An Adaptive Event-Triggered Control Method for the Flight Attitude of a Quadrotor UAV

Abstract

The present invention discloses an adaptive event-triggered control method for the flight attitude of a quadrotor unmanned aerial vehicle, which relates to the technical field of unmanned aerial vehicle tracking control. The method includes constructing a dynamic model of the flight attitude system of the unmanned aerial vehicle and dividing the unmanned aerial vehicle system into a position system and an attitude system. On the basis of a classical backstepping controller, combined with the dynamic surface control method, the approximation characteristics of the RBF neural network are used to reduce the influence of external disturbances on the system. An event-triggered mechanism is introduced. On the basis of adopting a fixed threshold strategy to trigger control, a switching threshold event-triggered strategy including a fixed threshold strategy and a relative threshold strategy is proposed to achieve effective control of the flight attitude system of the quadrotor unmanned aerial vehicle and avoid the Zeno phenomenon. By designing an adaptive event-triggered control method for the flight attitude of the quadrotor unmanned aerial vehicle, the tracking trajectory error of the flight attitude of the unmanned aerial vehicle is greatly reduced, and effective tracking control of the attitude of the quadrotor unmanned aerial vehicle is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of UAV tracking control, and particularly relates to an adaptive event-triggered control method for the flight attitude of a quadrotor UAV. Background Technique

[0002] In recent years, UAVs have been more and more widely used in civil, military and other fields, such as military reconnaissance, disaster relief, aerial photography and other fields, and the role of UAVs is becoming more and more prominent. Since quadrotor UAVs are smaller in volume, lighter in mass and more convenient to operate than fixed-wing UAVs, they can complete more complex flight maneuvers.

[0003] With the continuous improvement of the requirements for UAV flight missions, UAV flight attitude control faces the following challenges: The quadrotor UAV system has model dynamic uncertainty, with characteristics such as strong nonlinearity, strong coupling and strong time-variation, and there are external disturbances in the system. Therefore, it is very necessary to study the flight attitude trajectory tracking control of quadrotor UAVs.

[0004] Through literature review and analysis of the existing technology, it is found that currently in the field of UAV flight attitude control, nonlinear control methods based on modern control theory are adopted, such as backstepping method, sliding mode control method, dynamic surface control method, adaptive control method, etc. "A Formation Control Method for Quadrotor UAVs Based on Adaptive RBF Neural Network" uses an adaptive RBF neural network to deal with nonlinear dynamic uncertainty and inevitable disturbances. "A Multi-Quadrotor UAV Adaptive Sliding Mode Attitude Tracking Control Method and System" provides a multi-quadrotor UAV adaptive sliding mode attitude tracking control method, constructs a sliding mode controller, improves the robustness of the UAV system to external disturbances, and effectively suppresses the influence of external disturbances. "A Quadrotor UAV Adaptive Robust Trajectory Tracking Control Method" uses an adaptive estimation algorithm to estimate the parameter values of the rotors, meeting the accuracy requirements of the quadrotor UAV trajectory tracking for control input.

[0005] However, all of the above are continuous algorithms, which require the controller to be updated in real time, so that the actuators in the actual system also adjust continuously in real time. In the actual system, the controller is usually an embedded data controller, and usually needs to run after discretizing the continuous algorithm period. In order not to damage the stability of the system algorithm, the controller still needs to be updated at a high frequency, which has high requirements for the controller performance. At the same time, high-frequency adjustment will also cause losses to the actuators. Therefore, an adaptive event-triggered control method for the flight attitude of quadrotor UAVs is needed. Summary of the Invention

[0006] The purpose of this application is to provide an adaptive event-triggered control method for the flight attitude of a quadrotor UAV to solve the problems raised in the above background technique.

[0007] To achieve the above object, the present application provides the following technical solutions: An adaptive event-triggered control method for the flight attitude of a quadrotor UAV, comprising the following steps:

[0008] S1. According to the structural characteristics of the UAV system, construct a dynamic model of the UAV flight attitude system, and divide the UAV system into a position system and an attitude system;

[0009] S2. Based on the classical backstepping controller, combined with the dynamic surface control method, utilize the approximation characteristics of the RBF neural network to reduce the influence of external disturbances on the system;

[0010] S3. Introduce an event-triggered mechanism. On the basis of adopting a fixed-threshold strategy to trigger control, propose a switching-threshold event-triggered strategy that includes a fixed-threshold strategy and a relative-threshold strategy to achieve effective control of the quadrotor UAV flight attitude system and avoid the occurrence of Zeno phenomena.

