A trajectory tracking control method for unmanned helicopter based on filter observer

Abstract

The present application discloses a trajectory tracking control method for an unmanned helicopter based on a filter observer, and relates to the technical field of flight control. The method establishes a disturbed unmanned aerial vehicle position system model including a lumped interference term and a measurement noise term, then constructs a state vector with the actual position and actual speed of the unmanned helicopter, converts the disturbed unmanned aerial vehicle position system model to obtain a system state equation based on the state vector, and obtains a filter observer based on a time-continuous Kalman filter design according to the system state equation, and designs a composite PID controller for the disturbed unmanned aerial vehicle position system model. The composite PID controller is combined with the filter observer to perform trajectory tracking control on the disturbed unmanned helicopter, and dynamic and real-time feedforward compensation for the lumped interference can be performed, and effective suppression of the measurement noise can be achieved, thereby improving the tracking accuracy, anti-interference performance and robustness in the unmanned helicopter control system.

Description

Technical Field

[0001] The present application relates to the field of flight control technology, and in particular to a trajectory tracking control method for an unmanned helicopter based on a filter observer. Background Art

[0002] With the continuous advancement of modern control theory, unmanned helicopters have been widely used in many fields such as military, civil and scientific research. Unmanned helicopters have shown extremely high flexibility and adaptability when performing various tasks in complex environments due to their ability to achieve fixed-point hovering, low-altitude flight and vertical take-off and landing. However, the unmanned helicopter itself is a highly nonlinear and strongly coupled system. During flight, it is easily affected by multiple sources of noise and interference such as engine vibration, transmission mechanism vibration, aerodynamic parameter perturbation, unmodeled dynamics and external interference. In addition, when using sensors to obtain the position information of unmanned helicopters, it is inevitably affected by sensor measurement noise. In this context, how to effectively filter out the influence of high-frequency noise in the measurement information and how to suppress the influence of multiple sources of interference to achieve high-precision trajectory tracking has become a very challenging problem faced in the trajectory tracking process of unmanned helicopters.

[0003] In response to the anti-interference control problem of unmanned helicopters, domestic and foreign scholars have proposed a variety of anti-interference control strategies, including robust control strategies that passively eliminate interference based on system robustness and active anti-interference control strategies that rely on extended state observers to observe and feedforward compensate interference in real time. However, the anti-interference performance of existing robust control strategies is obtained at the expense of the nominal performance of the system, and the active anti-interference control strategy based on the extended state observer will significantly amplify the adverse effects of measurement noise when there is measurement noise in the system. Summary of the invention

[0004] In view of the above problems and technical requirements, this application proposes a trajectory tracking control method for an unmanned helicopter based on a filter observer. The technical solution of this application is as follows:

[0005] A unmanned helicopter trajectory tracking control method based on a filter observer, the unmanned helicopter trajectory tracking control method comprising:

[0006] Establish a disturbed UAV position system model, which includes the position dynamic equation of the unmanned helicopter under the control quantity and the position measurement equation of the position measurement sensor in the unmanned helicopter. The position dynamic equation contains a lumped interference term used to characterize the external interference to the unmanned helicopter, and the position measurement equation contains a measurement noise term used to characterize the measurement error interference to the position measurement sensor.

[0007] The state vector is constructed based on the actual position and actual speed of the unmanned helicopter, the disturbed UAV position system model is converted to obtain the system state equation based on the state vector, and the filter observer is designed based on the time continuous Kalman filter according to the system state equation;

[0008] Design a composite PID controller for the disturbed UAV position system model;

[0009] When performing trajectory tracking control on an unmanned helicopter, the control quantity of the unmanned helicopter and the position measurement value of the position measurement sensor are input into the filter observer to obtain the actual position estimation value and the lumped interference term estimation value, the position error between the actual position estimation value and the three-dimensional reference trajectory and the lumped interference term estimation value are input into the composite PID controller, and the unmanned helicopter is controlled according to the command value of the control quantity output by the composite PID controller.

