A method for active control of adaptive line spectrum vibration in helicopters

Abstract

This invention discloses an adaptive line spectrum vibration active control method for helicopters. The method involves conducting a system identification experiment on the helicopter's secondary channel to obtain a secondary channel model; employing a frequency estimation algorithm to estimate the frequency of signals generated by multiple helicopter vibration sources to obtain the vibration frequencies of the vibration sources; using the vibration frequency of the vibration sources as a reference signal frequency and the vibration acceleration signal of the control point as a feedback signal to perform adaptive line spectrum vibration active control on the vibration generated at the helicopter vibration sources. This adaptive line spectrum vibration control method actively controls helicopter vibration to achieve vibration reduction. The method described in this invention can effectively reduce the main frequency components in the vibration signal during helicopter flight, ensuring the normal operation of helicopter structural components and onboard equipment, and preventing flight accidents.

Description

Technical Field

[0001] This invention belongs to the field of helicopter vibration control, and in particular, it is an adaptive line spectrum vibration active control method. Background Technology

[0002] The vibration level of a helicopter fuselage has always been a crucial indicator for evaluating helicopter performance. During flight, rotating components such as the rotor, tail rotor, engine, and transmission generate alternating loads. Furthermore, taxiing, landing, and weapon launches also induce excitation forces. These alternating loads and excitation forces produce significant airframe vibrations, leading to numerous hazards: pilot discomfort and fatigue, resulting in operational errors and compromised flight safety; passenger discomfort and reduced mental well-being; structural fatigue and damage, decreasing overall reliability and safety; instrument malfunction, impacting system reliability; and reduced weapon accuracy, affecting combat effectiveness, among others. Therefore, reducing helicopter airframe vibration levels has been a critical challenge in helicopter development and operation.

[0003] Active control technology for helicopters is one of the effective ways to reduce helicopter vibration levels. Among various active control technologies, Active Control of Structural Response (ACSR) is one of the few that has achieved successful engineering applications. Its basic principle is as follows: ACSR places sensors at key vibration reduction locations on the fuselage to collect the excitation response signals generated by the vibration source and inputs them to a controller. The controller then inputs control signals to actuators placed on the fuselage structure. The actuators drive the fuselage structure to generate the desired actuation response at the key vibration reduction locations. The excitation response and actuation response at the key locations are superimposed and cancel each other out to obtain the control response error. Minimizing the control response error achieves active vibration control of the helicopter. ACSR directly targets the structural vibration response and has advantages such as low power consumption and adaptability to changes in rotor speed and flight conditions. It can significantly reduce the vibration level of helicopters and is considered the most promising active vibration control technology for helicopters currently available.

[0004] For the control of line spectrum vibration, the key lies in the accurate acquisition of the line spectrum frequency. Even a 1% frequency error can prevent the system from eliminating the target vibration, significantly reducing the system's control performance. Helicopter vibration is mainly generated by the periodic operation of its internal equipment, and its spectrum is represented by a mid-to-low frequency line spectrum; therefore, the resulting vibration is also called line spectrum vibration. Due to the diversity of helicopter equipment and varying operating states, the frequency components of structural vibration are complex, making it difficult to directly obtain the frequency information of the vibration signal. This poses a significant challenge to methods for accurately acquiring frequency information. Therefore, there is an urgent need in this field for an adaptive line spectrum vibration control method to actively control helicopter vibration.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of the present invention, and therefore may contain information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] To address the aforementioned technical problems, the objective of this invention is to propose an adaptive line spectrum vibration active control method. This invention utilizes adaptive spectrum shaping to reduce vibrations in the main frequency components with large amplitudes, thereby significantly reducing the control force provided by the actuator and avoiding the actuator's saturation effect. To achieve the above objective, this invention employs the following scheme.

[0007] An adaptive line spectrum vibration active control method for helicopters, the method comprising the following steps:

[0008] Step S100: Conduct a system identification experiment on the helicopter secondary channel to obtain a secondary channel model;

[0009] Step S200: Use a frequency estimation algorithm to estimate the frequency of the signals generated by the vibration of multiple helicopter vibration sources, and obtain the vibration frequency of the vibration sources;

[0010] Step S300: Using the vibration frequency of the vibration source as the reference signal frequency and the vibration acceleration signal of the control point as the feedback signal, adaptive line spectrum vibration active control is performed on the vibration generated at the vibration source of the helicopter. The helicopter vibration is actively controlled through adaptive line spectrum vibration control to achieve vibration reduction effect.

[0011] In step S100, the positions of the actuator and control point as the secondary source are determined, multiple sets of excitation signals and control point response signals are obtained through the excitation of the secondary source, and the secondary channel is systematically identified using the excitation signals and response signals.

