Multi-rotor unmanned aerial vehicle anti-interference flight control method

Abstract

The invention discloses an anti-interference flight control method for a multi-rotor unmanned aerial vehicle. The method comprises the following steps: establishing an attitude dynamic model of the multi-rotor unmanned aerial vehicle; according to a moment of momentum theorem and an Euler equation, establishing an unmanned aerial vehicle attitude kinetic equation; setting a control moment in the man-machine attitude kinetic equation as a dependent variable, and performing first-order Taylor expansion at any point; adding expectation into the expanded kinetic equation to obtain an improved incremental nonlinear dynamic inverse controller; using real-time measured values of motion variables of the multi-rotor unmanned aerial vehicle in the improved incremental nonlinear dynamic inverse controller, and optimizing the improved incremental nonlinear dynamic inverse controller by adopting a control closed-loop transfer function of an attitude angular acceleration loop and a disturbance closed-loop transfer function of the attitude angular acceleration loop; and controlling the attitude of the multi-rotor unmanned aerial vehicle by using the optimized improved incremental nonlinear dynamic inverse controller. According to the method, the dependence of the unmanned aerial vehicle control method on the model can be reduced, and the stability of the multi-rotor unmanned aerial vehicle is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle control, and particularly to a multi-rotor unmanned aerial vehicle anti-disturbance flight control method. Background Art

[0002] In recent years, the application scenarios of multi-rotor unmanned aerial vehicles have expanded rapidly. Especially in the military field, complex maneuvering tasks such as terrain reconnaissance, moving target tracking and strike, and dynamic precise intersection missions have put forward higher and higher requirements for the dynamic response ability of unmanned aerial vehicles. However, in the above scenarios, it is difficult to ensure that the outdoor environment is always an ideal calm wind environment. In extreme cases, the unmanned aerial vehicle needs to perform tasks in a disturbance environment with a wind force of level 5 or stronger. Under such strong disturbance environmental conditions, flight control is required to have sufficient anti-disturbance ability while ensuring stability, rapidity, and accuracy. Traditional anti-disturbance control methods include linear control methods and non-linear control methods. Among them, linear control methods include PID (Proportional-Integral-Derivative control), H∞, and LQR (Linear Quadratic Regulator), etc. However, such methods usually do not have anti-disturbance characteristics, and the state recovery speed is usually slow after being disturbed; in non-linear control methods, model predictive control, intelligent control methods based on deep learning, etc. are usually difficult to be applied to the single-chip microcomputer actually used for flight control due to complex results and extremely large computational amounts, and it is difficult to be put into practical engineering applications and obtain ideal control effects.

[0003] Generally speaking, due to the complex and changeable aerodynamic characteristics of unmanned aerial vehicles, their characteristics are also different in different motion modes. Therefore, the modeling of the overall aircraft aerodynamics is usually relatively complex, and it is also difficult to ensure the accuracy of its modeling. However, the Nonlinear Dynamic Inversion (NDI) method relies too much on the model. When the overall aircraft aerodynamic model is inaccurate, the control effectiveness of the Nonlinear Dynamic Inversion (NDI) method will drop significantly. In addition, the anti-disturbance ability of the Nonlinear Dynamic Inversion (NDI) method is poor. When the unmanned aerial vehicle is disturbed externally, it is difficult to converge to the original state.

[0004] The existing multi-rotor unmanned aerial vehicle flight control methods have poor anti-disturbance ability. When the multi-rotor unmanned aerial vehicle is disturbed externally, it is difficult to quickly recover to the original state and may even become unstable. In addition, the existing multi-rotor unmanned aerial vehicle flight control methods have a high dependence on the model. When the model is inaccurate, the control effectiveness drops significantly, resulting in the difficulty of the multi-rotor unmanned aerial vehicle to fly stably. Moreover, there is also a problem that the response speed of the multi-rotor unmanned aerial vehicle flight control method is slow, so that when the multi-rotor unmanned aerial vehicle performs large maneuvering flight tasks, it is difficult to quickly respond to instructions, affecting the flight effect, and ultimately unable to ensure that the unmanned aerial vehicle can complete large maneuvering flight tasks quickly, stably, accurately, and robustly. Summary of the Invention

[0005] To solve some or all of the technical problems existing in the above-mentioned prior art, the present invention provides a multi-rotor UAV anti-disturbance flight control method, which can reduce the dependence of the multi-rotor UAV control method on the model. Even when the model is inaccurate, it can still maintain good control performance, improve the stability of the multi-rotor UAV, enhance the anti-disturbance ability of the control closed-loop. After being disturbed, the multi-rotor UAV can quickly return to the original state, avoid instability, ensure that the multi-rotor UAV can quickly and stably complete large maneuver flight tasks, improve flight efficiency and flexibility, and enable the multi-rotor UAV to quickly, stably, accurately and robustly complete large maneuver flight tasks.

