Active control method for self-adaptive line spectrum vibration of helicopter

Abstract

The invention discloses a self-adaptive line spectrum vibration active control method for a helicopter, and the method comprises the steps: carrying out the system identification experiment of a secondary channel of the helicopter, so as to obtain a secondary channel model; performing frequency estimation on signals generated by vibration of the plurality of helicopter vibration sources by adopting a frequency estimation algorithm to obtain vibration frequency of the vibration sources; the vibration source vibration frequency serves as the reference signal frequency, the vibration acceleration signal of the control point serves as the feedback signal, self-adaptive line spectrum vibration active control is conducted on vibration generated at the helicopter vibration source, and active control is conducted on the helicopter vibration in a self-adaptive line spectrum vibration control mode so as to achieve the vibration reduction effect. According to the method, main frequency components in the vibration signals during flight of the helicopter can be effectively reduced, normal work of structural components and airborne equipment of the helicopter is guaranteed, and flight accidents are avoided.

Description

Technical Field

[0001] The present invention belongs to the field of helicopter vibration control, and particularly relates to an active control method for adaptive line spectrum vibration. Background Technique

[0002] The vibration level of a helicopter airframe has always been an important indicator for evaluating the performance of a helicopter. During flight, rotating components such as the rotor, tail rotor, engine, and transmission will generate alternating loads. In addition, during ground taxiing, landing, weapon firing, and other operations, the helicopter will also generate exciting forces. These alternating loads and exciting forces will cause significant airframe vibrations, bringing many hazards: it is easy to make the pilot uncomfortable and fatigued, resulting in operational errors and affecting flight safety; it is easy to make the crew members inside the aircraft uncomfortable and affect their mental state; it is easy to cause fatigue damage to the airframe structure, reducing the reliability and safety of the whole aircraft; it is easy to make the instruments and meters malfunction and affect the reliability of the system; it is easy to reduce the hit rate of weapon firing and affect combat effectiveness, and so on. Therefore, reducing the vibration level of the helicopter airframe has always been a difficult problem that needs to be solved urgently during the development and use of helicopters.

[0003] Helicopter active control technology is one of the effective ways to reduce the vibration level of helicopters. Among various active control technologies, Active Control of Structural Response (ACSR) is one of the few technologies that have been successfully applied in engineering. Its basic principle is: ACSR arranges sensors at key positions of the airframe vibration reduction to collect the excitation response signals generated by the excitation of the vibration source and inputs them into the controller. The controller inputs the control signals into the actuators arranged on the airframe structure. The actuators drive the airframe structure to generate the desired actuator response at the key positions of vibration reduction. The excitation response and the actuator response at the key positions are superimposed and cancel each other out to obtain the control response error. Minimizing the control response error can achieve active control of the airframe vibration. ACSR directly targets the structural vibration response, has the advantages of low power consumption and being able to adapt to changes in rotor speed and flight conditions, can significantly reduce the vibration level of the helicopter, and has been considered the most potential helicopter vibration active control technology at present.

[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 will cause the system to be unable to eliminate the target vibration, greatly reducing the control performance of the system. The vibration of a helicopter is mainly generated by the periodic operation of its internal equipment, and the spectrum shows medium and low-frequency line spectra. Therefore, the generated vibration is also called line spectrum vibration. Due to the variety of helicopter equipment and different working states, the frequency components of the structural vibration are complex, and it is difficult to directly obtain the frequency information of the vibration signal, posing a great challenge to the precise acquisition method of 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 above information disclosed in the background section is only used to enhance the understanding of the background of the present invention, and thus may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0006] In order to solve the above technical problems, the object of the present invention is to propose an adaptive line spectrum vibration active control method. The present invention reduces vibration for the frequency components with relatively large main amplitudes through an adaptive spectrum shaping method, thereby greatly reducing the control force provided by the actuator and avoiding the saturation effect of the actuator. To achieve the above object, the present invention adopts the following solutions.

