Self-adaptive anti-interference control method for suppressing vibration of wings of unmanned aerial vehicle

Abstract

The invention discloses a self-adaptive anti-interference control method for suppressing vibration of wings of an unmanned aerial vehicle. The method comprises the following steps: attaching an MFC piezoelectric patch to the root of the upper surface of the wing of the unmanned aerial vehicle as a brake, and attaching an MFC piezoelectric patch to the lower surface of the wing of the unmanned aerial vehicle as a sensor; according to the structural characteristics of the flexible wing, obtaining a dynamic model of the flexible wing equivalent to a variable cross-section cantilever beam; a voltage signal generated by the MFC piezoelectric sensor is used as an input and feedback signal, and a control system is established; a voltage signal is calculated by the control system and serves as an output and brake signal, and wing vibration is restrained. According to the self-adaptive anti-interference control method for inhibiting the vibration of the wings of the unmanned aerial vehicle, unknown disturbance suffered by the wings can be quickly and accurately predicted, disturbance compensation is carried out through a control algorithm, the vibration of the wings can be effectively inhibited, and help is provided for improving the flight stability and cruising ability of the unmanned aerial vehicle.

Description

Technical Field

[0001] The present invention relates to the field of aviation and automation control, and in particular to an adaptive anti-disturbance control method for suppressing the vibration of a drone wing. Background Art

[0002] With the rapid development of UAV technology, UAVs are increasingly used in military, civilian, scientific research and other fields. However, in actual flight, the flexible wings of fixed-wing UAVs are prone to unstable vibrations due to their lightweight design and high flexibility when subjected to unknown disturbances such as complex external weather. This vibration will not only significantly affect flight performance and flight safety, but may also cause structural fatigue and even wing damage. Therefore, how to effectively suppress the vibration of flexible wings and compensate for the impact of unknown disturbances has become an important technical problem in the field of UAV design and control. Summary of the invention

[0003] In view of the above-mentioned defects of the prior art, the technical problem to be solved by the present invention is that most of the existing traditional control methods only focus on feedback and lack consideration of unknown disturbances; although some existing anti-disturbance methods take into account the influence of disturbances, they rely on disturbance characteristic information to a certain extent. The present invention provides an adaptive anti-disturbance control method for suppressing the vibration of UAV wings, which can quickly and accurately predict the unknown disturbances to the wings, and perform disturbance compensation through control algorithms, which can effectively suppress the vibration of the wings, and can effectively suppress the vibration deformation that is prone to occur in fixed-wing lightweight UAVs under meteorological interference, and provide help to improve the flight stability and endurance of UAVs.

[0004] To achieve the above object, the present invention provides an adaptive anti-disturbance control method for suppressing the vibration of the wing of a UAV, comprising the following steps:

[0005] An MFC piezoelectric sheet is attached to the root of the upper surface of the drone's wing as a brake, and an MFC piezoelectric sheet is attached to the lower surface as a sensor;

[0006] According to the structural characteristics of the flexible wing, the dynamic model of the flexible wing is obtained by equivalently treating it as a cantilever beam with a variable cross-section.

[0007] The control system is established by using the voltage signal generated by the MFC piezoelectric sensor as the input and feedback signal;

[0008] The voltage signal is calculated by the control system and used as the output and braking signal to suppress the wing vibration.

[0009] Furthermore, it also includes establishing a state observer to predict various state variables, that is, various order modal displacements, for calculating feedback gains in the control law.

[0010] Furthermore, it also includes establishing a Lumberg state observer, selecting a reasonable bandwidth to obtain the corresponding observer gain, and according to the estimated error between the established model output and the true value measured by the sensor, combined with the obtained gain, quickly and dynamically adjusting the estimated state variable to make it approach the true state variable. The predicted state variables, that is, the modal displacements of each order, are used to calculate the control feedback.

[0011] Furthermore, it also includes establishing a disturbance observer for estimating future disturbances in real time and compensating the wing through the adaptive feedforward part of the control law.

