An electro-optical guidance method for unmanned aerial vehicles (UAVs) employing pitch and yaw error lead correction.

Abstract

This invention provides an electro-optical guidance method for unmanned aerial vehicles (UAVs) employing pitch and yaw error lead correction. It measures the pitch and yaw angles and distance information of the UAV using an infrared thermal imager and a laser rangefinder within the electro-optical guidance system, and calculates the approximate vertical and lateral deviation signals. Then, an adaptive method is used to design a parameter-adaptive differential lead corrector to solve for the lead differential signal of the pitch and yaw angles. An algebraic method is then used to solve for the algebraic differential signal of the pitch and yaw angles, and the differential difference between this signal and the lead corrector is calculated to adjust the differential and proportional-time parameters of the adaptive differential lead corrector, thereby obtaining an adaptive lead correction signal that provides damping for the guidance system. Furthermore, an adaptive method is used to adaptively estimate and compensate for angular interference, constant interference, and differential interference in the two channels, thereby improving the accuracy and anti-interference capability of guided landing.

Description

Technical Field

[0001] This invention relates to the field of UAV recovery and position control, and more specifically, to a UAV photoelectric guidance method for anti-pitch and yaw error correction. Background Technology

[0002] With the development of UAV technology, the recovery and precise landing of UAVs under photoelectric guidance have attracted increasing attention from scholars. Photoelectric guidance has the advantages of simple and accurate measurement and convenient and easy construction of guidance schemes. The general design method is to directly use angle error and distance error for negative feedback adjustment, but this method is difficult to further improve control accuracy and anti-interference ability, and the stability is poor. In the guidance process, angle information is more direct than distance information, and the differential signal capability of the angle greatly enhances the smoothness of the guidance process. Based on the above background reasons, this invention considers using an adaptive lead correction method to solve the angle differential signal, and compares it with the algebraic derivative to obtain the error to adjust the time parameter of the corrector, so as to achieve adaptive adjustment; at the same time, an adaptive method is used to estimate and compensate for interference, and finally, better stability and accuracy are obtained, so that this invention has high engineering application value.

[0003] It should be noted that the information in the background section above is only used to enhance the understanding of the background of the present invention, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] The purpose of this invention is to provide an electro-optical guidance method for unmanned aerial vehicles (UAVs) that employs pitch and yaw error advance correction, thereby overcoming the problem that the stability and accuracy of UAV guidance cannot be simultaneously achieved due to severe coupling of pitch and yaw channels.

[0005] According to one aspect of the present invention, a method for electro-optical guidance of an unmanned aerial vehicle (UAV) employing pitch and yaw error advance correction is provided, comprising the following five steps:

[0006] Step S10: The ship's electro-optical guidance system uses an infrared thermal imager to measure the UAV's pitch and yaw signals, and a laser rangefinder from the same system to measure the distance between the UAV and the landing point. Based on the distance and pitch signals, the ship's vertical approximate deviation signal is calculated. Based on the distance and azimuth signals, the ship's lateral approximate deviation signal is calculated as follows:

[0007] y a =rsin(θ1);

[0008] z a =rsin(θ2);

[0009] Where θ1 is the pitch signal of the UAV measured by the infrared thermal imager using the electro-optical guidance system, θ2 is the yaw signal of the UAV measured by the infrared thermal imager using the electro-optical guidance system, r is the distance information of the UAV from the landing point measured by the laser rangefinder of the ship's electro-optical guidance system, and y a This is the approximate vertical deviation signal for the UAV; z a This is an approximate lateral deviation signal for the UAV;

[0010] Step S20: Based on the pitch signal of the UAV, an adaptive method is used to design a parameter adaptive differential lead corrector to obtain a pitch lead corrector signal; then, based on the vertical approximate deviation signal of the UAV, a first parameter adaptive differential lead corrector is designed to obtain a vertical distance lead signal; then, based on the distance information of the UAV from the landing point, a second parameter adaptive differential lead corrector is designed to obtain a total distance lead signal; then, the vertical distance lead signal and the total distance lead signal are used to calculate the pitch algebraic differential signal; the pitch algebraic differential signal is compared with the pitch lead corrector signal to obtain a pitch lead error signal.

[0011] Step S30: Based on the pitch advance error signal, a nonlinear adaptive method is used to design the estimation law of the pitch differential time constant and the estimation law of the pitch proportional time constant of the parameter adaptive differential advance corrector. Fractional approximate integration is then performed to obtain the pitch differential time constant and the pitch proportional time constant. The pitch signal of the UAV is integrated to obtain the pitch integral signal. Based on the pitch advance error signal and the UAV's pitch signal, adaptive laws for pitch constant interference parameters, pitch interference parameters, and pitch advance interference parameters are designed. Nonlinear fractional integration is then performed to obtain the pitch constant interference parameters, pitch interference parameters, and pitch advance interference parameters. The UAV's pitch signal, pitch advance correction signal, and pitch integral signal are then superimposed to obtain the pitch sliding mode signal. Finally, the pitch sliding mode signal, its nonlinear transformation, and the pitch advance correction signal are combined to generate the desired attitude command for the UAV pitch channel, which is then sent to the UAV attitude stabilization tracking system to complete the interference-guided landing of the pitch channel.

