JPH11202949A - Vibration reducing method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To actively reduce engine vibration, etc., by implementing LMS(least mean of squares) algorithm by using an adaptive digital filter consisting of a recursive filter. SOLUTION: When the recursive filter is used as the adaptive filter 13, stability that a nonrecursive filter which is always stable has is not guaranteed, but the adaptive digital filter which has equivalent properties with relatively low order can be designed to a size about 1/100 as large as a conventional ROM size. Thus, the system scale can be decreased.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a vibration reduction method and a vibration reduction apparatus which execute an LMS algorithm using an adaptive digital filter comprising a recursive filter to actively reduce engine vibration and the like.

[0002]

2. Description of the Related Art When an unknown system can be approximated by a FIR (finite response) filter, that is, a non-recursive filter, its parameter, that is, an impulse response, is estimated to obtain the same output as that of the unknown system. It is. In other words, adaptive signal processing consists in estimating the parameters of an unknown system. For example, if an engine vibration system is analyzed as an unknown system, a vibration reduction device that generates a pseudo vibration and actively cancels the engine vibration is configured. can do.

A vibration reduction device 1 shown in FIG. 5 reduces vibration of an engine 3 transmitted to a seat 2 such as a driver's seat by vibrating an actuator 4 mounted on an engine mount, and is a center of vibration reduction. An adaptive algorithm operation unit 6 is incorporated in the controller 5. The vibration frequency of the engine 3 is captured by the controller 5 by the frequency sensor 7, and the acceleration applied to the seat 2 is captured by the controller 5 by the acceleration sensor 8. The controller 5 supplies a vibration output that cancels the vibration of the vibration system estimated by the adaptive algorithm calculator 6 to the actuator 4 via the power amplifier 9 to actively suppress engine vibration.

The conventional vibration reduction apparatus 1 uses, for example, a synchronous LMS (least mean square) algorithm, so-called SFX algorithm, as an adaptive algorithm. This SFX algorithm generates an impulse train synchronized with the fundamental period of a periodic signal or a pseudo-periodic signal in a processor, and uses the filtered impulse train as a virtual input to apply a filtered XLMS algorithm. Since the signal and the reference signal can be generated in a limited manner, convolution operation is unnecessary, the amount of operation is small, and control performance can be improved by increasing the sampling frequency. Was.
However, since the SFX algorithm processes a periodic signal such as engine vibration, the sampling frequency also increases in accordance with the upper limit of the frequency, and it is necessary to increase the order of the number of taps of the impulse response. There has been a problem that the sampling period becomes longer as the time increases, and the calculation accuracy also decreases.

Therefore, in order to remove a specific frequency component of a periodic signal, for example, as disclosed in Japanese Patent Application Laid-Open No. Hei 8-44377, "Adaptive Control Method of Periodic Signal", the square of a function including a sine wave output signal is used. A gradient vector is obtained by partially differentiating the evaluation function represented by a filter coefficient of an estimation system which is a function of the amplitude and phase of the output signal, and a value obtained by multiplying the gradient vector by a certain number is subtracted from the filter coefficient. By doing so, an adaptive algorithm has been proposed in which the filter coefficient is updated each time, and the amplitude and phase of the sine wave output signal are updated based on the updated amplitude and phase of the filter coefficient. This adaptive algorithm can be operated only from the basic period of the periodic signal, and does not require a convolution operation for generating a filter coefficient and a reference signal. Also,
Since the sampling period is constant, there are advantages such as that the calculation time is not sacrificed due to an increase in the frequency.

[0006]

In the conventional vibration reduction method, a synchronous adaptive algorithm is used as an adaptive algorithm, and the equivalent transfer characteristic of a control target system that generates a reference signal is enormous in both phase data and attenuation data. R
OM data is required, and there is a problem that the system scale is easily enlarged.

SUMMARY OF THE INVENTION The present invention has been made to solve the above problems, and has as its object to reduce the ROM data to about 1/100 by using an adaptive digital filter comprising a recursive filter, thereby suppressing the system scale. Things.

[0008]

In order to achieve the above object, the present invention provides a method for estimating a vibration system using an adaptive digital filter according to an LMS algorithm using a steepest descent method, and based on the estimated value, In the vibration reduction method for actively suppressing vibration, a recursive filter is used as an adaptive digital filter.

[0009]

BRIEF DESCRIPTION OF THE DRAWINGS FIG.
This will be described with reference to FIG. FIG. 1 is a schematic configuration diagram showing an embodiment of a controller to which the vibration reduction method of the present invention is applied, and FIG. 2 is a circuit configuration diagram showing a specific configuration example of the adaptive digital filter shown in FIG.