[0007] A helicopter adaptive line spectrum vibration active control method, 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 frequencies of signals generated by multiple helicopter vibration sources to obtain the vibration source frequencies;

[0010] Step S300, use the vibration source frequencies as the reference signal frequencies and the vibration acceleration signal at the control point as the feedback signal to actively control the vibration generated at the helicopter vibration source, and actively control the helicopter vibration through an adaptive line spectrum vibration control method to achieve a vibration reduction effect.

[0011] In step S100, determine the positions of the actuator and the control point as the secondary source, obtain multiple groups of excitation signals and the response signals of the control point through secondary source excitation, and use the excitation signals and the response signals to conduct system identification on the secondary channel.

[0012] In step S100, install acceleration sensors at the positions of the actuator and the control point respectively, use multiple groups of excitation signals and the response signals of the control point to obtain the average autocorrelation function of the excitation signals, the average cross-correlation function of the excitation signals and the 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, and use the least squares method to identify this frequency response function as an infinite impulse response filter model (IIR model).

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

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

[0015] Step S202, construct an 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 minimized, 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 filters, Fourier transforms, short-time Fourier transforms, and wavelet transforms.

[0019] In step S300, it includes: using a harmonic signal generator to generate a pair of orthogonal signals according to the vibration frequency of the vibration source; inputting the linear combination of the pair of orthogonal signals into the controller; the controller output includes two branches: a cancellation branch and an equalization branch.

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

[0021] Among them, ω c is the vibration frequency of the vibration source described in step S200, w a (n) and w b (n) are controller coefficients; w a (n) represents the controller coefficient corresponding to the cosine signal, w b (n) represents the controller coefficient corresponding to the sine signal; x a (n) represents the cosine signal, x b (n) represents the sine signal; n is 1, 2, 3... representing the discretization of the continuous signal; x a (n), x b (n)'s linear combination y(n) approximately represents the vibration signal.

[0022] It can be known from the properties of trigonometric functions that: Any harmonic signal can be obtained by superimposing a pair of sine and cosine signals with the same frequency. Therefore, through the orthogonal signals; x a (n), x b (n)'s linear combination y(n) approximately represents the vibration signal.

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

[0024] The update equation of 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 e b (n)x′

[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) represent the updated values of the coefficients of the two controllers at the next moment; w a (n), w b (n) represent the values of the coefficients of the two controllers at the current moment; μ a , μ b are convergence factors, which are adjustable parameters and determine the speed of the update equation iteration; e s (n) represents the error output of the system; x′ a (n), x′ b (n) is the filter reference signal, which is the output of the reference signals x a (n) and x b (n) after passing through the secondary channel filter model; s(n) is the impulse response function from the actuator to the control point and represents 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 they are, the faster the convergence speed.

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

[0032] Helicopter vibration suppression is carried out by an adaptive line spectrum vibration active control method. The algorithm is simple and does not require constructing complex algorithms. It can accurately correct and control the amplitude of the main vibration frequency in the frequency domain. While suppressing the vibration frequency, it can make the control force provided by the actuator as small as possible to avoid the saturation effect of the actuator. The method of the present invention can effectively reduce the main frequency components in the vibration signal during helicopter flight, ensure the normal operation of helicopter structural components and airborne equipment, avoid flight accidents, and at the same time improve the working environment of flight crew such as pilots. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0034] Figure 1 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 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 is a reference signal frequency estimation convergence process diagram of an adaptive line spectrum vibration active control method according to an embodiment of the present invention;

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

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

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

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

[0041] Figure 8 is a time-domain simulation diagram of the controlled point with and without control during the excitation of the vibration source of an adaptive line spectrum vibration active control method according to an embodiment of the present invention;

[0042] Figure 9It is the frequency-domain simulation diagram of the controlled point with and without control during the vibration source excitation of the adaptive line spectrum vibration active control method according to an embodiment of the present invention;

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

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

[0045] The following will further elaborate on the present invention in conjunction with the attached Figures 1 to 11(b) drawings and implementation manners. It can be understood that the specific implementation manners described herein are only used to explain the relevant content and do not limit the present invention. Additionally, it should be noted that for the convenience of description, only the parts related to the present invention are shown in the drawings.