[0006] The technical solution of the present invention is as follows:

[0007] The present invention provides a multi-rotor UAV anti-disturbance flight control method, including:

[0008] According to the momentum moment theorem and Euler's equation, establish the attitude dynamics equation of the multi-rotor UAV;

[0009] Set the control torque in the attitude dynamics equation as the dependent variable and perform a first-order Taylor expansion at any point;

[0010] Add the expectation to the expanded dynamics equation to obtain an improved incremental nonlinear dynamic inverse controller;

[0011] Use the real-time measured values of the multi-rotor UAV motion variables in the improved incremental nonlinear dynamic inverse controller to compensate for the total disturbance of the UAV system;

[0012] Optimize the improved incremental nonlinear dynamic inverse controller by using the control closed-loop transfer function of the attitude angular acceleration loop and the disturbance closed-loop transfer function of the attitude angular acceleration loop;

[0013] Use the optimized improved incremental nonlinear dynamic inverse controller to perform closed-loop control on the attitude of the multi-rotor UAV.

[0014] Furthermore, in the above-mentioned multi-rotor UAV anti-disturbance flight control method, the multi-rotor UAV attitude dynamics equation includes:

[0015]

[0016] Among them, J = diag([J xx J yy J zz ) represents the inertia tensor of the multi-rotor UAV; represents the attitude angular acceleration of the multi-rotor UAV; ω = [p q r] T , represents the attitude angular velocity of the multi-rotor UAV; represents the control torque of the multi-rotor UAV; Mext = M a + d, representing other torques of the multi-rotor UAV except the control torque, including the aerodynamic torque M a and the disturbance torque d.

[0017] Furthermore, in the above anti-disturbance flight control method of the multi-rotor UAV, the control torque in the attitude dynamics equation of the multi-rotor UAV is set as the dependent variable, and the first-order Taylor expansion at any point includes:

[0018]

[0019] Among them, represents the control torque at the current moment, represents the attitude angular acceleration at the current moment, ω0 represents the attitude angular velocity at the current moment, represents the other torques at the current moment, O 2 represents the high-order remainder term of the first-order Taylor expansion.

[0020] Furthermore, in the above anti-disturbance flight control method of the multi-rotor UAV, the control torque in the attitude dynamics equation of the multi-rotor UAV is set as the dependent variable, and the first-order Taylor expansion is performed at any point. After adding the expectation, the improved incremental nonlinear dynamic inverse controller includes:

[0021]

[0022] Among them, represents the desired control torque of the multi-rotor UAV, represents the control torque estimated through the functional relationship g(Ω) between the control torque of the multi-rotor UAV and the motor speeds of the multi-rotor UAV, where Ω is the vector composed of the motor speeds of each multi-rotor UAV, represents the desired attitude angular acceleration of the multi-rotor UAV.

[0023] Furthermore, in the above anti-disturbance flight control method of the multi-rotor UAV, the closed-loop transfer function of the attitude angular acceleration loop is calculated through the following formula:

[0024]

[0025] Among them, s represents the Laplace transform of the differential link, A(s) represents the motor dynamics transfer function, and I3 represents the third-order identity matrix.

[0026] Furthermore, in the above anti-disturbance flight control method of the multi-rotor UAV, the disturbance closed-loop transfer function of the attitude angular acceleration loop includes:

[0027]

[0028] Among them, J -1 represents the inverse operation of the inertia tensor of the multi-rotor UAV.