[0011] Preferably, in S1, the dynamic model of the UAV flight attitude system is expressed as follows:

[0012]

[0013]

[0014]

[0015] Among them, l is the distance from the origin of the UAV coordinate system to the rotor center, I x 、I y 、I z are the moments of inertia of the UAV, is the attitude angle, U = [U2 U3 U4] T is the control input of the attitude system, Ω i (i = 1, 2, 3, 4) is the motor speed, Ω r = Ω1 - Ω2 + Ω3 - Ω4.

[0016] Preferably, define the expression of the attitude angle error as:

[0017]

[0018] θ e = θ - θ d

[0019] ψ e = ψ - ψ d

[0020] Among them, is the desired attitude angle.,

[0021] Preferably, the attitude system includes a roll angle subsystem, and the control input of the roll angle subsystem is:

[0022]

[0023] where is the output of the RBF network, and α1 is the virtual control rate.

[0024] Preferably, in S3, the introduction of the event-triggering mechanism includes establishing the triggering conditions of the triggering mechanism, and the steps of establishing the triggering conditions of the triggering mechanism include:

[0025] Let represent the trigger time sequence of the roll angle subsystem, which is updated from to Regarding as the instant of controller update, and forming the set of controller execution time intervals whose lower bound is t * ;

[0026] The expression of the triggering condition is:

[0027]

[0028] Preferably, the event-triggering controller of the roll angle subsystem is:

[0029]

[0030]

[0031] In summary, the technical effects and advantages of the present invention are as follows:

[0032] 1. By designing an adaptive event-triggering control method for the flight attitude of a quadrotor UAV, the present invention significantly reduces the tracking trajectory error of the UAV's flight attitude, achieving effective tracking control of the quadrotor UAV's attitude.

[0033] 2. The present invention adopts a switching threshold event-triggering strategy, providing a balance between the tracking error and the execution interval, offering more flexibility to balance system performance and network constraints, and effectively avoiding large impulses to the system when new events are triggered, effectively protecting system components.

[0034] 3. The present invention introduces an event-triggering control mechanism into the flight attitude system of a quadrotor UAV, effectively avoiding the occurrence of the Zeno phenomenon. Brief Description of the Drawings

[0035] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those skilled in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0036] Figure 1 The structure diagram of the attitude control system of the quadrotor UAV in this embodiment;

[0037] Figure 2 The attitude tracking curve of the quadrotor UAV in this embodiment;

[0038] Figure 3 The control input curve of the roll angle subsystem under the event trigger mechanism in this embodiment;

[0039] Figure 4 The number of trigger times under the fixed threshold event trigger strategy in this embodiment;

[0040] Figure 5 The number of trigger times under the switching threshold event trigger strategy in this embodiment. Detailed implementation manners

[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0042] Embodiment: Refer to Figures 1-5 An adaptive event-triggered control method for the flight attitude of a quadrotor UAV shown in the following steps:

[0043] S1. The flight attitude system of the quadrotor UAV consists of a roll angle subsystem, a pitch angle subsystem, and a yaw angle subsystem. Build a mathematical model of the flight attitude system of the quadrotor UAV. Taking the roll angle subsystem as an example, establish the expression of the state space equation:

[0044]

[0045] Among them, a1, a2, b1, Ω r Are design parameters, Represents the external disturbance torque;

[0046] S2. Calculate the attitude angle error based on the expected value and the actual value of the roll angle of the UAV attitude system, and considering the unknown external interference terms existing in the system, establish a control model for the quadrotor UAV attitude system under the corresponding event-triggering mechanism;

[0047] Let represent the trigger time sequence of the roll angle subsystem. When the system meets the event-triggering condition, update the value of the system control input to the system, that is, from update to Regard as the instant when the controller is updated, and form a set of controller execution time intervals whose lower bound is t * .