[0012] Furthermore, a disturbance observer is designed based on radial basis functions, and the approximation ability of radial basis functions is used to estimate unknown complex disturbances.

[0013] Furthermore, an adaptive law of weight values ​​is designed to cope with unknown disturbances that change in real time.

[0014] Furthermore, after the estimated disturbance variables and state variables are obtained through the observer, they are fed back to the control system to offset the disturbance and stabilize the UAV wing system.

[0015] Technical Effects

[0016] The present invention provides an adaptive anti-disturbance control method for suppressing the vibration of the wing of a drone. The method includes two parts: feedforward control and feedback control. According to the designed adaptive radial basis function (RBF) disturbance observer, the unknown disturbance to the wing can be predicted quickly and accurately, and the disturbance compensation is performed through the control algorithm, which can effectively suppress the vibration of the wing. The large deformation nonlinearity of the wing is also considered in the model, so that the designed control algorithm can also have a better effect for more extreme and complex disturbances, which helps to improve the flight stability and endurance of the drone. This method can effectively suppress vibration without the need for information such as the disturbance position and frequency, and also takes into account the nonlinear large deformation situation, and can cope with more extreme and complex disturbances.

[0017] The concept, specific structure and technical effects of the present invention will be further described below in conjunction with the accompanying drawings to fully understand the purpose, characteristics and effects of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of a piezoelectric intelligent wing of an adaptive anti-disturbance control method for suppressing the vibration of a drone wing according to a preferred embodiment of the present invention;

[0019] Figure 2 It is a schematic diagram of an adaptive anti-disturbance control system of an adaptive anti-disturbance control method for suppressing wing vibration of a UAV according to a preferred embodiment of the present invention;

[0020] Figure 3 It is a schematic diagram of sinusoidal disturbance prediction of an adaptive anti-disturbance control method for suppressing wing vibration of a drone in a preferred embodiment of the present invention;

[0021] Figure 4 It is a schematic diagram of multi-frequency disturbance prediction of an adaptive anti-disturbance control method for suppressing UAV wing vibration according to a preferred embodiment of the present invention;

[0022] Figure 5 It is a random disturbance suppression effect of an adaptive anti-disturbance control method for suppressing UAV wing vibration in a preferred embodiment of the present invention;

[0023] Figure 6 The invention discloses a random disturbance suppression effect of an adaptive anti-disturbance control method for suppressing the vibration of the wing of a UAV according to a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0025] In the following description, specific details such as specific internal procedures and techniques are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0026] like Figure 1 As shown, an MFC piezoelectric sheet is attached to the root of the upper surface of the wing as a brake, and an MFC piezoelectric sheet is attached to the lower surface as a sensor. Among them, the shape parameter: L is the length of the wing.

[0027] According to the structural characteristics of the flexible wing, it can be equivalent to a cantilever beam with a variable cross-section to obtain its dynamic equation. The obtained dynamic equation is the basis of the entire algorithm and is used to derive the state space equation required for control. In this embodiment, it is a fixed-wing UAV with a lightweight flexible wing, which is made of a lightweight alloy as a skeleton and covered with a skin on the outside. The overall structure can be simplified as a cantilever beam with a variable cross-section. According to the Euler Bernoulli beam theory and the Galerkin method, the dynamic model of the flexible wing is obtained:

[0028]

[0029] Where ρ is the mass matrix, A s is the cross-sectional area, Is is the section inertia moment, Y is Young's modulus, φ is the shape function, q is the modal displacement, N is the modal order, S is the number of discrete directions of the wing extension, l s and l s+1 are the starting and ending lengths of the discrete part, and x is the coordinate in the length direction of the wing.

[0030] For use in control systems, the model is further expressed as:

[0031]

[0032] Where M is the mass matrix, C is the damping matrix, K is the stiffness matrix, f(t) is the unknown external force disturbance, and F a (x, t) is the control force applied by the brake piezoelectric patch.