[0046] It should be noted that, without conflict, the implementation manners in the present invention and the features in the implementation manners can be combined with each other. The following will detail the technical solutions of the present invention with reference to the drawings and in conjunction with the implementation manners.

[0047] Unless otherwise specified, the illustrated exemplary implementation manners / embodiments will be understood to provide exemplary features of various details of some ways that can implement the technical concept of the present invention in practice. Therefore, unless otherwise specified, without departing from the technical concept of the present invention, the features of various implementation manners / embodiments can be additionally combined, separated, interchanged, and / or rearranged.

[0048] In the drawings, cross-hatching and / or shading are generally used to make the boundaries between adjacent components clear. Thus, unless stated, the presence or absence of cross-hatching or shading does not convey or imply any preference or requirement for the specific material, material properties, dimensions, proportions, commonalities between the illustrated components, and / or any other characteristics, attributes, properties, etc. of the components. Additionally, in the drawings, for clarity and / or descriptive purposes, the dimensions and relative dimensions of the components may be exaggerated. When the exemplary embodiments can be implemented differently, the specific process sequences can be executed in a different order than described. For example, two consecutively described processes can be executed substantially simultaneously or in an order opposite to the described order. Moreover, the same reference numerals represent the same components.

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

[0050] For descriptive purposes, the present invention may use spatial relative terms such as "under", "below", "beneath", "lower", "above", "upper", "on", "over", "higher" and "side (e.g., as in "sidewall") etc., so as to describe the relationship between one component and another (other) component as shown in the drawings. In addition to the orientation depicted in the drawings, the spatial relative terms are also intended to encompass different orientations of the device during use, operation and / or manufacturing. For example, if the device in the drawings is flipped, the component described as "under" or "beneath" another component or feature will then be positioned "above" the other component or feature. Thus, the exemplary term "under" can encompass both "above" and "below" orientations. In addition, the device can be otherwise positioned (e.g., rotated 90 degrees or at other orientations), and thus, the spatial relative descriptors used herein are to be interpreted accordingly.

[0051] The terms used herein are for the purpose of describing particular embodiments and are not intended to be limiting. As used herein, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are also intended to include the plural forms. In addition, when the terms "comprise" and / or "include" and their variants are used in this specification, it is stated that there are the stated features, integers, steps, operations, components, assemblies and / or groups thereof, but do not preclude the presence or addition of one or more other features, integers, steps, operations, components, assemblies 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 and not as terms of degree, so that they are used to explain the inherent deviations of measured, calculated and / or provided values that would be recognized by a person of ordinary skill in the art.

[0052] A method for active control of helicopter adaptive line spectrum vibration, the method comprising the following steps:

[0053] Step S100, performing 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 frequencies of the signals generated by the vibrations of multiple helicopter vibration sources, and obtain the vibration frequencies of the vibration sources;

[0055] Step S300: Use the vibration frequency of the vibration source as the reference signal frequency, and use the vibration acceleration signal of the control point as the feedback signal to perform active control of the vibration generated at the helicopter vibration source by adaptive line spectrum vibration control, and actively control the helicopter vibration through adaptive line spectrum vibration control to achieve the vibration reduction effect.

[0056] In step S100, determine the positions of the actuators and control points serving as secondary sources, obtain multiple sets of excitation signals and the response signals of the control points through secondary source excitation, and perform system identification on the secondary channel using the excitation signals and the response signals.

[0057] In step S100, install acceleration sensors at the positions of the actuators and control points respectively, obtain the average autocorrelation function of the excitation signals and the average cross-correlation function of the excitation signals and the response signals respectively using multiple sets of excitation signals and the response signals of the control points. The ratio of the autocorrelation function and the cross-correlation function is the frequency response function from the actuator to the control point. Use the least squares method to identify this frequency response function as an infinite impulse response filter model (IIR model).

[0058] In step S200, adopt a frequency estimation algorithm based on an adaptive notch filter, 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 an 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 minimized, 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 filters, Fourier transforms, short-time Fourier transforms, and wavelet transforms.