[0010] The beneficial technical effects of this application are:

[0011] The present application discloses a trajectory tracking control method for an unmanned helicopter based on a filter observer. The method feeds back the lumped disturbance estimation information to the design of a composite PID controller, and significantly improves the anti-interference performance and robustness of the system by performing dynamic and real-time feedforward compensation for the lumped disturbance. In addition, while obtaining the dynamic position tracking error and lumped disturbance estimation information, the measurement noise is effectively suppressed. The method can not only significantly improve the tracking accuracy in the unmanned helicopter control system, but the proposed control method can also be extended and applied to the high-precision control of other aircraft, and has a very broad application prospect. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a control block diagram of an unmanned helicopter trajectory tracking control method according to an embodiment of the present application.

[0013] Figure 2 It is a comparison chart of tracking trajectories of two control methods in the simulation example.

[0014] Figure 3 It is a comparison chart of the three-channel tracking error response curves of the two control methods in the simulation example.

[0015] Figure 4 It is a comparison chart of the three-channel MAE response curves of the two control methods in the simulation example.

[0016] Figure 5 It is a comparison chart of the virtual control quantity response curves of the two control methods in the simulation example.

[0017] Figure 6 It is a comparison chart of the virtual control quantity change rate response curves of the two control methods in the simulation example.

[0018] Figure 7 It is a comparison diagram of the estimated errors of the three-channel lumped disturbance terms of the two control methods in the simulation example. DETAILED DESCRIPTION

[0019] The specific implementation of the present application is further described below in conjunction with the accompanying drawings.

[0020] This application discloses a method for tracking the trajectory of an unmanned helicopter based on a filter observer. Figure 1 The control block diagram shown in the figure, the unmanned helicopter trajectory tracking control method includes the following contents:

[0021] Step 1: Establish a disturbed UAV position system model. The disturbed UAV position system model includes the position dynamic equation of the unmanned helicopter under the control quantity and the position measurement equation of the position measurement sensor in the unmanned helicopter.

[0022] The position dynamic equation indicates the position change of the unmanned helicopter. Since the unmanned helicopter will be affected by external interference, the position dynamic equation contains a lumped interference term used to characterize the external interference to the unmanned helicopter. The established position dynamic equation of the unmanned helicopter is:

[0023]

[0024] Among them, (x, y, z) is the actual position of the unmanned helicopter in the three-axis direction of the inertial coordinate system, (D x , D y , D z ) is the lumped interference term in the three-axis direction of the inertial coordinate system.

[0025] The first derivative of the actual position in the x-axis direction of the inertial coordinate system is the actual velocity in the x-axis direction of the inertial coordinate system. It represents the second-order derivative of the actual position in the x-axis direction of the inertial coordinate system. The first-order derivative and second-order derivative of other parameters are expressed in the same way and will not be described in detail later.

[0026] m is the mass of the unmanned helicopter, g is the gravitational acceleration constant, k d is the velocity damping coefficient, which are known parameters. In one embodiment, m = 6 kg, g = 9.8 m / s 2 , k d =0.09.

[0027] The control quantity of the unmanned helicopter includes the main rotor lift T m , roll angle φ, pitch angle θ and heading angle ψ.

[0028] In order to simplify the description and design, a virtual control variable a is introduced. x 、ay 、a z for:

[0029]

[0030] Then the position dynamic equation based on the control amount shown in the above formula (1) can be rewritten as the position dynamic equation based on the virtual control amount as follows:

[0031]

[0032] The position measurement equation indicates the relationship between the position measurement value obtained by the position measurement sensor in the unmanned helicopter and the actual position. Since the measurement sensor is disturbed by the measurement error, the position measurement equation contains a measurement noise term used to characterize the measurement error interference of the position measurement sensor. The position measurement equation of the position measurement sensor in the unmanned helicopter is established as:

[0033]

[0034] Among them, (x m ,y m , z m ) is the position measurement value of the inertial coordinate system in the three-axis direction obtained by the position measurement sensor in the unmanned helicopter. (v mx , v my , v mz ) is the measurement noise term of the position measurement sensor in the unmanned helicopter in the three-axis direction of the inertial coordinate system. The measurement noise term is usually Gaussian white noise, satisfying v mx ~(0,R x ),v my ~(0,R y ) and v mz ~(0,R z ), where R x is the measurement noise term v mx The covariance of y is the measurement noise term v my The covariance of z is the measurement noise term v mz In one embodiment, R x =R x =R x =0.04.