[0012] Step S40: Based on the yaw signal of the UAV, an adaptive method is used to design a parameter adaptive differential lead corrector to obtain a yaw lead correction signal; then, based on the lateral approximate deviation signal of the UAV, a third yaw parameter adaptive differential lead corrector is designed to obtain a lateral distance lead signal; then, the lateral distance lead signal and the total distance lead signal are used to calculate the yaw algebraic differential signal; the yaw algebraic differential signal is compared with the yaw lead correction signal to obtain the yaw lead error signal.

[0013] Step S50: Based on the yaw lead error signal, a nonlinear adaptive method is used to design the estimation law of the yaw differential time constant and the estimation law of the yaw proportional time constant of the yaw parameter adaptive differential lead corrector. Fractional approximate integration is then performed to obtain the yaw differential time constant and the yaw proportional time constant. The yaw signal of the UAV is integrated to obtain the yaw integral signal. Adaptive laws for yaw constant interference parameters, yaw interference parameters, and yaw lead interference parameters are designed based on the yaw lead error signal and the UAV's yaw signal. Nonlinear fractional integration is then performed to obtain the yaw constant interference parameters, yaw interference parameters, and yaw lead interference parameters. The UAV's yaw signal, yaw lead correction signal, and yaw integral signal are then superimposed to obtain the yaw sliding mode signal. Finally, the yaw sliding mode signal, its nonlinear transformation, and the yaw lead correction signal are combined to generate the expected attitude command for the UAV yaw channel, which is then sent to the UAV attitude stabilization tracking system to complete the interference-guided landing of the yaw channel.

[0014] In one exemplary embodiment of the present invention, based on the pitch signal of the UAV, an adaptive method is used to design a parameter adaptive differential lead corrector to obtain a pitch lead corrector signal; then, based on the vertical approximate deviation signal of the UAV, a first lead differentiator is designed to obtain a vertical distance lead signal; then, based on the distance information of the UAV from the landing point, a second lead differentiator is designed to obtain a total distance lead signal; then, the vertical distance lead signal and the total distance lead signal are used to calculate the pitch algebraic differential signal; the pitch algebraic differential signal is compared with the pitch lead corrector signal to obtain a pitch lead error signal including:

[0015]

[0016] e1=θ 1ad -θ 1d ;

[0017] Where s is the differential operator of the transfer function of the parameter adaptive differential look-up compensator; T1 is the constant parameter of the parameter adaptive differential look-up compensator. For pitch differential time constant, The pitch ratio time constant is set to 0 for the initial calculation, θ 1d For pitch lead correction signal; y ad T4 is the vertical distance lead signal; T4 is the constant parameter of the first parameter, the adaptive differential lead compensator, r ad T5 is the total distance lead signal; T5 is the constant parameter of the second parameter, the adaptive differential lead compensator, and θ is the total distance lead signal. 1ad e1 is the pitch algebraic differential signal; e2 is the pitch lead error signal.

[0018] In one exemplary embodiment of the present invention, based on the pitch lead error signal, an estimation law for the pitch differential time constant and the pitch proportional time constant of the parameter adaptive differential lead corrector are designed using a nonlinear adaptive method. Then, fractional-order approximate integration is performed to obtain the pitch differential time constant and the pitch proportional time constant, respectively. The pitch signal of the UAV is integrated to obtain the pitch integral signal, which includes:

[0019]

[0020]

[0021] s1=∫θ1dt;

[0022] Where d t2 Here, k1, k2, and k3 are constant parameters used to adjust the convergence rate of the pitch differential time constant, and ε1 is a constant parameter used to adjust the convergence rate of the pitch differential time constant piecewise. t3 K is the estimation law for the pitch ratio time constant; k4, k5, and k6 are constant parameters used to adjust the convergence rate of the pitch ratio time constant. The pitch differential time constant; s1 is the pitch proportional time constant; s2 is the pitch integral signal.

[0023] In one exemplary embodiment of the present invention, an adaptive law for constant pitch interference parameters, an adaptive law for pitch interference parameters, and an adaptive law for pitch advance interference parameters are designed based on the pitch advance error signal and the pitch signal of the UAV; and nonlinear fractional integration is performed to obtain the constant pitch interference parameters, pitch interference parameters, and pitch advance interference parameters respectively; then, the UAV's pitch signal, pitch advance correction signal, and pitch integral signal are superimposed to obtain the pitch sliding mode signal; and the UAV pitch channel attitude expectation command is generated by combining the pitch sliding mode signal with its nonlinear transformation and the pitch advance correction signal, including:

[0024]

[0025]

[0026] Where a d4 For the adaptive law of pitch constant disturbance parameters, k d4 ε1 is a constant parameter used to adjust the convergence speed of the pitch constant disturbance parameter; ε2 is a constant parameter used to adjust the convergence speed of the parameter in segments; a d5 For the pitch disturbance parameter adaptive law, k d5 This is a constant parameter used to adjust the convergence speed of the pitch disturbance parameters. d6 For the pitch advance disturbance parameter adaptive law, kd6 This is a constant parameter used to adjust the convergence speed of the pitch advance disturbance parameter; For pitch constant disturbance parameters, For pitch interference parameters, For pitch advance interference parameters; s 1a For pitch sliding mode signal, k 10 , k 11 , k 12 ε1 represents constant sliding mode parameters; ε2, k7, k8, and k9 represent constant control parameters; u1 represents the desired attitude command for the UAV pitch channel.