[0064] In step S300, it includes: using a harmonic signal generator to generate a pair of orthogonal signals according to the vibration frequency of the vibration source; inputting the linear combination of the pair of orthogonal signals into the controller; the output of the controller includes two branches: a cancellation 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); A 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 is the vibration frequency of the vibration source described in step S200, w a (n) and w b (n) are controller coefficients; w a (n) represents the controller coefficient corresponding to the cosine signal, w b (n) represents the controller coefficient corresponding to the sine signal; x a (n) represents the cosine signal, x b (n) represents the sine signal; n is 1, 2, 3... representing the discretization of the continuous signal; x a (n), x b (n)'s linear combination y(n) approximately represents the vibration signal.

[0067] It can be known from the properties of trigonometric functions that: Any harmonic signal can be obtained by superimposing a pair of sine and cosine signals with the same frequency. Therefore, through the orthogonal signals; x a (n), x b (n)'s linear combination y(n) approximately represents the vibration signal.

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

[0069] The update equation of 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' ay(n) = x a y(n) * s(n)

[0073] x' b y(n) = x b y(n) * s(n)

[0074] where w a (n + 1), w b (n + 1) represent the updated values of the two controller coefficients at the next moment; w a (n), w b (n) represent the values of the two controller coefficients at the current moment; μ a , μ b are convergence factors, which are adjustable parameters and determine the iteration speed of the update equation; e s (n) represents the error output of the system; x' a (n), x' b (n) are the filter reference signals, which are the outputs after the reference signal x a (n) and x b (n) pass through the secondary channel filter model; s(n) is the impulse response function from the actuator to the control point and represents 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 they are, the faster the convergence speed.

[0076] For better understanding, Figure 1 is a schematic diagram of the steps of the adaptive line spectrum vibration active control method, as Figure 1 shown, the adaptive line spectrum vibration active control method includes the following steps:

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

[0078] Acceleration sensors are installed at the positions of the actuator and the control point respectively. The average autocorrelation function of the excitation signals and the average cross-correlation function of the excitation signals and the response signals are obtained respectively using multiple groups of excitation signals and the response signals of the control point. The ratio of the autocorrelation function and the cross-correlation function is the frequency response function from the actuator to the control point, and this 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 signal generated by the vibration of the vibration source is subjected to frequency estimation. The frequency estimation algorithm is a frequency estimation algorithm based on an adaptive notch filter. The specific operations are as follows: First, the input signal passes through an adaptive notch filter (ANF) to obtain an output signal; Second, a suitable error function is constructed, and the error signal is calculated using the output signal; Then, the notch frequency is adjusted according to the error signal and the adaptive algorithm; Finally, when the error signal is minimized, 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;

[0080] For an input signal:

[0081] x(n) = Acos(w 0 n + θ)

[0082] Where: A, w 0 , θ are 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 - 2coswz -1 + z -2

[0085] Where: w is the estimated value of the input signal frequency w 0 ;

[0086] The output signal of the input signal x(n) after passing through the 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] According to the LMS adaptive algorithm, the estimated iterative formula for the estimated frequency w is:

[0091]

[0092] The following illustrates the effect through an example. Set the amplitude A = 1.5, frequency w 0 = 0.1π, phase θ = π / 6, and the initial iterative frequency is 0.5π. The iterative process is shown in the figure. From Figure 3 it can be seen that the iterative frequency value quickly converges from 0.5π to the input signal frequency 0.1π.

[0093] In the third step S3, taking 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 for adaptive line spectrum vibration active control, the specific process is as follows: A pair of orthogonal signals are generated by a harmonic signal generator: x a (n) = cos(ω c n) and x b (n) = sin(ω c n), as Figures 11(a) to 11(b) shown. Figure 11(a) is a cosine signal and Figure 11(b) is a sine signal. Where ω c is the vibration frequency of the vibration source identified in step S2. The input of 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 with the same frequency. Therefore, through the orthogonal signals x a (n), x b (n), the vibration signal is approximately expressed by the linear combination y(n).