[0012] In step S100, acceleration sensors are installed at the actuator and control point locations, respectively. The average autocorrelation function of the excitation signal and the average cross-correlation function of the excitation and response signals are obtained using multiple sets of excitation signals and control point response signals, respectively. The ratio of the autocorrelation function and the cross-correlation function is the frequency response function from the actuator to the control point. The frequency response function is identified as an infinite impulse response filter model (IIR model) using the least squares method.

[0013] In step S200, a frequency estimation algorithm based on an adaptive notch filter is used, including the following steps:

[0014] Step S201: Input the input signal into the adaptive notch filter (ANF) to obtain the output signal;

[0015] Step S202: Construct the error function and calculate the error signal using the output signal;

[0016] Step S203: Adjust the notch frequency of the adaptive notch filter according to the error signal and the adaptive algorithm;

[0017] Step S204: When the error signal is at its minimum, the notch frequency is equal to the signal frequency, that is, the notch frequency at this time is the estimated value of the signal frequency; the estimated value of the signal frequency is the vibration frequency of the vibration source.

[0018] In step S200, the frequency estimation algorithm includes frequency estimation algorithms based on adaptive notch filter, Fourier transform, short-time Fourier transform, and wavelet transform.

[0019] Step S300 includes: using a harmonic signal generator to generate a pair of orthogonal signals based on the vibration frequency of the vibration source; inputting the linear combination of the pair of orthogonal signals into a controller; the controller output includes two branches: a reduction branch and an equalization branch.

[0020] A pair of orthogonal signals is: x a (n)=cos(ω c n), x b (n)=sin(ω c n); the linear combination of a pair of orthogonal signals is y(n) = w a (n)x a (n)+w b (n)x b (n);

[0021] Where, ω c The vibration frequency of the vibration source described in step S200, w a (n) and w b (n) are the controller coefficients; w a (n) represents the controller coefficient corresponding to the cosine signal, w b (n) represents the controller coefficient corresponding to the sinusoidal signal; x a (n) represents the cosine signal, x b (n) represents a sinusoidal signal; n = 1, 2, 3… represents the discretization of a continuous signal; x a (n), x b The linear combination y(n) of (n) approximates the vibration signal.

[0022] From the properties of trigonometric functions, we know that: Any harmonic signal can be obtained by superimposing a pair of sine and cosine signals of the same frequency; therefore, through orthogonal signals; x a (n), x b The linear combination y(n) of (n) approximates the vibration signal.

[0023] The gain coefficients of the reduction branch and the equalization branch are 1-β and β, respectively; the outputs of the reduction branch and the equalization branch are respectively represented as y c (n)=(1-β)y(n) and y b (n) = βy(n).

[0024] The update equation for the controller is:

[0025] w a (n+1)=w a (n)+μ a e s (n)x′ a (n)

[0026] w b (n+1)=w b (n)+μ b e s (n)x′ b (n)

[0027] x′ a (n)=x a (n)*s(n)

[0028] x′ b (n)=x b (n)*s(n)

[0029] Among them, w a (n+1), w b (n+1) represents the updated values ​​of the two controller coefficients at the next time step; w a (n), w b (n) represents the values ​​of the two controller coefficients at the current time; μ a μ b It is the convergence factor, an adjustable parameter that determines the speed of the iteration of the update equation; e s (n) represents the system's error output; x′ a (n), x′ b (n) is the filter reference signal, which is the reference signal x. a (n) and x b (n) is the output after passing through the secondary channel filter model; s(n) is the impulse response function from the actuator to the control point, representing the secondary channel model.

[0030] In step S300, the controller can simultaneously control the vibration frequencies of multiple vibration sources; μ a μ b The larger the value, the faster the convergence speed.

[0031] Compared with the prior art, the present invention has the following technical effects:

[0032] Helicopter vibration suppression is achieved through an adaptive line spectrum vibration active control method. This method is simple, requiring no complex algorithm construction, and can accurately correct and control the amplitude of the main vibration frequencies in the frequency domain. While suppressing vibration frequencies, it minimizes the control force provided by the actuators, avoiding actuator saturation effects. The method described in this invention can effectively reduce the main frequency components in the vibration signal during helicopter flight, ensuring the normal operation of helicopter structural components and onboard equipment, preventing flight accidents, and improving the working environment for pilots and other aircrew. Attached Figure Description

[0033] The accompanying drawings illustrate exemplary embodiments of the invention and, together with the description thereof, serve to explain the principles of the invention. These drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification.

[0034] Figure 1 This is a schematic diagram of the steps of an adaptive line spectrum vibration active control method according to an embodiment of the present invention;

[0035] Figure 2 This is an adaptive vibration shaping control block diagram of an adaptive line spectrum vibration active control method according to an embodiment of the present invention;

[0036] Figure 3 This is a diagram showing the convergence process of reference signal frequency estimation in an adaptive line spectrum vibration active control method according to the present invention.