[0027] In one exemplary embodiment of the present invention, based on the yaw signal of the UAV, an adaptive method is used to design a parameter adaptive differential lead corrector to obtain a yaw lead corrector signal; then, based on the lateral approximate deviation signal of the UAV, a third lead differentiator is designed to obtain a lateral distance lead signal; then, the lateral distance lead signal and the total distance lead signal are used to calculate the yaw algebraic differential signal; the yaw algebraic differential signal is compared with the yaw lead corrector signal to obtain a yaw lead error signal including:

[0028]

[0029] e2=θ 2ad -θ 2d ;

[0030] Where T w1 These are the constant parameters of the yaw parameter adaptive differential lead corrector. For the yaw differential time constant, The yaw proportional time constant is initially set to 0, θ 2d This is the yaw lead correction signal; z ad This is a lateral distance leading signal; T w4 For the third yaw parameter, the constant parameter of the adaptive differential lead compensator, θ 2ad e1 is the algebraic differential signal of yaw; e2 is the yaw lead error signal.

[0031] In one exemplary embodiment of the present invention, based on the yaw lead error signal, an estimation law for the yaw differential time constant and the estimation law for the yaw proportional time constant of the yaw parameter adaptive differential lead corrector are designed using a nonlinear adaptive method. Then, fractional-order approximate integration is performed to obtain the yaw differential time constant and the yaw proportional time constant, respectively. The yaw signal of the UAV is integrated to obtain the yaw integral signal, which includes:

[0032]

[0033] s2=∫θ2dt;

[0034] Where d tw2 For the estimation law of the yaw differential time constant, k w1 k w2 k w3 This is a constant parameter used to adjust the convergence rate of the yaw differential time constant and ε. w1 This is a constant parameter used to adjust the convergence rate of the yaw differential time constant in segments; d tw3 The estimation law for the yaw ratio time constant; k w4 k w5 k w6 This is a constant parameter used to adjust the convergence rate of the yaw ratio time constant; The yaw differential time constant; s1 is the yaw proportional time constant; s2 is the yaw integral signal.

[0035] In one exemplary embodiment of the present invention, adaptive laws for yaw constant interference parameters, yaw interference parameters, and yaw advance interference parameters are designed based on the yaw lead error signal and the yaw signal of the UAV; and nonlinear fractional integration is performed to obtain the yaw constant interference parameters, yaw interference parameters, and yaw advance interference parameters respectively; then, the UAV's yaw signal, yaw advance correction signal, and yaw integral signal are superimposed to obtain the yaw sliding mode signal; and the UAV's yaw channel attitude expectation command is generated by combining the yaw sliding mode signal with its nonlinear transformation and the yaw advance correction signal, including:

[0036]

[0037] Where a dw4 For the adaptive law of yaw constant disturbance parameters, k dw4 ε is a constant parameter used to adjust the convergence speed of the yaw constant disturbance parameter. w3 This is a constant parameter used to adjust the convergence speed of the parameters piecewise; a dw5 For the adaptive law of yaw interference parameters, k dw5 This is a constant parameter used to adjust the convergence speed of the yaw disturbance parameters. dw6 For the adaptive law of yaw advance disturbance parameters, k dw6 This is a constant parameter used to adjust the convergence speed of the yaw lead disturbance parameter; For yaw constant disturbance parameters, For yaw interference parameters, For yaw advance interference parameters; s 2a For yaw sliding mode signal, k w10 , k w11 , k w12 ε is a constant yaw sliding mode parameter; 2w , k w7k w8 k w9 is a constant yaw control parameter; u1 is the expected attitude command for the UAV yaw channel.

[0038] Beneficial effects

[0039] This invention discloses an electro-optical guidance method for unmanned aerial vehicles (UAVs) employing pitch and yaw error lead correction. Its main innovations are as follows: First, it proposes an adaptive approach to design an adaptive differential lead corrector. By adjusting the differential and proportional-time parameters of the adaptive differential lead corrector using the differential difference between the algebraic differential signal and the lead corrector, an angle error lead correction signal for the pitch and yaw channel is obtained, providing damping for guided landing and ensuring a smooth guidance process. Second, it employs an adaptive method to estimate and compensate for constant interference, angle interference, and differential interference in both channels, improving the accuracy and anti-interference capability of the electro-optical guidance for both channels. Therefore, this invention has high engineering practical value.

[0040] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0041] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0042] Figure 1 This is a flowchart of an electro-optical guidance method for unmanned aerial vehicles (UAVs) that employs pitch and yaw error advance correction, provided by the present invention.