[0094] Where w a (n) and w b (n) are the controller coefficients, which are two undetermined parameters of the controller and need to converge to the optimal two parameter values w a and w b through an adaptive algorithm; The controller uses the reference signals x a x b and w a w b to linearly combine to obtain an approximate output of the vibration signal. The controller output contains two branches: a cancellation branch and an equalization branch. The gain coefficients of the cancellation branch and the equalization branch are 1 - β and β respectively, and the value of β ranges from 0 to 1. This value represents the proportion of the residual vibration after vibration reduction. Therefore, the outputs of the two branches can be written as y c (n) = (1 - β)y(n) and y b (n) = βy(n).

[0095] In addition, the update equation of 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) is the filter reference signal, 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; the convergence factor μ l The larger it is, the faster the convergence speed.

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

[0098] At the same time, in order to illustrate that the larger the convergence factor μ i is, the faster the convergence speed. In the control algorithm, the values of μ i are set to 0.0001, 0.0005, 0.001, and 0.005 respectively, as Figure 7 shown. It can be seen from the four figures that when μ i = 0.0001, the control system takes 4.5s to converge; when μ i = 0.0005, the control system converges in 1.4s; when μ i = 0.001, the control system has converged after 0.7s; when μ i = 0.005, the control system converges quickly after 0.2s. It reflects the characteristic that the larger the convergence factor μ i of the control algorithm is, the faster the convergence speed. The method of the present invention will be further described below with reference to the accompanying drawings.

[0099] Figure 1It is a flowchart of an adaptive line spectrum vibration active control method of the present invention. First, it is necessary to determine the positions of the actuators (secondary sources) and the control points. Multiple groups of excitation signals and the response signals of the control points are obtained through the excitation of the secondary sources. The secondary channel is system-identified using the excitation signals and the response signals to obtain the coefficients of the secondary channel model (IIR model); the frequency estimation algorithm of the adaptive notch filter is used to estimate the frequency of the signal generated by the helicopter vibration source to obtain the vibration source frequency; the estimated vibration source frequency is used as the reference frequency to generate a reference signal, and the vibration acceleration signal of the control point is used as the feedback signal for adaptive line spectrum vibration active control. It can accurately correct and control the amplitude of the main vibration frequency in the frequency domain. While suppressing the vibration frequency, the control force provided by the actuator can be made as small as possible to avoid the saturation effect of the actuator.

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

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

[0102] Where: x a (n), x b (n) are the reference signals; w p is the reference signal frequency;

[0103] The output of the controller is their linear combination, that is:

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

[0105] Where: w a (n), w b (n) are the coefficients of the controller;

[0106] Among them, the output of the controller includes two branches: the cancellation branch and the equalization branch. The two branches have different gain coefficients 1 - β and β. The outputs of the cancellation branch and the equalization branch are respectively:

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

[0108] Therefore, the error output of the system is:

[0109]

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

[0111] To achieve control over the amplitude of the residual signal, the system feeds a pseudo-error signal back to the adaptive system, and this pseudo-error signal is defined as:

[0112]

[0113] where: is the identified filter model of the secondary channel.

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

[0115]

[0116]

[0117] where: is the filtered reference signal.

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

[0119] e(n) = βd(n)

[0120] Therefore, the control over the damping amplitude of different frequency components can be achieved by setting the value of β. When the value of β is smaller, the residual noise is smaller, the damping amplitude is larger, and the damping effect is better.

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

[0122]

[0123]

[0124] l = 0, ±1,..., ±(L - 1)

[0125] where: N is the number of sample data, is the average autocorrelation function of the excitation signal, is the average cross-correlation function of the excitation signal and the response signal. and The discrete Fourier transforms of are respectively represented as the auto-power spectrum and the cross-power spectrum, and the frequency response function can be expressed as the ratio of the auto-power spectrum and the cross-power spectrum. Since and are of length 2L - 1, the first L points are intercepted for discrete Fourier transform. Let m = l+(L - 1), and the estimated frequency response function can be expressed as:

[0126]

[0127] where: 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, in order to improve the signal-to-noise ratio, the values close to zero at the back can be truncated, and then system identification is carried out. The system identification method uses the least squares method.