[0035] Step 2: Use the actual position (x, y, z) and actual speed of the unmanned helicopter The state vector is constructed, and the disturbed UAV position system model is converted into a system state equation based on the state vector. According to the system state equation, a filter observer is designed based on a time-continuous Kalman filter.

[0036] In this step, when designing the filter observer, three-channel filter observers are designed for the three-axis directions of the inertial coordinate system, including:

[0037] (1) For the x-axis direction of the inertial coordinate system

[0038] First, construct the state vector X=[xv x ] T and the derivative of the state vector It represents the actual speed of the unmanned helicopter in the x-axis direction of the inertial coordinate system. It represents the actual acceleration of the unmanned helicopter in the x-axis direction of the inertial coordinate system.

[0039] Then, according to the disturbed UAV position system model (3) and (4), the system state equation in the x-axis direction of the inertial coordinate system is obtained as follows:

[0040]

[0041] in,

[0042] Then, based on the system state equation (5), the filter observer in the x-axis direction of the inertial coordinate system is designed to observe the actual position estimate and the lumped interference estimate in the x-axis direction. The filter observer in the x-axis direction of the inertial coordinate system is designed as:

[0043]

[0044] Among them, K x ∈R 2×1 is the Kalman gain, P x ∈R 2×2 is the estimated covariance matrix of the state vector X. is the estimated value of the system state in the x-axis direction obtained by the Kalman filter, It is an estimate The first derivative of Indicates the position estimate obtained by the Kalman filter in the x-axis direction. Indicates the velocity estimate obtained by the Kalman filter in the x-axis direction. It represents the actual position estimate of the unmanned helicopter in the x-axis direction of the inertial coordinate system. It represents the actual velocity estimate of the unmanned helicopter in the x-axis direction of the inertial coordinate system, that is, the estimated value of the first-order derivative of the actual position. It represents the estimated value of the lumped interference term in the x-axis direction of the inertial coordinate system. x ∈R 2×2 is the estimated aggregate interference term The covariance matrix of The estimated values ​​of other parameters are also expressed in this form, which will not be described in detail later.

[0045] z x1 、z x2 、z x3 is the intermediate variable of the filter observer. is a positive constant parameter of the filter observer, and its value satisfies is Hurwitz's, and s is a complex frequency domain parameter. In one embodiment,

[0046] (2) For the y-axis direction of the inertial coordinate system

[0047] Construct the state vector Y=[yv y ] T , the derivative of the state vector It represents the actual speed of the unmanned helicopter in the y-axis direction of the inertial coordinate system. It represents the actual acceleration of the unmanned helicopter in the y-axis direction of the inertial coordinate system.

[0048] Then, according to the disturbed UAV position system model (3) and (4), the system state equation in the y-axis direction of the inertial coordinate system is obtained as follows:

[0049]

[0050] in,

[0051] According to the system state equation (8), the filter observer in the y-axis direction of the inertial coordinate system is designed to observe the actual position estimation value and the lumped interference term estimation value in the y-axis direction. The designed filter observer in the y-axis direction of the inertial coordinate system is:

[0052]

[0053] Among them, K y ∈R 2×1 is the Kalman gain, P y ∈R 2×2 is the estimated covariance matrix of the state vector Y, is the estimated value of the system state in the y-axis direction obtained by the Kalman filter, It is an estimate The first derivative of is the estimate of the actual position obtained by the Kalman filter in the y-axis direction, is the velocity estimate obtained by the Kalman filter in the y-axis direction. It represents the actual position estimate of the unmanned helicopter in the y-axis direction of the inertial coordinate system. It represents the final actual speed estimate of the unmanned helicopter in the y-axis direction of the inertial coordinate system. It represents the estimated value of the lumped interference term in the y-axis direction of the inertial coordinate system. y ∈R 2×2 is the estimated aggregate interference term The covariance matrix of, in one embodiment,

[0054] z y1 、z y2 、z y3 is the intermediate variable of the filter observer. is a positive constant parameter of the filter observer, and its value satisfies is Hurwitz. In one embodiment,

[0055] (3) For the z-axis direction of the inertial coordinate system

[0056] Construct the state vector Z of the unmanned helicopter in the z-axis direction of the inertial coordinate system = [zv z ] T , the derivative of the state vector It represents the actual speed of the unmanned helicopter in the z-axis direction of the inertial coordinate system. It represents the actual acceleration of the unmanned helicopter in the z-axis direction of the inertial coordinate system.