[0043] Figure 2 This is the pitch signal curve (unit: degrees) of the UAV using the method provided in the embodiments of the present invention;

[0044] Figure 3 This is the azimuth signal curve (unit: degrees) of the UAV provided by the method in the embodiments of the present invention;

[0045] Figure 4 This refers to the distance information (unit: meters) between the UAV and the landing point provided by the method in this embodiment of the invention.

[0046] Figure 5 This is the approximate vertical deviation signal curve of the UAV (unit: meters) provided by the method in the embodiments of the present invention;

[0047] Figure 6This is the approximate lateral deviation signal curve of the UAV (unit: meters) provided by the method in the embodiments of the present invention;

[0048] Figure 7 This is the pitch lead error signal curve of the UAV (unitless) provided by the method in the embodiments of the present invention;

[0049] Figure 8 This is the expected attitude command curve (unitless) of the UAV pitch channel provided by the method in the embodiments of the present invention;

[0050] Figure 9 This is the yaw lead error signal curve (unitless) of the method provided in the embodiments of the present invention;

[0051] Figure 10 This is the expected attitude command curve (unitless) of the yaw channel of the UAV provided by the method in the embodiments of the present invention. Detailed Implementation

[0052] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make the invention more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a full understanding of embodiments of the invention. However, those skilled in the art will recognize that the technical solutions of the invention may be practiced with one or more of these specific details omitted, or other methods, components, apparatus, steps, etc., may be employed. In other instances, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of the invention.

[0053] This invention provides an electro-optical guidance method for unmanned aerial vehicles (UAVs) employing pitch and yaw error advance correction. It measures the UAV's pitch azimuth and distance information using an infrared thermal imager and a laser rangefinder within the electro-optical guidance system, and calculates the approximate vertical and lateral deviation signals. Then, an adaptive method is used to design a parameter-adaptive differential advance corrector to solve for the advance differential signal of the pitch and yaw angles. An algebraic method is then used to solve for the algebraic differential signal of the pitch and yaw angles, and the differential difference between this signal and the advance corrector is calculated to adjust the differential and proportional-time parameters of the adaptive differential advance corrector, thereby obtaining an adaptive advance correction signal that provides damping for the guidance system. Furthermore, an adaptive method is used to adaptively estimate and compensate for angular interference, constant interference, and differential interference in the two channels, thereby improving the accuracy and anti-interference capability of guided landing.

[0054] The following will, with reference to the accompanying drawings, further explain and illustrate an electro-optical guidance method for unmanned aerial vehicles (UAVs) employing pitch and yaw error lead correction according to the present invention. (Reference) Figure 1 As shown, this UAV electro-optical guidance method employing pitch and yaw error lead correction may include the following steps:

[0055] Step S10: The ship's electro-optical guidance system uses an infrared thermal imager to measure the UAV's pitch and yaw signals, and a laser rangefinder from the same system to measure the distance between the UAV and the landing point. Based on the distance and pitch signals, the ship's vertical approximate deviation signal is calculated. Based on the distance and azimuth signals, the ship's lateral approximate deviation signal is calculated as follows:

[0056] y a =rsin(θ1);

[0057] z a =rsin(θ2);

[0058] Where θ1 is the pitch signal of the UAV measured by the infrared thermal imager using the electro-optical guidance system, θ2 is the yaw signal of the UAV measured by the infrared thermal imager using the electro-optical guidance system, r is the distance information of the UAV from the landing point measured by the laser rangefinder of the ship's electro-optical guidance system, and y a This is the approximate vertical deviation signal for the UAV; z a This is an approximate lateral deviation signal for the UAV;

[0059] Step S20: Based on the pitch signal of the UAV, an adaptive method is used to design a parameter adaptive differential lead corrector to obtain a pitch lead corrector signal; then, based on the vertical approximate deviation signal of the UAV, a first parameter adaptive differential lead corrector is designed to obtain a vertical distance lead signal; then, based on the distance information of the UAV from the landing point, a second parameter adaptive differential lead corrector is designed to obtain a total distance lead signal; then, the vertical distance lead signal and the total distance lead signal are used to calculate the pitch algebraic differential signal; the pitch algebraic differential signal is compared with the pitch lead corrector signal to obtain a pitch lead error signal.

[0060] Specifically, this can be broken down into the following five steps. The first step is to design an adaptive differential lead corrector based on the pitch signal of the UAV, using an adaptive method, to obtain the pitch lead correction signal as follows:

[0061]

[0062] Where s is the differential operator of the transfer function of the parameter adaptive differential look-up compensator; T1 is the constant parameter of the parameter adaptive differential look-up compensator. For pitch differential time constant, The pitch ratio time constant is set to 0 for the initial calculation, θ 1d This is the pitch lead correction signal.

[0063] The second step involves designing the first lead differentiator based on the aforementioned approximate vertical deviation signal of the UAV, resulting in the following vertical distance lead signal:

[0064]

[0065] Where y ad T4 represents the vertical distance lead signal; T4 is the constant parameter of the first parameter, the adaptive differential lead compensator.

[0066] The third step involves designing a second lead differentiator based on the distance information between the UAV and the landing point, resulting in the following total distance lead signal:

[0067]

[0068] Where r ad T5 is the total distance lead signal; T5 is the constant parameter of the second parameter, the adaptive differential lead compensator.