[0037] Figure 4 This is a flowchart of the secondary channel IIR filter model identification of an adaptive line spectrum vibration active control method according to an embodiment of the present invention;

[0038] Figure 5 This is a frequency response diagram of the secondary channel model identification result of an adaptive line spectrum vibration active control method according to the present invention;

[0039] Figure 6 This is a time-domain simulation diagram of different β parameter values ​​controlling different line spectrum amplitudes according to an embodiment of the adaptive line spectrum vibration active control method of the present invention;

[0040] Figure 7 This is a time-domain simulation diagram showing the influence of different μ parameter values ​​on the convergence speed of the control algorithm in an adaptive line spectrum vibration active control method according to an embodiment of the present invention.

[0041] Figure 8 This is a time-domain simulation diagram of the controlled point under whether or not it is under control during vibration source excitation in an adaptive line spectrum vibration active control method according to an embodiment of the present invention.

[0042] Figure 9This is a frequency domain simulation diagram of the controlled point under whether or not there is control during vibration source excitation in an adaptive line spectrum vibration active control method according to an embodiment of the present invention.

[0043] Figure 10 This is a simplified model of a helicopter main gearbox test bench for an adaptive line spectrum vibration active control method according to an embodiment of the present invention;

[0044] Figures 11(a) to 11(b) This is a schematic diagram of a pair of orthogonal signals according to an embodiment of the present invention. Detailed Implementation

[0045] The following is in conjunction with the appendix Figures 1 to 11(b) The present invention will be further described in detail below with reference to the embodiments. It is to be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be noted that, for ease of description, only the parts relevant to the present invention are shown in the accompanying drawings.

[0046] It should be noted that, unless otherwise specified, the embodiments and features described in this invention can be combined with each other. The technical solution of this invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0047] Unless otherwise stated, the exemplary embodiments / exemplifications shown are to be understood as providing exemplary features of various details that provide ways in which the technical concept of the invention can be implemented in practice. Therefore, unless otherwise stated, the features of the various embodiments / exemplifications may be additionally combined, separated, interchanged and / or rearranged without departing from the technical concept of the invention.

[0048] The use of crosshairs and / or shading in the accompanying drawings is generally used to clarify the boundaries between adjacent components. Thus, unless otherwise stated, the presence or absence of crosshairs or shading does not convey or indicate any preference or requirement for the specific material, material properties, dimensions, proportions, commonalities between the illustrated components, or any other characteristics, properties, etc., of the components. Furthermore, in the accompanying drawings, the dimensions and relative dimensions of components may be exaggerated for clarity and / or descriptive purposes. When exemplary embodiments can be implemented differently, a specific process sequence may be performed in a different order than that described. For example, two consecutively described processes may be performed substantially simultaneously or in the reverse order of their description. Furthermore, the same reference numerals denote the same components.

[0049] When a component is referred to as being "on" or "above" another component, "connected to," or "joined to" another component, the component may be directly on, directly connected to, or directly joined to the other component, or there may be intermediate components. However, when a component is referred to as being "directly on" another component, "directly connected to," or "directly joined to" another component, there are no intermediate components. Therefore, the term "connection" can refer to a physical connection, an electrical connection, etc., and may or may not have intermediate components.

[0050] For descriptive purposes, the present invention may use spatial relative terms such as “below,” “under,” “below,” “down,” “above,” “above,” “higher,” and “side (e.g., in a “sidewall”)” to describe the relationship between one component and another component as shown in the accompanying drawings. In addition to the orientations depicted in the drawings, the spatial relative terms are also intended to encompass different orientations of the device during use, operation, and / or manufacture. For example, if the device in the drawings is flipped, a component described as “below” or “under” another component or feature would subsequently be positioned “above” said other component or feature. Thus, the exemplary term “below” can encompass both “above” and “below” orientations. Furthermore, the device may be otherwise positioned (e.g., rotated 90 degrees or in other orientations), thus interpreting the spatial relative descriptive terms used herein accordingly.

[0051] The terminology used herein is for the purpose of describing particular embodiments and is not intended to be limiting. As used herein, unless the context clearly indicates otherwise, the singular forms “a” and “the” are intended to include the plural forms as well. Furthermore, when the terms “comprising” and / or “including” and variations thereof are used in this specification, it indicates the presence of the stated features, integrals, steps, operations, parts, components, and / or groups thereof, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, parts, components, and / or groups thereof. It should also be noted that, as used herein, the terms “substantially,” “about,” and other similar terms are used as approximate terms rather than as terms of degree, thus explaining the inherent biases in measurements, calculated values, and / or provided values ​​that would be recognized by one of ordinary skill in the art.