[0130] The process of system identification is as Figure 4 shown. During the experiment, first, acceleration sensors are installed at the positions of the actuator and the control point respectively. The average auto-correlation function of the excitation signal, the average cross-correlation function of the excitation and the response are obtained by using multiple groups of excitation signals and the response signals of the control point. The ratio of the auto-correlation function and the cross-correlation function is the frequency response function from the actuator to the control point. Then, the inverse Fourier transform of the frequency response function is performed to obtain the impulse response function. Finally, the impulse response function is identified as an IIR model by using the least squares method. In the preferred embodiment of the present invention, acceleration sensors are installed at the positions of the actuator and the control point respectively, as Figure 10 shown. By collecting, analyzing and calculating multiple groups of excitation signals and the response signals of the control point, the 50-order IIR model coefficients are obtained as shown in Table 1.

[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 the preferred embodiment of the present invention, in the second step S2: The frequency estimation algorithm of the adaptive notch filter is used to estimate the vibration frequency of the vibration source.

[0134] In the preferred embodiment of the present invention, in the third step S3: Taking 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, active adaptive line spectrum vibration control is performed on the helicopter vibration source. Active control of the helicopter vibration is carried out through the method of 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), first use the adaptive notch filter (ANF) frequency estimation algorithm to estimate the main frequency component ω contained in the signal;

[0137] (2) Generate reference signals x a (n) = cos(ωn) and x b (n) = sin(ωn). The output of the controller is a linear combination of them, 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 output of the actuator and the vibration response of the vibration source are superimposed at the control point to obtain the error signal where y c (n) = (1 - β)y(n), and s(n) is the secondary channel model;

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

[0140] In one example, such as Figure 3 is the convergence process diagram of the reference signal frequency estimation. It can be seen from the figure that the iterative frequency can quickly converge to the true frequency value of the signal. Such as Figure 4 is the flow chart of the secondary channel model identification. The average autocorrelation function of the excitation signal, the average cross-correlation function of the excitation and the response are obtained by using multiple groups of excitation signals and the response signals at the control point. The ratio of the autocorrelation function and the cross-correlation function is the frequency response function from the actuator to the control point. Then, the impulse response function is obtained by performing the inverse Fourier transform on the frequency response function. Finally, the impulse response function is identified as an IIR model by using the least squares method. Such as Figure 5The figure shows the frequency response diagram of the secondary channel model identification result. It can be seen from the figure that the amplitude-frequency response calculated theoretically is basically the same as that of the identified IIR model in terms of amplitude in the entire frequency range, and the phase-frequency response calculated theoretically is also basically the same as that of the identified IIR model in terms of phase in the entire frequency range, which demonstrates the accuracy of the secondary channel model identification. Figure 6 It is a time-domain simulation diagram for separately controlling different frequency components according to different parameter β values of an adaptive line spectrum vibration active control method of the present invention. It can be seen from the figure that the amplitudes of the four spectral lines are respectively reduced by 20%, 50%, 70%, and 90%, which is consistent with the separately set β. Figure 7 It is a simulation diagram of the influence of different convergence parameter μ values of an adaptive line spectrum vibration active control method of the present invention on the convergence speed of the control algorithm. It can be seen from the four figures that the larger the μ value, the faster the convergence speed. Figure 8 It is a time-domain simulation diagram of the controlled point with and without control during the vibration source excitation according to an adaptive line spectrum vibration active control method of the present invention. Figure 9 It is a frequency-domain simulation diagram of the controlled point with and without control during the vibration source excitation according to an adaptive line spectrum vibration active control method of the present invention. From Figure 8 and Figure 9 It can be seen that with the application of control, the vibration at the control point gradually converges and tends to be stable. It can be seen from the frequency spectrum that the main vibration frequency components are significantly suppressed, while the other frequency components with very small amplitudes are not affected, indicating that this method can accurately correct and control the vibration response of the control point in the frequency domain, and can minimize the control force provided by the actuator while suppressing the vibration frequency, avoiding the saturation effect of the actuator. Figure 10 It is a simplified diagram of a helicopter main reducer test bench model according to an adaptive line spectrum vibration active control method of the present invention. The specific positions of the vibration control points and the actuators are marked in the figure, and sensors are arranged at these positions during the experiment for vibration control.