[0069] The fourth step involves using the aforementioned vertical range lead signal and total range lead signal to calculate the pitch algebraic differential signal as follows:

[0070]

[0071] Where θ 1ad It is the pitch algebraic differential signal.

[0072] The fifth step is to compare the pitch algebraic differential signal with the pitch lead correction signal to obtain the pitch lead error signal as follows:

[0073] e1=θ 1ad -θ 1d ;

[0074] Where e1 is the pitch lead error signal.

[0075] Step S30: Based on the pitch advance error signal, a nonlinear adaptive method is used to design the estimation law of the pitch differential time constant and the estimation law of the pitch proportional time constant of the parameter adaptive differential advance corrector. Fractional approximate integration is then performed to obtain the pitch differential time constant and the pitch proportional time constant. The pitch signal of the UAV is integrated to obtain the pitch integral signal. Based on the pitch advance error signal and the UAV's pitch signal, adaptive laws for pitch constant interference parameters, pitch interference parameters, and pitch advance interference parameters are designed. Nonlinear fractional integration is then performed to obtain the pitch constant interference parameters, pitch interference parameters, and pitch advance interference parameters. The UAV's pitch signal, pitch advance correction signal, and pitch integral signal are then superimposed to obtain the pitch sliding mode signal. Finally, the pitch sliding mode signal, its nonlinear transformation, and the pitch advance correction signal are combined to generate the desired attitude command for the UAV pitch channel, which is then sent to the UAV attitude stabilization tracking system to complete the interference-guided landing of the pitch channel.

[0076] Specifically, this can be broken down into the following steps. First, based on the pitch lead error signal, the estimation laws for the pitch differential time constant and the pitch proportional time constant of the parameter adaptive differential lead corrector are designed using a nonlinear adaptive method, as follows:

[0077]

[0078] Where d t2 Here, k1, k2, and k3 are constant parameters used to adjust the convergence rate of the pitch differential time constant, and ε1 is a constant parameter used to adjust the convergence rate of the pitch differential time constant piecewise. t3 K is the estimation law for the pitch ratio time constant; k4, k5, and k6 are constant parameters used to adjust the convergence rate of the pitch ratio time constant.

[0079] The second step involves performing fractional approximate integrations based on the estimation laws of the pitch differential time constant and the pitch proportional time constant, respectively, to obtain the pitch differential time constant and the pitch proportional time constant as follows:

[0080]

[0081]

[0082] in The pitch differential time constant; is the pitch ratio time constant.

[0083] The third step is to integrate the pitch signal of the UAV to obtain the integrated pitch signal as follows:

[0084] s1=∫θ1dt;

[0085] Where s1 is the pitch integral signal.

[0086] The fourth step involves designing adaptive laws for constant pitch interference parameters, pitch interference parameters, and pitch advance interference parameters based on the pitch lead error signal and the UAV's pitch signal. Nonlinear fractional integrals are then performed to obtain the constant pitch interference parameters, pitch interference parameters, and pitch advance interference parameters as follows:

[0087]

[0088] Where a d4 For the adaptive law of pitch constant disturbance parameters, k d4 ε1 is a constant parameter used to adjust the convergence speed of the pitch constant disturbance parameter; ε2 is a constant parameter used to adjust the convergence speed of the parameter in segments; a d5 For the pitch disturbance parameter adaptive law, k d5 This is a constant parameter used to adjust the convergence speed of the pitch disturbance parameters. d6 For the pitch advance disturbance parameter adaptive law, k d6 This is a constant parameter used to adjust the convergence speed of the pitch advance disturbance parameter; For pitch constant disturbance parameters, For pitch interference parameters, These are the pitch lead interference parameters.

[0089] The fifth step involves superimposing the UAV's pitch signal, pitch advance correction signal, and pitch integral signal onto the constant pitch interference parameters, pitch interference parameters, and pitch advance interference parameters to obtain the pitch sliding mode signal. Then, the desired UAV pitch channel attitude command is generated by combining the pitch sliding mode signal with its nonlinear transformation and the pitch advance correction signal, as follows:

[0090]

[0091] Where s 1a For pitch sliding mode signal, k 10 k 11 k 12 k1 represents constant sliding mode parameters; k7, k8, and k9 represent constant control parameters; and u1 represents the desired attitude command for the UAV pitch channel.

[0092] Step S40: Based on the yaw signal of the UAV, an adaptive method is used to design a parameter adaptive differential lead corrector to obtain a yaw lead correction signal; then, based on the lateral approximate deviation signal of the UAV, a third yaw parameter adaptive differential lead corrector is designed to obtain a lateral distance lead signal; then, the lateral distance lead signal and the total distance lead signal are used to calculate the yaw algebraic differential signal; the yaw algebraic differential signal is compared with the yaw lead correction signal to obtain the yaw lead error signal.

[0093] Specifically, this can be broken down into the following four steps. The first step is to design a parameter adaptive differential lead corrector using an adaptive method based on the yaw signal of the UAV, obtaining the yaw lead correction signal as follows:

[0094]

[0095] Where T w1 These are the constant parameters of the yaw parameter adaptive differential lead corrector. For the yaw differential time constant, The yaw proportional time constant is initially set to 0, θ 2d This is a yaw lead correction signal.