[0033] The MFC piezoelectric sensor will generate a voltage signal Vs as input and feedback signal under stress, and a signal acquisition card is used to obtain these signals. The electrical signal is converted into a real-time vibration displacement signal through the conversion equation, and the displacement is introduced into the control algorithm. The corresponding control output (including feedforward and feedback) is obtained according to the designed control law. Finally, the control output is converted into a voltage signal and applied to the MFC piezoelectric brake through a voltage amplifier. The control system adopts adaptive anti-disturbance control, and the voltage signal V calculated by the control system a As output and braking signal, wing vibration suppression is achieved.

[0034] According to the wing dynamics model, the state space required for its control is obtained:

[0035]

[0036] y(t)=Cx(t)

[0037] in, q(t) is the state variable, is the first-order derivative of the state variable with respect to time, t is time, A is the state matrix of the wing, B is the control matrix, C is the output matrix, f(t) is the unknown external force disturbance, u(t) is the input signal, and y(t) is the output.

[0038] In actual situations, only the displacement in the direction of wing deflection can be obtained through sensors. Due to the needs of the control algorithm, a state observer must be established to observe each state variable, that is, each modal displacement. Through the Lomborg observer, estimated state variables can be obtained. Specifically, the dynamic equation model established in the previous article is used as the dynamic equation of the observer. The output of the real wing system is obtained through the sensor, and the difference between the real system output and the observer output is used as error feedback. The error is converged by designing an appropriate gain L, thereby obtaining a predicted value that is constantly approaching the real state variable. Specifically:

[0039]

[0040] in is the state variable predicted by the observer, L X =[L1L2] T is the observer gain. The observer gain is obtained using the following bandwidth parameterization method:

[0041] |sI-(AL X C)|=(s+ω0) N

[0042] Where ω0 is the bandwidth of the observer. According to the dynamic performance requirements of the system, a desired bandwidth can be selected and the corresponding observer gain L can be calculated. X .

[0043] In order to suppress the influence of unknown disturbances, a disturbance observer needs to be established to estimate the unknown disturbances in real time and compensate the wing through the controller. The disturbance observer is designed based on the radial basis function (RBF), and the powerful approximation ability of RBF is used to estimate the unknown complex disturbances, where the RBF hidden layer vector h = [h1,h2,…h J ] T The element h in j It can be expressed as:

[0044]

[0045] where c j is the basis function center vector, b j is the basis function width, is the input vector.

[0046] Theoretically, any unknown disturbance can be expressed as:

[0047] f(t)=w T h(α)+ε

[0048] where w=[w1,w2,…w J ] Tis the weight value corresponding to each basis function, and ε is the residual value. The estimated disturbance obtained by using the RBF disturbance observer is:

[0049]

[0050] In order to cope with unknown disturbances that change in real time, a weight value needs to be designed The adaptive law can be expressed as:

[0051]

[0052] Where γ is a positive constant gain value, and P is a positive definite matrix. The matrix P can be obtained according to the following equation:

[0053] (AL X C) T C T PC+C T PC(AL X C)=-Q

[0054] The matrix P is used to assist in the definition of the Lyapunov function below, ensuring that the control algorithm is asymptotically stable.

[0055] In order to ensure the stability of the designed adaptive control system, its stability needs to be verified. Lyapunov's second method is used to verify the stability of the system. The Lyapunov function V is defined according to the system state error equation. By proving that V is always greater than or equal to 0 and the derivative of V is always less than or equal to 0, the system can be proved to be stable. The state error equation of the system can be obtained as:

[0056]

[0057] Define the Lyapunov function as:

[0058]

[0059] Obviously V ≥ 0.

[0060] definition

[0061] Taking the derivative of the function we get:

[0062]

[0063] Assuming that the residual ε is small enough and can be ignored in most cases, we can conclude that Prove that the system is stable.

[0064] As mentioned above, after the estimated disturbance and state variables are obtained through the observer, they need to be fed back to the control system to offset the disturbance and stabilize the overall wing system. The designed control law is

[0065]

[0066] Where K X for The feedback gain matrix, in order to build a stable closed-loop system, the state feedback gain K X The LQR method is used to solve the problem.