[0141] In the description of this specification, the description with reference to terms such as "one embodiment / way", "some embodiments / ways", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment / way or example are included in at least one embodiment / way or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment / way or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments / ways or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments / ways or examples described in this specification and the features of different embodiments / ways or examples.

[0142] In addition, 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 quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise specifically defined.

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

Claims

1. An active control method for helicopter adaptive line spectrum vibration, characterized in that, the method comprises the following steps: Step S100, conduct a system identification experiment on the secondary channel of the helicopter to obtain the secondary channel model; Step S200, use a frequency estimation algorithm to estimate the frequencies of the signals generated by the vibrations of multiple helicopter vibration sources to obtain the vibration source frequencies; Step S300, use the vibration source frequency as the reference signal frequency and the vibration acceleration signal at the control point as the feedback signal to actively control the vibration generated at the helicopter vibration source, and actively control the helicopter vibration through the adaptive line spectrum vibration control method to achieve the vibration reduction effect.

2. The method according to claim 1, characterized in that, Preferably, in step S100, determine the positions of the actuator as the secondary source and the control point, obtain multiple groups of excitation signals and the response signals of the control point through the excitation of the secondary source, and use the excitation signals and the response signals to conduct system identification on the secondary channel.

3. The method according to claim 1, characterized in that, In step S100, install acceleration sensors at the positions of the actuator and the control point respectively, use multiple groups of excitation signals and the response signals of the control point to obtain the average autocorrelation function of the excitation signal, the average cross-correlation function of the excitation signal and the response signal respectively. The ratio of the autocorrelation function and the cross-correlation function is the frequency response function from the actuator to the control point, and use the least squares method to identify this frequency response function as an infinite impulse response filter model (IIR model).

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 filters, Fourier transforms, short-time Fourier transforms, and wavelet transforms.

5. The method according to claim 1, characterized in that, In step S300, it includes: using a harmonic signal generator to generate a pair of orthogonal signals according to the vibration source frequency; inputting the linear combination of the pair of orthogonal signals into the controller; the output of the controller includes two branches: a cancellation branch and an equalization branch.

6. The method according to claim 5, characterized in that, A pair of orthogonal signals are: x a (n) = cos(ω c n), x b (n) = sin(ω c n); A linear combination of the pair of orthogonal signals is y(n) = w a (n)x a (n) + w b (n)x b (n); where ω c is the vibration frequency of the vibration source described in step S200, w a (n) and w b (n) are controller coefficients; w a (n) represents the controller coefficient corresponding to the cosine signal, w b (n) represents the controller coefficient corresponding to the sine signal; x a (n) represents the cosine signal, x b (n) represents the sine signal; n is 1, 2, 3... representing the discretization of the continuous signal; x a (n), x b (n) The linear combination y(n) approximately represents the vibration signal.

7. The method according to claim 6, characterized in that, The gain coefficients of the attenuation branch and the equalization branch are 1 - β and β respectively; the outputs of the attenuation branch and the equalization branch are respectively expressed as y c (n) = (1 - β)y(n) and y b (n) = βy(n).

8. The method according to claim 7, characterized in that, The update equation of the controller is: w a (n + 1) = w a (n) + μ a e s (n)x' a (n) w b (n + 1) = w b (n) + μ b e s (n)x′ b (n) x′ a (n) = x a (n) * s(n) x′ b (n) = x b (n) * s(n) where, w a (n + 1), w b (n + 1) represents the updated values of the two controller coefficients at the next moment; w a (n), w b (n) represents the values of the two controller coefficients at the current moment; μ a , μ b are convergence factors, which are adjustable parameters and determine the speed of the update equation iteration; e s (n) represents the error output of the system; x′ a (n), x′ b (n) is the filter reference signal, which is the output after the reference signal x a (n) and x b (n) pass through the secondary channel filter model; s(n) is the impulse response function from the actuator to the control point and represents the secondary channel model.

9. The method according to claim 8, characterized in that, In step S300, the controller can control the vibration frequencies of multiple vibration sources simultaneously; μ a , μ b The larger the value, the faster the convergence speed.

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