[0096] The second step involves designing a third lead differentiator based on the aforementioned UAV lateral approximate deviation signal, resulting in the following lateral distance lead signal:

[0097]

[0098] Where z ad This is a lateral distance lead signal; T w4 The constant parameter for the third yaw parameter adaptive differential lead corrector.

[0099] The third step involves using the aforementioned lateral range lead signal and total range lead signal to calculate the yaw algebraic differential signal as follows:

[0100]

[0101] Where θ 2ad This is the yaw algebraic differential signal.

[0102] The fourth step is to compare the yaw algebraic derivative signal with the yaw lead correction signal to obtain the yaw lead error signal as follows:

[0103] e2=θ 2ad -θ 2d ;

[0104] Where e2 is the yaw lead error signal;

[0105] Step S50: Based on the yaw lead error signal, a nonlinear adaptive method is used to design the estimation law of the yaw differential time constant and the estimation law of the yaw proportional time constant of the yaw parameter adaptive differential lead corrector. Fractional approximate integration is then performed to obtain the yaw differential time constant and the yaw proportional time constant. The yaw signal of the UAV is integrated to obtain the yaw integral signal. Adaptive laws for yaw constant interference parameters, yaw interference parameters, and yaw lead interference parameters are designed based on the yaw lead error signal and the UAV's yaw signal. Nonlinear fractional integration is then performed to obtain the yaw constant interference parameters, yaw interference parameters, and yaw lead interference parameters. The UAV's yaw signal, yaw lead correction signal, and yaw integral signal are then superimposed to obtain the yaw sliding mode signal. Finally, the yaw sliding mode signal, its nonlinear transformation, and the yaw lead correction signal are combined to generate the expected attitude command for the UAV yaw channel, which is then sent to the UAV attitude stabilization tracking system to complete the interference-guided landing of the yaw channel.

[0106] Specifically, it can be broken down into the following five steps. The first step, based on the aforementioned yaw lead error signal, is to design the estimation laws for the yaw differential time constant and the yaw proportional time constant of the yaw parameter adaptive differential lead corrector using a nonlinear adaptive method, as follows:

[0107]

[0108] Where d tw2 For the estimation law of the yaw differential time constant, k w1 k w2 k w3 This is a constant parameter used to adjust the convergence rate of the yaw differential time constant and ε. w1 This is a constant parameter used to adjust the convergence rate of the yaw differential time constant in segments; d tw3 The estimation law for the yaw ratio time constant; k w4 k w5 k w6 This is a constant parameter used to adjust the convergence rate of the yaw ratio time constant.

[0109] The second step involves performing fractional approximate integrations based on the estimation laws for the yaw differential time constant and the yaw proportional time constant, respectively, to obtain the yaw differential time constant and the yaw proportional time constant as follows:

[0110]

[0111] in The yaw differential time constant; This is the yaw ratio time constant.

[0112] The third step is to integrate the yaw signal of the UAV to obtain the yaw integral signal as follows:

[0113] s2=∫θ2dt;

[0114] Where s2 is the yaw integral signal.

[0115] The fourth step involves designing adaptive laws for yaw constant interference parameters, yaw interference parameters, and yaw lead interference parameters based on the aforementioned yaw lead error signal and the UAV's yaw signal. Nonlinear fractional integrals are then performed to obtain the yaw constant interference parameters, yaw interference parameters, and yaw lead interference parameters as follows:

[0116]

[0117] Where a dw4 For the adaptive law of yaw constant disturbance parameters, k dw4 ε is a constant parameter used to adjust the convergence speed of the yaw constant disturbance parameter. w3 This is a constant parameter used to adjust the convergence speed of the parameters piecewise; a dw5 For the adaptive law of yaw interference parameters, k dw5 This is a constant parameter used to adjust the convergence speed of the yaw disturbance parameters. dw6 For the adaptive law of yaw advance disturbance parameters, k dw6 This is a constant parameter used to adjust the convergence speed of the yaw lead disturbance parameter; For yaw constant disturbance parameters, For yaw interference parameters, These are the parameters for yaw-lead interference.

[0118] The fifth step involves superimposing the yaw signal, yaw advance correction signal, and yaw integral signal of the UAV onto the yaw constant interference parameters, yaw interference parameters, and yaw advance interference parameters to obtain the yaw sliding mode signal. Then, the desired yaw channel attitude command for the UAV is generated by combining the yaw sliding mode signal with its nonlinear transformation and the yaw advance correction signal, as follows:

[0119]

[0120] Where s 2a For yaw sliding mode signal, k w10 , k w11 , k w12 For constant yaw sliding mode parameters; k w7 , k w8 , k w9 is a constant yaw control parameter; u1 is the expected attitude command for the UAV yaw channel.