[0052] An adaptive line spectrum vibration active control method for helicopters, the method comprising the following steps:

[0053] Step S100: Conduct a system identification experiment on the helicopter secondary channel to obtain a secondary channel model;

[0054] Step S200: Use a frequency estimation algorithm to estimate the frequency of the signals generated by the vibration of multiple helicopter vibration sources, and obtain the vibration frequency of the vibration sources;

[0055] Step S300: Using the vibration frequency of the vibration source as the reference signal frequency and the vibration acceleration signal of the control point as the feedback signal, adaptive line spectrum vibration active control is performed on the vibration generated at the vibration source of the helicopter. The helicopter vibration is actively controlled through adaptive line spectrum vibration control to achieve vibration reduction effect.

[0056] In step S100, the positions of the actuator and control point as the secondary source are determined, multiple sets of excitation signals and control point response signals are obtained through the excitation of the secondary source, and the secondary channel is systematically identified using the excitation signals and response signals.

[0057] In step S100, acceleration sensors are installed at the actuator and control point locations, respectively. The average autocorrelation function of the excitation signal and the average cross-correlation function of the excitation and response signals are obtained using multiple sets of excitation signals and control point response signals, respectively. The ratio of the autocorrelation function and the cross-correlation function is the frequency response function from the actuator to the control point. The frequency response function is identified as an infinite impulse response filter model (IIR model) using the least squares method.

[0058] In step S200, a frequency estimation algorithm based on an adaptive notch filter is used, including the following steps:

[0059] Step S201: Input the input signal into the adaptive notch filter (ANF) to obtain the output signal;

[0060] Step S202: Construct the error function and calculate the error signal using the output signal;

[0061] Step S203: Adjust the notch frequency of the adaptive notch filter according to the error signal and the adaptive algorithm;

[0062] Step S204: When the error signal is at its minimum, the notch frequency is equal to the signal frequency, that is, the notch frequency at this time is the estimated value of the signal frequency; the estimated value of the signal frequency is the vibration frequency of the vibration source.

[0063] In step S200, the frequency estimation algorithm includes frequency estimation algorithms based on adaptive notch filter, Fourier transform, short-time Fourier transform, and wavelet transform.

[0064] Step S300 includes: using a harmonic signal generator to generate a pair of orthogonal signals based on the vibration frequency of the vibration source; inputting the linear combination of the pair of orthogonal signals into a controller; the controller output includes two branches: a reduction branch and an equalization branch.

[0065] A pair of orthogonal signals is: x a(n)=cos(ω c n), x b (n)=sin(ω c n); the linear combination of a pair of orthogonal signals is y(n) = w a (n)x a (n)+w b (n)x b (n);

[0066] Where, ω c The vibration frequency of the vibration source described in step S200, w a (n) and w b (n) are the controller coefficients; w a (n) represents the controller coefficient corresponding to the cosine signal, w b (n) represents the controller coefficient corresponding to the sinusoidal signal; x a (n) represents the cosine signal, x b (n) represents a sinusoidal signal; n = 1, 2, 3… represents the discretization of a continuous signal; x a (n), x b The linear combination y(n) of (n) approximates the vibration signal.

[0067] From the properties of trigonometric functions, we know that: Any harmonic signal can be obtained by superimposing a pair of sine and cosine signals of the same frequency; therefore, through orthogonal signals; x a (n), x b The linear combination y(n) of (n) approximates the vibration signal.

[0068] The gain coefficients of the reduction branch and the equalization branch are 1-β and β, respectively; the outputs of the reduction branch and the equalization branch are respectively represented as y c (n)=(1-β)y(n) and y b (n) = βy(n).

[0069] The update equation for the controller is:

[0070] w a (n+1)=w a (n)+μ a e s (n)x′ a (n)

[0071] w b (n+1)=w b (n)+μ b e s (n)x′ b (n)

[0072] x′ a(n)=x a (n)*s(n)

[0073] x′ b (n)=x b (n)*s(n)

[0074] Among them, w a (n+1), w b (n+1) represents the updated values ​​of the two controller coefficients at the next time step; w a (n), w b (n) represents the values ​​of the two controller coefficients at the current time; μ a μ b It is the convergence factor, an adjustable parameter that determines the speed of the iteration of the update equation; e s (n) represents the system's error output; x′ a (n), x′ b (n) is the filter reference signal, which is the reference signal x. a (n) and x b (n) is the output after passing through the secondary channel filter model; s(n) is the impulse response function from the actuator to the control point, representing the secondary channel model.

[0075] In step S300, the controller can simultaneously control the vibration frequencies of multiple vibration sources; μ a μ b The larger the value, the faster the convergence speed.