[0048] For define the expression of the roll angle error of the attitude system as:

[0049]

[0050] where, is the actual value of the roll angle, is the expected value of the roll angle.

[0051] The expression of the control model of the roll angle subsystem of the UAV attitude system is:

[0052]

[0053] where, is the output of the RBF network, and α1 is the virtual control rate;

[0054] S3. Introduce an event-triggering mechanism, establish the triggering condition of the triggering mechanism, and apply the event-triggering control model to the controller of the UAV system to achieve effective control of the UAV system.

[0055] Let represent the trigger time sequence of the roll angle subsystem. When the system meets the event-triggering condition, update the value of the system control input to the system, that is, from update to Regard as the instant when the controller is updated, and form a set of controller execution time intervals whose lower bound is t * .

[0056] Based on the fixed-threshold event-triggering strategy, the expression of the triggering condition is:

[0057]

[0058] Design the event-triggering controller of the roll angle subsystem as:

[0059]

[0060]

[0061] The parameter settings of the roll angle subsystem of the attitude system of the quadrotor UAV in this embodiment are as follows:

[0062] m = 10, I X = 0.006, I Y = 0.006, I Z = 0.012

[0063] b = 3.13e-5, d = 7.5e-7, J r = 5e-5, l = 3;

[0064] S4. Ensure the stability of the system under the event-triggered mechanism and avoid the Zeno phenomenon. Verify the stability of the UAV attitude system according to the Lyapunov stability theory. By analyzing, ensure that the internal event time is greater than a positive constant, and ensure that the control signal is updated discontinuously in time, so as to avoid the Zeno phenomenon;

[0065] Define the error variables z1, z2, and s2 as the filtering error:

[0066] z1 = x1 - x d

[0067] z2 = x2 - α1

[0068] where, is the desired roll angle, and α1 is the virtual control rate.

[0069] Since the backstepping method adopts a "progressive" design method, during the process of designing the controller, it is necessary to take the derivative of the virtual control rate α1. In order to avoid the "differential explosion" phenomenon during the derivative process, is input into the first-order low-pass filter, and the expression of the derivative of the virtual control rate is obtained as:

[0070]

[0071] Define the filtering error s2 as:

[0072]

[0073] Considering that there is an external disturbance δ with an unknown upper bound in the state-space equation of the roll angle subsystem φ , use the RBF neural network adaptive control method to approximate the disturbance term, so as to realize the estimation and compensation of the disturbance in the system. The RBF network algorithm is as follows:

[0074]

[0075]

[0076] Among them, \(x\) is the input of the RBF network, \(j\) represents the \(j\)th node of the hidden layer, \(h = [h j T is the output of the Gaussian basis function, \(W * is the ideal weight, \(\varepsilon\) is the approximation error, is the difference of the network weights.

[0077] Design the output of the RBF network and the adaptive law of the RBF network weights:

[0078]

[0079]

[0080] Among them, \(\eta\) and \(\sigma\) are constants, and \(\eta>0\), \(\eta>0\).

[0081] Define the Lyapunov function \(V1\):

[0082]

[0083] Take the derivative of \(V1\) to get:

[0084]

[0085] Among them, \(r1>0\),

[0086]

[0087] Take the extreme value of \(V1\) to get:

[0088]

[0089] Therefore, the left and right signals of the closed-loop system are bounded. By finding the extreme value, it can be obtained that \(V1\) is bounded. Therefore, the system can achieve asymptotic stability.

[0090] Take the derivative of the measurement error \(e(t)=w1(t)-U2(t)\) to get:

[0091]

[0092] Since both \(z2\) and \(U2(t)\) are smooth and bounded functions, it is not difficult to prove that \(h2(z2)\) is a bounded function. Therefore, is bounded. That is, there exists a constant satisfying For When \(t = t φ, \), there is \(e9t φ, ​) = 0; when the set event trigger condition is satisfied, i.e., |w1(t) - U2(t)| = m1, there is

[0093] Integrate within the set of execution time intervals for to obtain by integration:

[0094]

[0095]

[0096] From the above equation, it can be obtained that:

[0097]

[0098] Therefore, there exists such that the lower bound of the set composed of the execution time intervals of the system is t * , that is, the system control model designed by the present invention can avoid the Zeno phenomenon.