[0067] K X =R -1 B T P

[0068] The symmetric positive definite matrix P can be obtained by solving an algebraic Riccati equation:

[0069] A T P+PA+C T QC-PBR -1 B T P=0

[0070] The symmetric positive definite matrices Q and R are the weight matrices of the output and input signals of the controlled system, respectively, which can be approximated by the Bryson rule.

[0071] A simulation example using the above control method is given below.

[0072] This example uses a wing model with a length of 1m and a Young's modulus Y of 71GPa. By applying a 17Hz sine wave excitation of 8N to the wing tip, the RBF disturbance observer is used to predict the disturbance. The results are as follows Figure 3 Then, 8N 17Hz sine wave and 4N 2Hz square wave excitation were applied simultaneously. The predicted results of the combined excitation are shown in Figure 4 The results show that the disturbance observer designed by the present invention can better observe disturbances. By applying random high-frequency disturbance force to the wing tip to simulate complex meteorological disturbances, the results are shown in Figure 5 and Figure 6 As shown, the displacement oscillation amplitude of the wing tip is significantly reduced after the method of the present invention is adopted, which proves that the method of the present invention can effectively solve the problem that the UAV wing is prone to vibration under unknown disturbances during flight.

[0073] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. An adaptive anti-disturbance control method for suppressing the vibration of the wing of an unmanned aerial vehicle, characterized in that: The following steps are involved: An MFC piezoelectric sheet is attached to the root of the upper surface of the drone's wing as a brake, and an MFC piezoelectric sheet is attached to the lower surface as a sensor; According to the structural characteristics of the flexible wing, the dynamic model of the flexible wing is obtained by equivalently treating it as a cantilever beam with a variable cross-section. The control system is established by using the voltage signal generated by the MFC piezoelectric sensor as the input and feedback signal; The voltage signal is calculated by the control system and used as the output and braking signal to suppress the wing vibration.

2. The adaptive anti-disturbance control method for suppressing the wing vibration of a UAV according to claim 1, characterized in that: It also includes establishing a state observer to predict various state variables, namely, the displacements of each order mode, which are used to calculate the feedback gain in the control law.

3. The adaptive anti-disturbance control method for suppressing the wing vibration of a UAV as claimed in claim 2, characterized in that: The state variables are estimated using the Lomborg observer.

4. The adaptive anti-disturbance control method for suppressing the wing vibration of a UAV as claimed in claim 3, characterized in that: The Lumberg observer is used, and the output error between the real wing and the established model is used as feedback. By designing a reasonable observer gain, the estimated state variables are continuously approached to the real state variables, and the estimated state variables are calculated.

5. The adaptive anti-disturbance control method for suppressing the wing vibration of a UAV as claimed in claim 4, characterized in that: According to the bandwidth of the observer and the expected bandwidth, the corresponding observer gain is calculated.

6. The adaptive anti-disturbance control method for suppressing the wing vibration of a UAV as claimed in claim 1, characterized in that: It also includes the establishment of a disturbance observer to estimate future disturbances in real time and compensate the wing through the adaptive feedforward part of the designed control law.

7. The adaptive anti-disturbance control method for suppressing the wing vibration of a UAV as claimed in claim 6, characterized in that: A disturbance observer is designed based on radial basis functions, and the approximation ability of radial basis functions is used to estimate unknown complex disturbances.

8. The adaptive anti-disturbance control method for suppressing the wing vibration of a UAV as claimed in claim 7, characterized in that: Design an adaptive law for weight values ​​to cope with unknown disturbances that change in real time.

9. The adaptive anti-disturbance control method for suppressing the wing vibration of a UAV according to claim 2 or 6, characterized in that: After the estimated disturbance variables and state variables are obtained through the observer, they are fed back to the control system to offset the disturbance and stabilize the UAV wing system.

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