[0076] To better understand, Figure 1 This is a schematic diagram illustrating the steps of an adaptive line spectrum vibration active control method, as shown below. Figure 1 As shown, the adaptive line spectrum vibration active control method includes the following steps:

[0077] In the first step S1, a system identification experiment is conducted on the secondary channel of the helicopter. First, the positions of the actuator (secondary source) and the control point are determined. Multiple sets of excitation signals and control point response signals are obtained through the excitation of the secondary source. The secondary channel is then systematically identified using the excitation signals and response signals. The identified secondary channel model is an infinite impulse response filter (IIR model).

[0078] Accelerometers are installed at the actuator and control point locations, respectively. The average autocorrelation function of the excitation signal and the average cross-correlation function of the excitation and response signals are obtained using multiple sets of excitation signals and control point response signals. The ratio of the autocorrelation function to the cross-correlation function is the frequency response function from the actuator to the control point. The frequency response function is identified as an infinite impulse response filter model (IIR model) using the least squares method.

[0079] In the second step S2, the frequency of the signal generated by the vibration source is estimated. The frequency estimation algorithm is based on the adaptive notch filter (ANF) and is specifically operated as follows: First, the input signal is passed through the adaptive notch filter (ANF) to obtain the output signal; second, a suitable error function is constructed, and the error signal is calculated using the output signal; then, the notch filter frequency is adjusted according to the error signal and the adaptive algorithm; finally, when the error signal is minimized, the notch filter frequency is equal to the signal frequency, that is, the notch filter frequency at this time is the estimated value of the signal frequency.

[0080] For an input signal:

[0081] x(n) = Acos(w0n + θ)

[0082] In the formula: A, w0, and θ represent the amplitude, frequency, and phase of the signal, respectively.

[0083] The adaptive notch filter (ANF) is taken as an adaptive finite impulse response notch filter (FIR-ANF), and its transfer function is:

[0084] H(z,w)=1-2cos(z) -1 +z -2

[0085] In the formula: w is the estimated value of the input signal frequency w0;

[0086] The output signal of the input signal x(n) after passing through FIR-ANF is:

[0087] e(n)=x(n)H(z,w)=x(n)-2coswx(n-1)+x(n-2)

[0088] Construct a suitable error function using the output signal:

[0089] J(w)=e 2 (k)

[0090] The estimation iterative formula for the estimated frequency w obtained from the LMS adaptive algorithm is as follows:

[0091]

[0092] The following example illustrates the effect. The input signal amplitude is set to A = 1.5, frequency w0 = 0.1π, phase θ = π / 6, and the initial iteration frequency is 0.5π. The iteration process is shown in the figure. Figure 3 It can be seen that the iteration frequency value converges rapidly from 0.5π to the input signal frequency of 0.1π.

[0093] In the third step S3, adaptive line spectrum vibration active control is performed using the vibration frequency of the vibration source as the reference signal frequency and the vibration acceleration signal of the control point as the feedback signal. The specific process is as follows: A pair of orthogonal signals is generated using a harmonic signal generator: x a (n)=cos(ω c n) and x b (n)=sin(ω c n), such as Figures 11(a) to 11(b) As shown, Figure 11(a) shows a cosine signal, and Figure 11(b) shows a sine signal. Where ω c This is the vibration frequency of the vibration source identified in step S2. The input to the controller is a linear combination of these two orthogonal signals, y(n) = w. a (n)x a (n)+w b (n)x(n), since the vibration signal is a harmonic signal, and according to the properties of trigonometric functions: Any harmonic signal can be obtained by superimposing a pair of sine and cosine signals of the same frequency; therefore, through orthogonal signals; x a (n), x b The linear combination y(n) of (n) approximates the vibration signal.

[0094] Where w a (n) and w b (n) are the controller coefficients, which are two parameters of the controller to be determined. The optimal values ​​of the two parameters w need to be obtained through convergence of an adaptive algorithm. a and w b The controller uses the reference signal x a x b With w a w b A linear combination yields an approximate output of the vibration signal. The controller output comprises two branches: a reduction branch and an equalization branch. The gain coefficients of the reduction and equalization branches are 1-β and β, respectively, where β takes a value between 0 and 1, representing the proportion of residual vibration after reduction. Therefore, the output of the two branches can be written as y c (n)=(1-β)y(n) and y b (n) = βy(n).

[0095] Furthermore, the update equation for the controller is w l (n+1)=w l (n)+μ l e s (n)x′ l (n), (l=a,b), where x′ l (n)=x l (n)*s(n), (l=a,b) are the filter reference signals, μl is the convergence factor, and s(n) is the frequency response function from the actuator to the control point.

[0096] In this step, the controller can simultaneously control the vibration frequencies of multiple vibration sources; convergence factor μ l The larger the value, the faster the convergence speed.