[0121] Case Implementation and Computer Simulation Results Analysis

[0122] In step S10, the pitch signal of the UAV is measured using an infrared thermal imager of the photoelectric guidance system, such as... Figure 2 As shown; then, the infrared thermal imager of the photoelectric guidance system is used to measure the azimuth signal of the UAV, such as... Figure 3 As shown. The distance between the UAV and the landing point is measured using a laser rangefinder from the ship's electro-optical guidance system, such as... Figure 4 As shown; the approximate vertical deviation signal of the UAV is obtained through calculation, as shown below. Figure 5 As shown, the approximate lateral deviation signal of the UAV obtained through calculation is as follows: Figure 6 As shown.

[0123] In step S20, T1 = 0.05; T4 = 0.01; T5 = 0.01 are selected to obtain the pitch lead error signal as follows: Figure 7 As shown.

[0124] In step S30, k is selected. d4 =0.001, k d5 =0.001, k d6 =0.001, k 10 =6.5, k 11 =1.4, k 12 =0.8, the desired attitude command for the UAV pitch channel is obtained as follows: Figure 8 As shown.

[0125] In step S40, T is selected. w1 =0.05; the yaw lead error signal is obtained as follows: Figure 9 As shown.

[0126] In step S50, k is selected. dw4 =0.001, k dw5 =0.001, k dw6 =0.001, k w10 =6,k w11 =1.2, k w12 =0.7, the desired attitude command for the UAV yaw channel is obtained as follows: Figure 10 As shown.

[0127] Depend on Figure 2 It can be seen that the initial pitch angle is around 22 degrees; from Figure 3 It can be seen that the initial azimuth angle is around -18 degrees; from Figure 4 As can be seen, the distance decreased from 1000 meters to 180 meters, and then, due to the completion of the landing, it will slowly travel to midnight. Figure 8 and Figure 10 Two-channel attitude commands are provided, and it can be seen that the command transformations are balanced and reasonable, without sharp spikes, which facilitates tracking by the UAV system. Figure 5It can be seen that the initial height was 350 meters, from Figure 6 It can be seen that the initial lateral deviation was -300 meters, and ultimately, under the guidance of photoelectric sensors, the UAV accurately landed and was recovered from the ship in about 8 seconds. The overall experimental results show that the method provided by this invention exhibits good stability and speed, thus demonstrating its correctness and effectiveness, and possessing high engineering application and promotion value.