[0057] Then, according to the disturbed UAV position system model (3) and (4), the system state equation in the z-axis direction of the inertial coordinate system is obtained as follows:

[0058]

[0059] in,

[0060] According to the system state equation (11), the filter observer in the z-axis direction of the inertial coordinate system is designed to observe the actual position estimate and the lumped interference estimate in the z-axis direction. The designed filter observer in the z-axis direction of the inertial coordinate system is:

[0061]

[0062] Among them, K z ∈R 2×1 is the Kalman gain, P z ∈R 2×2is the estimated covariance matrix of the state vector Z, is the estimated value of the system state in the z-axis direction obtained by the Kalman filter, It is an estimate The first derivative of is the position estimate obtained by the Kalman filter in the z-axis direction, is the velocity estimate obtained by the Kalman filter in the z-axis direction. It represents the actual position estimate of the unmanned helicopter in the z-axis direction of the inertial coordinate system. It represents the actual estimated speed of the unmanned helicopter in the z-axis direction of the inertial coordinate system. It represents the estimated value of the lumped interference term in the z-axis direction of the inertial coordinate system. z ∈R 2×2 is the estimated aggregate interference term The covariance matrix of, in one embodiment,

[0063] z z1 、z z2 、z z3 is the intermediate variable of the filter observer. is a positive constant parameter of the filter observer, and its value satisfies is Hurwitz. In one embodiment,

[0064] Step 3: Design a composite PID controller for the disturbed UAV position system model. Similarly, the three-channel composite PID controllers designed for the three-axis directions of the inertial coordinate system are:

[0065]

[0066] Among them, (x r ,y r ,z r ) is the position instruction indicated by the three-dimensional reference trajectory that the unmanned helicopter is to follow. is the estimated value of the actual position in the three-axis direction of the inertial coordinate system, It is the estimated value of the lumped interference term in the three-axis direction of the inertial coordinate system.

[0067] K px , K ix , K dx It is the proportional coefficient, integral coefficient and differential coefficient in the x-axis direction of the inertial coordinate system. py , K iy , K dy It is the proportional coefficient, integral coefficient and differential coefficient in the y-axis direction of the inertial coordinate system. pz , Kiz , K dz It is the proportional coefficient, integral coefficient and differential coefficient in the z-axis direction of the inertial coordinate system. K in any direction j = x, y, z pj , K ij , K dj are positive numbers and satisfy the polynomial P j (s)=s 3 +K dj s 2 +K pj s+K ij = 0 is Hurwitz's. In one embodiment, K px =12, K py =K pz =18.75, K ix =8, K iy =K iz =15.625, K dx =6, K dy =K dz =7.5.

[0068] Step 4: When performing trajectory tracking control on the unmanned helicopter, the current control value of the unmanned helicopter and the position measurement value (x m ,y m , z m ) Input the filter observer to get the actual position estimate and the lumped interference term estimate

[0069] Then the actual position estimate Compared with the three-dimensional reference trajectory (x r ,y r ,z r ) and the estimated value of the aggregate interference term A composite PID controller (14) is input, and the unmanned helicopter is controlled according to the command value of the control quantity output by the composite PID controller.