[0097] In one example, the vibration signal mainly contains four frequency components: 27Hz, 54Hz, 80Hz, and 106Hz. When controlling each frequency component separately, the β values ​​are set to 0.8, 0.5, 0.3, and 0.1 respectively. These different β values ​​demonstrate that the control algorithm can precisely control the vibration amplitude of each spectral line. The control effect is as follows: Figure 6 As shown in the figure, the amplitude of the first spectral line with a frequency of 27 Hz decreased by 20.00% after control was implemented; the amplitude of the second spectral line with a frequency of 54 Hz decreased by 49.15%; the amplitude of the third spectral line with a frequency of 80 Hz decreased by 71.17%; and the amplitude of the fourth spectral line with a frequency of 106 Hz decreased by 90.43%. These values ​​are basically consistent with the set β parameter value, indicating that the amplitude of each frequency component in the signal can be precisely controlled by setting the β value, demonstrating the effectiveness of the adaptive line spectrum vibration active control method.

[0098] Meanwhile, in order to explain the convergence factor μ i The larger the value of μ, the faster the convergence speed. The control algorithm is configured with μ... i The values ​​are 0.0001, 0.0005, 0.001, and 0.005, such as... Figure 7 As shown in the four figures, μ i When μ = 0.0001, the control system converges after 4.5 seconds; i When μ = 0.0005, the control system converges after 1.4 seconds; i When μ = 0.001, the control system converged after 0.7s; i When μ = 0.005, the control system converges quickly in 0.2 seconds. This reflects the convergence factor μ of the control algorithm. i The larger the value, the faster the convergence speed. The method of the present invention will be further described below with reference to the accompanying drawings.

[0099] Figure 1This is a flowchart of an adaptive line spectrum vibration active control method according to the present invention. First, the positions of the actuator (secondary source) and the control point need to be determined. Multiple sets of excitation signals and control point response signals are obtained through excitation from the secondary source. The excitation and response signals are used to systematically identify the secondary channel, obtaining the coefficients of the secondary channel model (IIR model). An adaptive notch filter frequency estimation algorithm is used to estimate the frequency of the signal generated by the helicopter vibration source, obtaining the vibration frequency of the source. The estimated vibration frequency of the source is used as a reference frequency to generate a reference signal, and the vibration acceleration signal of the control point is used as a feedback signal for adaptive line spectrum vibration active control. This method can accurately correct and control the amplitude of the main vibration frequency in the frequency domain, suppressing the vibration frequency while minimizing the control force provided by the actuator, thus avoiding the saturation effect of the actuator.

[0100] Figure 2 This is a control block diagram of an adaptive line spectrum vibration active control method according to the present invention, wherein the harmonic signal generator generates a pair of orthogonal signals, i.e., reference signals, based on the vibration frequency of the vibration source estimated in the second step S2:

[0101] x a (n)=cos(w p n),x b (n)=sin(w p n)

[0102] In the formula: x a (n), x b (n) is the reference signal; w p It is the reference signal frequency;

[0103] The controller output is a linear combination of these, i.e.:

[0104] y(n)=w a (n)x a (n)+w b (n)x b (n)

[0105] In the formula: w a (n), w b (n) is the coefficient of the controller;

[0106] The controller output consists of two branches: a reduction branch and an equalization branch. These two branches have different gain coefficients, 1-β and β. The outputs of the reduction branch and the equalization branch are as follows:

[0107] y c (n)=(1-β)y(n),y b (n)=βy(n)

[0108] Therefore, the system error output is:

[0109]

[0110] In the formula: d(n) represents the primary noise; represents linear convolution; s(n) represents the impulse response function of the secondary channel.

[0111] To control the amplitude of the residual signal, the system feeds back a pseudo-error signal to the adaptive system. This pseudo-error signal is defined as follows:

[0112]

[0113] In the formula: The secondary channel filter model is for identification.

[0114] According to the stochastic gradient method, the controller update equation can be expressed as:

[0115]

[0116]

[0117] in: This is the reference signal for filtering.

[0118] When the adaptive algorithm converges, the pseudo-error signal e s (n) will approach 0. Therefore, let e s When (n) = 0, the residual noise can be expressed as:

[0119] e(n)=βd(n)

[0120] Therefore, the vibration reduction amplitude of different frequency components can be controlled by setting the β value. The smaller the β value, the smaller the residual noise, the larger the vibration reduction amplitude, and the better the vibration reduction effect.

