wavelet-based optimal control for vibration reduction of the engine-body system was developed computationally. The Haar wavelet properties were introduced and utilized to find the approximate solutions of optimal trajectories and robust optimal control by solving only algebraic equations instead of solving the Riccati differential equation. Numerical results were presented to illustrate the advantage of the approach.


Appendix A

A1. Some properties of Kronecker product

LetA:p×q, B:q×r,C:r×s and D:q×tbe fixed matrices, then we have:


A2. Derivatives of inner products of Kronecker product

Let A:n×n be fixed constants and x:n×1 be a vector of variables. Then, the following results can be established:


A3. Chain rule for matrix derivatives using Kronecker product

Let Ζ be a p×q matrix whose entries are a matrix function of the elements ofY:s×t, where Y is a function of matrixΧ:m×n. That is, Ζ=Η1(Y), whereY=Η2(X). The matrix of derivatives of Ζ with respect to Χ is given by


Appendix B

Mb1000 [kg]
Ib810 [kg m2]
kb20000 [N/m]
cb300 [N/m/s]
Lb2.5 [m]

Table 1.

The vehicle body parameter.

Me250 [kg]
Ie8.10 [kg m2]
ke200000 [N/m]
ce200 [N/m/s]
Le0.5 [m]

Table 2.

The engine parameters.


Table 3.

Pole-zero locations of the 8th -order model.

Appendix C

Theorem 1 (State Feedback) [9]. Consider dynamical system


under assumption (A,B1,C)is stabilizable. For a givenγ>0, the differential Riccati equation


has a positive semi-definite solution X(t) such that A(B1B1Tγ2B2B2T)X(t) is asymptotically stable. Then the control law u(t)=B1TX(t)x(t):=K(t)X(t) is stabilizing and satisfiesz(t)2<γw(t)2.


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Written By

Hamid Reza Karimi

Submitted: 21 December 2011 Published: 05 September 2012