[0070] In the actual unmanned helicopter flight process, in order to facilitate system control, it is always hoped that the heading angle of the unmanned helicopter is kept at zero. Since this application mainly involves position control, the command value ψ of the heading angle ψ can be d Keep setting it to 0, that is, fix ψ = 0 and substitute it into equation (14), then the main rotor lift T can be obtained by inverse solution m The command value Roll angle φ command value φ d , the pitch angle θ command value θ d for:

[0071]

[0072] In order to illustrate the effect of the unmanned helicopter trajectory tracking control method of the present application, a model of an unmanned helicopter is built by using the Simulink toolbox in the simulation software MATLAB R2020b for simulation verification. In addition to the various parameter settings provided in the above embodiments, other parameters in the simulation scenario are set as follows, taking into full consideration the system lumped interference and measurement noise:

[0073] In this simulation example, m=6kg, g=9.8m / s 2 , k d =0.09, and set the initial state of the unmanned helicopter to:

[0074]

[0075] The position instruction of the three-dimensional reference trajectory that the unmanned helicopter is to follow at any time t is:

[0076]

[0077] The lumped interference term is set as:

[0078]

[0079] In order to make the simulation results closer to the actual situation, during the control process, the range of the virtual control quantity and its change rate in each channel direction is restricted as follows:

[0080]

[0081] In the same simulation scenario, the unmanned helicopter trajectory tracking control is performed using the traditional method of using a composite PID controller combined with an extended state observer (denoted as PID+ESO), and the method of using the composite PID controller of the present application combined with a filter observer (denoted as PID+KFESO), and then the two control methods are compared and verified.

[0082] The three-dimensional space trajectory tracking response curves of the unmanned helicopter under the two control methods are shown in the figure below: Figure 2 As shown, the three-channel tracking error response curve is as follows Figure 3 As shown. Figure 3It can be seen that the steady-state tracking accuracy of the PID+KFESO control method of the present application is higher than that of the traditional PID+ESO method. In order to more intuitively demonstrate the steady-state tracking accuracy of the two control methods, the mean absolute value of steady-state tracking error (MAE) is introduced to quantify the steady-state tracking accuracy. When the system reaches a steady state (t>30s), the three-channel MAE response curves of trajectory tracking under the two control methods are as follows: Figure 4 The statistical comparison results are shown in the following table:

[0083] x-axis MAE(m) y-axis MAE(m) MAE in z axis (m) PID+ESO 0.0336 0.0346 0.0422 PID+KFESO 0.0397 0.0295 0.0373

[0084] According to the above table data and Figure 4 It can be clearly seen that the steady-state tracking accuracy of the control method of the present application is better than that of the traditional PID+ESO method.

[0085] The response curves of the virtual control quantity of the unmanned helicopter under the two control methods are as follows: Figure 5 As shown, from Figure 5 It can be seen that the control quantity smoothness of the PID+KFESO control method of the present application is better than that of the traditional PID+ESO method, which means that the method of the present application can reduce the frequency of the actuator's reciprocating motion and is more in line with engineering practice. In order to more intuitively demonstrate the control quantity smoothness of the two control methods, the mean absolute value of acceleration change rate (MAR) is introduced to quantify the control quantity smoothness. The three-channel MAR response curves of the control quantity change rate under the two control methods are shown in Figure 2. Figure 6 The statistical comparison results are shown in the following table:

[0086] x-axis MAR y-axis MAR z-axis MAR PID+ESO 43.5975 44.5635 45.4097 PID+KFESO 5.4346 6.6564 7.2974

[0087] According to the above table data and Figure 6 It can be clearly seen that the control method of the present application has better control quantity smoothness than the traditional PID+ESO method.

[0088] The estimated error response curves of the two control methods to the lumped disturbance term are as follows: Figure 7 As shown, from Figure 7 It can be seen from the estimation error response curve that the control method of the present application improves the estimation accuracy of the lumped interference term compared with the traditional PID+ESO method.

[0089] The above is only a preferred embodiment of the present application, and the present application is not limited to the above embodiments. It is understood that other improvements and changes directly derived or associated by those skilled in the art without departing from the spirit and concept of the present application should be considered to be included in the protection scope of the present application.