A controller 11 shown in FIG. 1 is a vibration reducing device 1 for actively controlling the engine vibration of an automobile.
It is applied to The vibration reduction target is a vibration system from the engine that is the vibration source to the sheet that is the vibration end, and this vibration system is the unknown system 12. Time t
The input x (t) of the unknown system 12 at == k, the noise n (t) included in the output, and the observed signal d (t) are represented by xk, nk, and dk, respectively. H (z) is the z-transform transfer function of the unknown system 12, and if its impulse response sequence is h0, h1,. It is represented by

The input x (t) to the unknown system 12 is taken into an adaptive digital filter 13 composed of a recursive filter (IIR) in the controller 11, and the filter coefficients ai and bj are variably set by an LMS algorithm calculator 14. You. As shown in FIG. 2, the adaptive digital filter 13 includes a non-recursive filter 13b including M filter coefficient units and unit delay elements, and a recursive filter 13 including N filter coefficient units and unit delay elements. Filter 13
a is cascade-connected, and has a z-transform transfer function represented by the following equation. Here, z-1 is a unit delay operator, ai is a filter coefficient of the recursive filter 13a, and bj is a filter coefficient of the non-recursive filter 13b. The denominator polynomial in the above equation functions as a non-recursive filter, and the adaptive digital filter 13 as a whole has a non-recursive configuration. Between input xk and output yk to adaptive digital filter 13 at time k, Relationship exists.

The difference between the output yk and the observed value dk is supplied to the LMS algorithm calculator 14 as an error ek. The LMS algorithm calculator 14 operates according to a least mean square (LMS) algorithm using the steepest descent method, uses the filter coefficients ai, bj before adaptation, that is, the old estimated values, and updates the estimated values, that is, the filter coefficients ai + 1, after the adaptation. An operation for calculating bj + 1 is performed. The simplest operation is the gradient method.

In the gradient method, since the evaluation function J is given by the squared value of the estimation error ε2, ai + 1 = ai + 2μ
εαi i = 1,2, ...
・ N bj + 1 = bj + 2νεβj
The recursive formula of the filter coefficient vector of j = 0, 1,... M is obtained. Here, μ and ν are step size parameters, and αi and βj are values obtained by partially differentiating the output yk with the filter coefficients ai and bj at time t = k. That is, αi = ∂yk / ∂ai βj = ∂yk / ∂bj. In practice, however, the calculation can be simplified by using approximate gradients for αi and βj.

As described above, the adaptive digital filter 13
Is composed of a non-recursive filter (FIR) which has a finite response. Therefore, although there is no guarantee of stability like a non-recursive filter which is always stable, a design of an adaptive digital filter having a relatively low order and equivalent properties is required. Possible, conventional ROM
Adaptive digital filter 13 at about 1/100 of the size
Can be designed, so that the system scale can be reduced.

In the above-described embodiment, L by the gradient method
The case where the MS algorithm was adopted was adopted, but besides this, for example, HARF (hypertable adaptive recur
A recursive adaptive digital filter can also be configured using a sivefilter algorithm. According to the HARF algorithm, convergence of a solution by iterative calculation and stability of a filter can be guaranteed based on the concept of super stability in control theory. HARF (simple hyper)
The SHARF algorithm, which is a simplified version of the stable adaptive recursive filter algorithm, can also be used.
In that case, the calculation can be further simplified.

[0016]

As described above, according to the present invention,
Since a recursive filter is used for the adaptive digital filter, there is no guarantee of stability like a non-recursive filter that is always stable, but it is possible to design an adaptive digital filter that has relatively low-order and equivalent characteristics. Since an adaptive digital filter can be designed at about 1/100 of the conventional ROM size, the system scale can be reduced. For example, when a recursive adaptive digital filter is configured by using the HARF algorithm, it is difficult to use a control theory. Based on the concept of hyper-stability, it is possible to guarantee the convergence of the solution by iterative calculation and the stability of the filter.
The use of the SHARF algorithm, which is a simplified version of the F-algorithm, provides excellent effects such as further simplifying the calculation.

[Brief description of the drawings]

FIG. 1 is a schematic configuration diagram showing an embodiment of a controller to which a vibration reduction method according to the present invention is applied.

FIG. 2 is a circuit configuration diagram showing an example of a specific configuration of the adaptive digital filter shown in FIG.

FIG. 3 is a schematic configuration diagram illustrating an example of a conventional vibration reduction device.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 1 Vibration reduction device 2 Seat 3 Actuator 5, 11 Controller 7 Frequency sensor 8 Acceleration sensor 9 Power amplifier 12 Unknown system 13 Adaptive digital filter 14 LMS algorithm calculator

Claims (1)

[Claims] 1. A vibration reduction method for estimating a vibration system using an adaptive digital filter according to an LMS algorithm using a steepest descent method and actively suppressing the vibration system based on the estimated value. A vibration reduction method, characterized in that a recursive filter is used in the method.