[0121] In a preferred embodiment of the present invention, in the first step S1: the secondary channel filter model is identified using the excitation and response signals of the exciter. Assuming the data length of the excitation and response is L, the average autocorrelation function of the excitation signal and the average cross-correlation function of the excitation and response can be expressed as:

[0122]

[0123]

[0124] l = 0, ±1, ..., ±(L-1)

[0125] In the formula: N is the number of sample data. It is the average autocorrelation function of the excitation signal. It is the average cross-correlation function of the excitation signal and the response signal. and The discrete Fourier transforms of these spectra are represented as the self-power spectrum and the cross-power spectrum, respectively, while the frequency response function can be expressed as the ratio of the self-power spectrum to the cross-power spectrum. Because... and The length is 2L-1, and the first L points are truncated for a discrete Fourier transform. Let m = l + (L-1), the estimated frequency response function can be expressed as:

[0126]

[0127] In the formula: W L =exp(-2jπ / L). The impulse response of the system is the inverse Fourier transform of the frequency response function, that is:

[0128]

[0129] Since the impulse response function h(n) is a decaying function, to improve the signal-to-noise ratio, its near-zero values ​​can be truncated before system identification. The system identification method uses the least squares method.

[0130] The system identification process is as follows: Figure 4 As shown. During the experiment, accelerometers were first installed at the actuator and control point locations. Multiple sets of excitation signals and control point response signals were used to obtain the average autocorrelation function of the excitation signal and the average cross-correlation function of the excitation and response. The ratio of the autocorrelation function to the cross-correlation function is the frequency response function from the actuator to the control point. Then, an inverse Fourier transform was performed on the frequency response function to obtain the impulse response function. Finally, the least squares method was used to identify the impulse response function as an IIR model. In a preferred embodiment of the invention, accelerometers are installed at the actuator and control point locations, such as... Figure 10 As shown in Table 1, the coefficients of the 50th order IIR model are obtained by collecting, analyzing and calculating multiple sets of excitation signals and control point response signals.

[0131] Table 1

[0132] 1 2 3 4 … 47 48 49 50 A 1 0.8932 0.1041 -0.0100 … 0.1225 -0.1874 -0.4326 -0.4934 B 0.3785 0.2533 -0.1291 -0.2329 … -0.1848 -0.2812 -0.2973 -0.2458

[0133] In a preferred embodiment of the present invention, in the second step S2: the vibration frequency of the vibration source is estimated using an adaptive notch filter frequency estimation algorithm.

[0134] In a preferred embodiment of the present invention, in the third step S3: the vibration frequency of the vibration source is used as the reference signal frequency, and the vibration acceleration signal of the control point is used as the feedback signal to perform adaptive line spectrum vibration active control on the helicopter vibration source. The helicopter vibration is actively controlled by adaptive line spectrum vibration control to achieve the expected vibration reduction effect.

[0135] The specific implementation steps are as follows:

[0136] (1) After obtaining the vibration acceleration signal x(t), the main frequency components w contained in the signal are estimated first using the adaptive notch filter (ANF) frequency estimation algorithm;

[0137] (2) Generate reference signal x based on w a (n) = cos(ωn) and x b y(n) = sin(ωn), and the controller output is a linear combination of these, y(n) = w a (n)x a (n)+w b (n)x b (n), where w a (n),w b (n) are the controller coefficients;

[0138] (3) The error signal is obtained by superimposing the actuator output and the vibration response of the vibration source at the control point. Where y c (n)=(1-β)y(n), s(n) is the secondary channel model;

[0139] (4) The adaptive algorithm continuously updates the controller coefficient w based on the error signal and the reference signal. a (n),w b (n), until the error signal e(n) is minimized, the update equations are respectively Where μ is the convergence factor, e s (n) is the pseudo-error signal When the error signal e(n) is at its minimum, the vibration response at the control point is at its minimum, thus achieving the vibration reduction effect.

[0140] In one instance, such as Figure 3 The graph shows the convergence process of frequency estimation for the reference signal. It demonstrates that the iterative frequency estimation quickly converges to the true signal frequency value. Figure 4 The flowchart for identifying the secondary channel model involves using multiple sets of excitation signals and control point response signals to obtain the average autocorrelation function of the excitation signal and the average cross-correlation function of the excitation and response. The ratio of the autocorrelation function to the cross-correlation function is the frequency response function from the actuator to the control point. Then, an inverse Fourier transform is performed on the frequency response function to obtain the impulse response function. Finally, the least squares method is used to identify this impulse response function as the IIR model. Figure 5The figure shows the frequency response of the secondary channel model identification results. As can be seen from the figure, the amplitude of the theoretically calculated amplitude response is basically consistent with that of the identified IIR model across the entire frequency range, and the phase of the theoretically calculated phase response is also basically consistent with that of the identified IIR model across the entire frequency range, which demonstrates the accuracy of the secondary channel model identification. Figure 6 The figure shows time-domain simulations of different frequency components controlled by different parameter β values ​​according to an adaptive line spectrum vibration active control method of the present invention. As can be seen from the figure, the amplitudes of the four spectral lines are reduced by 20%, 50%, 70%, and 90% respectively, which is consistent with the β values ​​set respectively. Figure 7 The four figures show the simulation results of the influence of different convergence parameter μ values ​​on the convergence speed of the control algorithm in an adaptive line spectrum vibration active control method according to the present invention. As can be seen from the four figures, the larger the μ value, the faster the convergence speed. Figure 8 This is a time-domain simulation diagram of the controlled point under and without control during vibration source excitation according to an adaptive line spectrum vibration active control method of the present invention. Figure 9 This is a frequency domain simulation diagram of the controlled point under and without control during vibration source excitation, according to an adaptive line spectrum vibration active control method of the present invention. Figure 8 and Figure 9 As can be seen from the data, with the application of control, the vibration at the control point gradually converges and tends to stabilize. The spectrum shows that the main vibration frequency components are significantly suppressed, while other frequency components with small amplitudes are unaffected. This indicates that the method can accurately correct and control the vibration response of the control point in the frequency domain. While suppressing the vibration frequency, it can minimize the control force provided by the actuator and avoid the saturation effect of the actuator. Figure 10 This is a simplified experimental model of a helicopter main reducer based on an adaptive line spectrum vibration active control method according to the present invention. The figure shows the specific locations of the vibration control point and the actuator. During the experiment, the sensor is placed at this location to perform vibration control.