Claims

1. A trajectory tracking control method for an unmanned helicopter based on a filter observer, characterized in that: The unmanned helicopter trajectory tracking control method comprises: Establishing a disturbed UAV position system model, the disturbed UAV position system model includes a position dynamic equation of the unmanned helicopter under the action of a control variable and a position measurement equation of a position measurement sensor in the unmanned helicopter, the position dynamic equation includes a lumped interference term for characterizing external interference to the unmanned helicopter, and the position measurement equation includes a measurement noise term for characterizing measurement error interference to the position measurement sensor; A state vector is constructed based on the actual position and actual speed of the unmanned helicopter, the disturbed unmanned aircraft position system model is converted to obtain a system state equation based on the state vector, and a filter observer is obtained based on a time-continuous Kalman filter design according to the system state equation; Design a composite PID controller for the disturbed UAV position system model; When performing trajectory tracking control on the unmanned helicopter, the control amount of the unmanned helicopter and the position measurement value of the position measurement sensor are input into the filter observer to obtain an actual position estimation value and a lumped interference term estimation value, the position error between the actual position estimation value and the three-dimensional reference trajectory and the lumped interference term estimation value are input into the composite PID controller, and the unmanned helicopter is controlled according to the command value of the control amount output by the composite PID controller; The establishment of the disturbed UAV position system model includes: The position dynamic equation of the unmanned helicopter is established as: The position measurement equation of the position measurement sensor in the unmanned helicopter is established as: Among them, (x, y, z) is the actual position of the unmanned helicopter in the three-axis direction of the inertial coordinate system, (x m ,y m , z m ) is the position measurement value of the inertial coordinate system in the three-axis direction obtained by the position measurement sensor in the unmanned helicopter, (v mx , v my , v mz ) is the measurement noise term of the position measurement sensor in the unmanned helicopter in the three-axis direction of the inertial coordinate system; (D x , D y , D z ) is the lumped interference term in the three-axis direction of the inertial coordinate system; The control quantity of the unmanned helicopter includes the main rotor lift T m , roll angle φ, pitch angle θ and heading angle ψ; m is the mass of the unmanned helicopter, g is the gravitational acceleration constant, k d is the velocity damping coefficient; The composite PID controller designed for the disturbed UAV position system model is: Among them, (x r ,y r ,z r ) is the position instruction indicated by the three-dimensional reference trajectory, is the estimated value of the actual position in the three-axis direction of the inertial coordinate system, is the estimated value of the lumped interference term in the three-axis direction of the inertial coordinate system; K px , K ix , K dx is the proportional coefficient, integral coefficient and differential coefficient in the x-axis direction of the inertial coordinate system, K py , K iy , K dy is the proportional coefficient, integral coefficient and differential coefficient in the y-axis direction of the inertial coordinate system, K pz , K iz , K dz It is the proportional coefficient, integral coefficient and differential coefficient in the z-axis direction of the inertial coordinate system. K in any direction j = x, y, z pj , K ij , K dj They are all positive numbers and satisfy the polynomial Pj(s)=s 3 +K dj s 2 +K pj s+K ij = 0 is Hurwitz, s is the complex frequency domain parameter.

2. The unmanned helicopter trajectory tracking control method according to claim 1, characterized in that: The designed filter observer includes: Construct the state vector X of the unmanned helicopter in the x-axis direction of the inertial coordinate system = [xv x ] T , the derivative of the state vector According to the disturbed UAV position system model, the system state equation in the x-axis direction of the inertial coordinate system is obtained as follows: A filter observer in the x-axis direction of the inertial coordinate system is designed to observe the actual position estimate and the lumped interference term estimate in the x-axis direction; wherein, Virtual control volume Indicates the actual speed of the unmanned helicopter in the x-axis direction of the inertial coordinate system; Construct the state vector Y=[yv y ] T , the derivative of the state vector According to the disturbed UAV position system model, the system state equation in the y-axis direction of the inertial coordinate system is obtained as follows: A filter observer in the y-axis direction of the inertial coordinate system is designed to observe the actual position estimate and the lumped interference estimate in the y-axis direction; wherein, Virtual control volume Indicates the actual speed of the unmanned helicopter in the y-axis direction of the inertial coordinate system; Construct the state vector Z of the unmanned helicopter in the z-axis direction of the inertial coordinate system = [zv z ] T , the derivative of the state vector According to the disturbed UAV position system model, the system state equation in the z-axis direction of the inertial coordinate system is obtained as follows: A filter observer in the z-axis direction of the inertial coordinate system is designed to observe the actual position estimate and the lumped interference estimate in the z-axis direction; wherein, Virtual control volume It indicates the actual speed of the unmanned helicopter in the z-axis direction of the inertial coordinate system.