[0099] Based on the fixed threshold event-triggering strategy, the present invention proposes a new switching threshold event-triggering strategy. When the control signal satisfies |U(t)| < D, the relative threshold strategy is adopted to achieve precise control when needed; when |U(t)| ≥ D, the threshold strategy is switched to the fixed threshold strategy to ensure a certain system performance. Since the control law adopted by the switching threshold strategy is the same as that adopted by the fixed threshold strategy, it can still be proved by the Lyapunov stability theory that all state variables of the system are bounded.

[0100] Use Matlab software to simulate the flight attitude adaptive event-triggering control method of a quadrotor UAV, and analyze the simulation results to verify the effectiveness of the method described in the present invention. The simulation results are shown in the figure. Among them, Figure 2 indicates that the attitude system of the quadrotor UAV realizes the trajectory tracking control of each desired attitude angle, and the tracking error is small, all remaining within ±0.005. Figure 3 is the comparison of the system control inputs U2, U2 ' under the event-triggering mechanism. After introducing the event-triggering mechanism, the control input data is a piecewise function, indicating that the system is updated only when triggered. Figure 4 is the number of trigger times under the fixed threshold event-triggering strategy. Within the same trigger time, the event-triggering controller constructed by the present invention triggers 2298 times within 10 s and achieves the same tracking effect as the periodic sampling controller sampling continuously 10,000 times. Figure 5It is the number of triggers under the switching threshold event trigger strategy. A total of 382 triggers occurred within 10s, achieving the same tracking effect as well. The execution time of the switching threshold strategy is longer than that of the fixed threshold strategy, effectively avoiding large pulses to the system when new events are triggered and protecting system components more effectively.

[0101] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An adaptive event-triggered control method for the flight attitude of a quadrotor UAV, characterized in that: It includes the following steps: S1. According to the structural characteristics of the UAV system, construct the dynamic model of the UAV flight attitude system, and divide the UAV system into a position system and an attitude system; The attitude system includes a roll angle subsystem, and the control input of the roll angle subsystem is: Among them, is the output of the RBF network, and α1 is the virtual control rate; S2. On the basis of the classical backstepping controller, combined with the dynamic surface control method, utilize the approximation characteristics of the RBF neural network to reduce the influence of external interference on the system; S3. Introduce an event-triggering mechanism. On the basis of adopting the fixed-threshold strategy to trigger control, propose a switching-threshold event-triggering strategy that includes both the fixed-threshold strategy and the relative-threshold strategy to achieve effective control of the flight attitude system of the quadrotor UAV and avoid the Zeno phenomenon; In S3, the introduction of the event-triggering mechanism includes establishing the triggering conditions of the triggering mechanism, and the steps of establishing the triggering conditions of the triggering mechanism include: Let represent the trigger moment sequence of the roll angle subsystem, which is updated from to Regard as the controller update instant and form the set of controller execution time intervals whose lower bound is t * ; The expression of the triggering conditions is:

2. An adaptive event-triggered control method for the flight attitude of a quadrotor UAV according to claim 1, characterized in that: In S1, the dynamic model of the UAV flight attitude system is expressed as follows: where \(l\) is the distance from the origin of the UAV coordinate system to the rotor center, \(I\) x , \(I\) y , \(I\) z are the moments of inertia of the UAV, is the attitude angle, \(U = [U_2\ U_3\ U_4]\) T is the control input of the attitude system, \(\Omega\) i \((i = 1, 2, 3, 4)\) is the motor speed, \(\Omega\) r =\(\Omega_1 - \Omega_2+\Omega_3 - \Omega_4\).

3. An adaptive event-triggered control method for the flight attitude of a quadrotor UAV according to claim 2, characterized in that: Define the expression of the attitude angle error as: θ e = θ - θ d ψ e = ψ - ψ d wherein, is the desired attitude angle.

4. An adaptive event-triggered control method for the flight attitude of a quadrotor UAV according to claim 3, characterized in that: The event-triggering controller of the roll angle subsystem is:

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