Claims

1. A UAV electro-optical guidance method employing pitch and yaw error lead correction, characterized in that, Includes the following steps: Step S10: The ship's electro-optical guidance system uses an infrared thermal imager to measure the UAV's pitch and yaw signals, and a laser rangefinder from the same system to measure the distance between the UAV and the landing point. Based on the distance and pitch signals, the ship's vertical approximate deviation signal is calculated. Based on the distance and azimuth signals, the ship's lateral approximate deviation signal is calculated as follows: y a =rsin(θ1); z a =rsin(θ2); Where θ1 is the pitch signal of the UAV measured by the infrared thermal imager using the electro-optical guidance system, θ2 is the yaw signal of the UAV measured by the infrared thermal imager using the electro-optical guidance system, r is the distance information of the UAV from the landing point measured by the laser rangefinder of the ship's electro-optical guidance system, and y a This is the approximate vertical deviation signal for the UAV; z a This is an approximate lateral deviation signal for the UAV; Step S20: Based on the pitch signal of the UAV, an adaptive method is used to design a parameter adaptive differential lead corrector to obtain a pitch lead corrector signal; then, based on the UAV's vertical approximate deviation signal, a first parameter adaptive differential lead corrector is designed to obtain a vertical distance lead signal; then, based on the distance information of the UAV from the landing point, a second parameter adaptive differential lead corrector is designed to obtain a total distance lead signal; then, the vertical distance lead signal and the total distance lead signal are used to calculate the pitch algebraic differential signal; the pitch algebraic differential signal is compared with the pitch lead corrector signal to obtain the pitch lead error signal as follows: e1=θ 1ad -θ 1d ; Where s is the differential operator of the transfer function of the parameter adaptive differential look-up compensator; T1 is the constant parameter of the parameter adaptive differential look-up compensator. For pitch differential time constant, The pitch ratio time constant is set to 0 for the initial calculation, θ 1d For pitch lead correction signal; y ad T4 is the vertical distance lead signal; T4 is the constant parameter of the first parameter, the adaptive differential lead compensator, r ad T5 is the total distance lead signal; T5 is the constant parameter of the second parameter, the adaptive differential lead compensator, and θ is the total distance lead signal. 1ad e1 is the pitch algebraic differential signal; e2 is the pitch lead error signal. Step S30: Based on the pitch advance error signal, a nonlinear adaptive method is used to design the estimation law of the pitch differential time constant and the estimation law of the pitch proportional time constant of the parameter adaptive differential advance corrector. Then, fractional-order approximate integration is performed to obtain the pitch differential time constant and the pitch proportional time constant. The pitch signal of the UAV is integrated to obtain the pitch integral signal. Based on the pitch advance error signal and the pitch signal of the UAV, an adaptive law of pitch constant interference parameters, an adaptive law of pitch interference parameters, and an adaptive law of pitch advance interference parameters are designed. The pitch constant interference parameters, pitch interference parameters, and pitch lead interference parameters are obtained by performing nonlinear fractional integration. Then, the UAV's pitch signal, pitch lead correction signal, and integrated pitch signal are superimposed to obtain the pitch sliding mode signal. Finally, the pitch sliding mode signal, its nonlinear transformation, and the pitch lead correction signal are combined to generate the UAV's pitch channel attitude expectation command, which is then sent to the UAV attitude stabilization tracking system to complete the pitch channel interference-guided landing as follows: Where d t2 Here, k1, k2, and k3 are constant parameters used to adjust the convergence rate of the pitch differential time constant, and ε1 is a constant parameter used to adjust the convergence rate of the pitch differential time constant piecewise. t3 K is the estimation law for the pitch ratio time constant; k4, k5, and k6 are constant parameters used to adjust the convergence rate of the pitch ratio time constant. The pitch differential time constant; s1 is the pitch proportional time constant; s2 is the pitch integral signal; a d4 For the adaptive law of pitch constant disturbance parameters, k d4 ε1 is a constant parameter used to adjust the convergence speed of the pitch constant disturbance parameter; ε2 is a constant parameter used to adjust the convergence speed of the parameter in segments; a d5 For the pitch disturbance parameter adaptive law, k d5 This is a constant parameter used to adjust the convergence speed of the pitch disturbance parameters. d6 For the pitch advance disturbance parameter adaptive law, k d6 This is a constant parameter used to adjust the convergence speed of the pitch advance disturbance parameter; For pitch constant disturbance parameters, For pitch interference parameters, For pitch advance interference parameters; s 1a For pitch sliding mode signal, k 10 k 11 k 12 k1 represents constant sliding mode parameters; k7, k8, and k9 represent constant control parameters; u1 represents the desired attitude command for the UAV pitch channel. Step S40: Based on the yaw signal of the UAV, an adaptive parameter adaptive differential lead corrector is designed using an adaptive method to obtain the yaw lead correction signal; then, based on the UAV lateral approximate deviation signal, a third yaw parameter adaptive differential lead corrector is designed to obtain the lateral distance lead signal; then, the lateral distance lead signal and the total distance lead signal are used to calculate the yaw algebraic differential signal; the yaw algebraic differential signal is compared with the yaw lead correction signal to obtain the yaw lead error signal as follows: e2=θ 2ad -θ 2d ; Where T w1 These are the constant parameters of the yaw parameter adaptive differential lead corrector. For the yaw differential time constant, The yaw proportional time constant is initially set to 0, θ 2d This is the yaw lead correction signal; z ad This is a lateral distance lead signal; T w4 For the third yaw parameter, the constant parameter of the adaptive differential lead compensator, θ 2ad e1 is the algebraic derivative of the yaw signal; e2 is the yaw lead error signal; Step S50: Based on the yaw lead error signal, a nonlinear adaptive method is used to design the estimation law of the yaw differential time constant and the estimation law of the yaw proportional time constant of the yaw parameter adaptive differential lead corrector. Then, fractional-order approximate integration is performed to obtain the yaw differential time constant and the yaw proportional time constant. The yaw signal of the UAV is integrated to obtain the yaw integral signal. Based on the yaw lead error signal and the yaw signal of the UAV, an adaptive law of yaw constant interference parameter, an adaptive law of yaw interference parameter, and an adaptive law of yaw lead interference parameter are designed. The yaw constant interference parameters, yaw interference parameters, and yaw lead interference parameters are obtained by performing nonlinear fractional integrals respectively. Then, the yaw signal, yaw lead correction signal, and yaw integral signal of the UAV are superimposed to obtain the yaw sliding mode signal. Finally, the yaw sliding mode signal is combined with its nonlinear transformation and the yaw lead correction signal to generate the expected attitude command for the UAV yaw channel, which is sent to the UAV attitude stabilization tracking system to complete the interference-guided landing of the yaw channel as follows: Where d tw2 For the estimation law of the yaw differential time constant, k w1 k w2 k w3 This is a constant parameter used to adjust the convergence rate of the yaw differential time constant and ε. w1 This is a constant parameter used to adjust the convergence rate of the yaw differential time constant in segments; d tw3 The estimation law for the yaw ratio time constant; k w4 k w5 k w6 This is a constant parameter used to adjust the convergence rate of the yaw ratio time constant; The yaw differential time constant; s1 is the yaw proportional time constant; s2 is the yaw integral signal; a dw4 For the adaptive law of yaw constant disturbance parameters, k dw4 ε is a constant parameter used to adjust the convergence speed of the yaw constant disturbance parameter. w3 This is a constant parameter used to adjust the convergence speed of the parameters piecewise; a dw5 For the adaptive law of yaw interference parameters, k dw5 This is a constant parameter used to adjust the convergence speed of the yaw disturbance parameters. dw6 For the adaptive law of yaw advance disturbance parameters, k dw6 This is a constant parameter used to adjust the convergence speed of the yaw lead disturbance parameter; For yaw constant disturbance parameters, For yaw interference parameters, For yaw advance interference parameters; s 2a For yaw sliding mode signal, k w10 k w11 k w12 For constant yaw sliding mode parameters; k w7 k w8 k w9 is the constant yaw control parameter; u2 is the expected attitude command for the UAV yaw channel.

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