3. The unmanned helicopter trajectory tracking control method according to claim 2, characterized in that: The filter observer designed in the x-axis direction of the inertial coordinate system is: Among them, K x ∈R 2×1 is the Kalman gain, P x ∈R 2×2 is the estimated covariance matrix of the state vector X, is the estimated value of the system state in the x-axis direction obtained by the Kalman filter, It is an estimate The first derivative of Indicates the position estimate obtained by the Kalman filter in the x-axis direction. Indicates the velocity estimate obtained by the Kalman filter in the x-axis direction; It represents the estimated actual position of the unmanned helicopter in the x-axis direction of the inertial coordinate system. It represents the estimated actual speed of the unmanned helicopter in the x-axis direction of the inertial coordinate system. represents the estimated value of the lumped interference term in the x-axis direction of the inertial coordinate system; Q x ∈R 2×2 is the estimated aggregate interference term The covariance matrix of x1 、z x2 、z x3 is the intermediate variable of the filter observer; is a positive constant parameter of the filter observer, and its value satisfies is Hurwitz, s is a complex frequency domain parameter; the measurement noise term v mx is Gaussian white noise, R x is the measurement noise term v mx The covariance of .

4. The unmanned helicopter trajectory tracking control method according to claim 2, characterized in that: The designed filter observer in the y-axis direction of the inertial coordinate system is: Among them, K y ∈R 2×1 is the Kalman gain, P y ∈R 2×2 is the estimated covariance matrix of the state vector Y, is the estimated value of the system state in the y-axis direction obtained by the Kalman filter, It is an estimate The first derivative of is the position estimate obtained by the Kalman filter in the y-axis direction, is the velocity estimate obtained by the Kalman filter in the y-axis direction; It represents the estimated actual position of the unmanned helicopter in the y-axis direction of the inertial coordinate system. It represents the estimated actual speed of the unmanned helicopter in the y-axis direction of the inertial coordinate system. represents the estimated value of the lumped interference term in the y-axis direction of the inertial coordinate system; Q y ∈R 2×2 is the estimated aggregate interference term The covariance matrix of y1 、z y2 、z y3 is the intermediate variable of the filter observer; is a positive constant parameter of the filter observer, and its value satisfies is Hurwitz, s is a complex frequency domain parameter; the measurement noise term v my is Gaussian white noise, R y is the measurement noise term v my The covariance of .

5. The unmanned helicopter trajectory tracking control method according to claim 2, characterized in that: The designed filter observer in the z-axis direction of the inertial coordinate system is: Among them, K z ∈R 2×1 is the Kalman gain, P z ∈R 2×2 is the estimated covariance matrix of the state vector Z, is the estimated value of the system state in the z-axis direction obtained by the Kalman filter, It is an estimate The first derivative of is the position estimate obtained by the Kalman filter in the z-axis direction, is the velocity estimate obtained by the Kalman filter in the z-axis direction; It represents the estimated actual position of the unmanned helicopter in the z-axis direction of the inertial coordinate system. It represents the estimated actual speed of the unmanned helicopter in the z-axis direction of the inertial coordinate system. represents the estimated value of the lumped interference term in the z-axis direction of the inertial coordinate system; Q z ∈R 2×2 is the estimated aggregate interference term The covariance matrix of z1 、z z2 、z z3 is the intermediate variable of the filter observer; is a positive constant parameter of the filter observer, and its value satisfies is Hurwitz, s is a complex frequency domain parameter; the measurement noise term v mz is Gaussian white noise, R z is the measurement noise term v mz The covariance of .

6. The unmanned helicopter trajectory tracking control method according to claim 1, characterized in that: Obtaining the command value of the control amount of the unmanned helicopter includes: While maintaining the command value of the heading angle ψ d =0, the designed filter observer is combined with the composite PID controller to obtain the command value of the main rotor lift. Roll angle command value φ d , the pitch angle command value θ d for: in,

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