[0141] In the description of this specification, the references to terms such as "one embodiment / mode," "some embodiments / modes," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment / mode or example is included in at least one embodiment / mode or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment / mode or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments / modes or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments / modes or examples described in this specification, as well as the features of different embodiments / modes or examples.

[0142] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0143] Those skilled in the art should understand that the above embodiments are merely for illustrating the present invention and are not intended to limit the scope of the invention. Those skilled in the art can make other changes or modifications based on the above disclosure, and these changes or modifications still fall within the scope of the present invention.

Claims

1. A method for active control of adaptive line spectrum vibration in helicopters, characterized in that, The method includes the following steps: Step S100: Conduct a system identification experiment on the helicopter secondary channel to obtain the secondary channel model; Step S200: Use a frequency estimation algorithm to estimate the frequency of the signals generated by the vibration of multiple helicopter vibration sources, and obtain the vibration frequency of the vibration sources; Step S300: Using the vibration frequency of the vibration source as the reference signal frequency and the vibration acceleration signal of the control point as the feedback signal, adaptive line spectrum vibration active control is performed on the vibration generated at the vibration source of the helicopter. The helicopter vibration is actively controlled through adaptive line spectrum vibration control to achieve vibration reduction effect. Step S300 includes: using a harmonic signal generator to generate a pair of orthogonal signals based on the vibration frequency of the vibration source; inputting the linear combination of the pair of orthogonal signals into a controller; the controller output includes two branches: a reduction branch and an equalization branch; A pair of orthogonal signals are: , A linear combination of a pair of orthogonal signals is ; in, It is the vibration frequency of the vibration source mentioned in step S200. and These are controller coefficients; This represents the controller coefficients corresponding to the cosine signal. This represents the controller coefficient corresponding to the sinusoidal signal; Represents a cosine signal. This represents a sinusoidal signal; n = 1, 2, 3… represents the discretization of a continuous signal. linear combination It approximates the expression of vibration signals.

2. The method according to claim 1, characterized in that, In step S100, the positions of the actuator and control point as the secondary source are determined, multiple sets of excitation signals and control point response signals are obtained through the excitation of the secondary source, and the secondary channel is systematically identified using the excitation signals and response signals.

3. The method according to claim 1, characterized in that, In step S100, acceleration sensors are installed at the actuator and control point locations, respectively. The average autocorrelation function of the excitation signal and the average cross-correlation function of the excitation and response signals are obtained using multiple sets of excitation signals and control point response signals, respectively. The ratio of the autocorrelation function and the cross-correlation function is the frequency response function from the actuator to the control point. The frequency response function is identified as an infinite impulse response filter model (IIR model) using the least squares method.

4. The method according to claim 1, characterized in that, In step S200, the frequency estimation algorithm includes frequency estimation algorithms based on adaptive notch filter, Fourier transform, short-time Fourier transform, and wavelet transform.

5. The method according to claim 1, characterized in that, The gain coefficients of the reduction and equalization branches are respectively and The outputs of the reduction branch and the equalization branch are respectively expressed as: and .

6. The method according to claim 5, characterized in that, The update equation for the controller is: ； ； ； ； in, , This represents the updated values ​​of the two controller coefficients at the next time step; , This represents the values ​​of the two controller coefficients at the current moment; , It is the convergence factor, an adjustable parameter that determines the speed of the iteration of the update equation; This represents the system's error output; , It is the filter reference signal. and The output after passing through the secondary channel filter model; It is the impact response function from the actuator to the control point, representing the secondary channel model.

7. The method according to claim 6, characterized in that, In step S300, the controller can simultaneously control the vibration frequencies of multiple vibration sources; , The larger the value, the faster the convergence speed.

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