[0029] The main advantages of the technical solution of the present invention are as follows:

[0030] For the anti-disturbance flight control method of the multi-rotor UAV of the present invention, by introducing an incremental method for reasonable transformation of the aircraft dynamics model, an incremental nonlinear dynamic inverse controller is constructed. While ensuring the response speed, the dependence of the method on the model is reduced, the problem of reduced control efficiency when the overall aircraft aerodynamic model is inaccurate is solved, the anti-disturbance ability of the control closed-loop is enhanced, and it is ensured that the UAV can quickly recover to the original state after being disturbed without instability. Furthermore, it can ultimately ensure that the UAV can quickly, stably, accurately and robustly complete large maneuver flight tasks. Description of the Drawings

[0031] The drawings described herein are used to provide a further understanding of the embodiments of the present invention and constitute a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0032] Figure 1 is a schematic flow chart of the anti-disturbance flight control method of the multi-rotor UAV according to an embodiment of the present invention;

[0033] Figure 2 is a schematic diagram of the principle of the incremental nonlinear dynamic inverse method in the anti-disturbance flight control method of the multi-rotor UAV provided by an embodiment of the present invention;

[0034] Figure 3 is a schematic diagram of the principle of the incremental nonlinear dynamic inverse method of the attitude angular acceleration loop in the anti-disturbance flight control method of the multi-rotor UAV provided by an embodiment of the present invention;

[0035] Figure 4 is an attitude angle response effect diagram under a step attitude command in the anti-disturbance flight control method of the multi-rotor UAV provided by an embodiment of the present invention;

[0036] Figure 5 is an attitude angular velocity response speed effect diagram under a step attitude command in the anti-disturbance flight control method of the multi-rotor UAV provided by an embodiment of the present invention;

[0037] Figure 6 is a control effect diagram of the incremental nonlinear dynamic inverse method under the change of the attitude dynamics model of the multi-rotor UAV in the anti-disturbance flight control method of the multi-rotor UAV provided by an embodiment of the present invention;

[0038] Figure 7Effect diagram of the incremental nonlinear dynamic inversion method control under the change of the attitude dynamics model of the multi-rotor UAV in the anti-disturbance flight control method provided by an embodiment of the present invention;

[0039] Figure 8 Effect diagram of the attitude response under disturbance in the anti-disturbance flight control method of the multi-rotor UAV provided by an embodiment of the present invention;

[0040] Figure 9 Effect diagram of the attitude angle response under disturbance in the anti-disturbance flight control method of the multi-rotor UAV provided by an embodiment of the present invention. Detailed implementation manners

[0041] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0042] The following combines the attached Figure 1 - attached Figure 9 to detail the technical solutions provided by the embodiments of the present invention.

[0043] Specifically, the nonlinear dynamic inversion (NDI) method is a nonlinear control method that relies on sensor measurement information and a high-precision aircraft dynamics model. Its structure is simple and easy to implement, and the basic principle is as follows.

[0044] Assume that the multi-rotor UAV is a rigid body and axisymmetric about the z-axis of the body frame. According to the momentum moment theorem and Euler's equation, the attitude dynamics equation of the multi-rotor UAV can be obtained as follows:

[0045]

[0046] where ω = [p q r] T represents the attitude angular velocity in the body frame, J = diag([J xx J yy J zz ) is the inertia tensor of the UAV, which is a diagonal matrix under the above assumption, and the rest of the products of inertia are all 0, represents the control torque in the body frame, M ext = M a +d represents the other torques except the control torque in the body frame, mainly including the aerodynamic torque M a and the disturbance torque d. Taking the control torque as the control quantity, substituting M cPlace it on the left side of the equation, and place the remaining terms on the right side. Take the estimated value of the aerodynamic moment as M ext 's estimated value, and the NDI attitude controller expression is obtained as shown in the following equation:

[0047]

[0048] where the subscript represents the expected value, represents the expected attitude angular acceleration, which is solved from the outer-loop attitude control and attitude angular rate control, is the expected control moment, represents the estimated value of the aerodynamic moment calculated according to the aerodynamic model.

[0049] As shown in the appendix Figure 1 , the embodiment of the present invention provides a multi-rotor UAV disturbance rejection flight control method, and this method includes the following steps S1 - S6:

[0050] Step S1: Establish the attitude dynamics equation of the multi-rotor UAV according to the momentum moment theorem and Euler's equation.

[0051] Assume that the multi-rotor UAV is a rigid body and is axisymmetric about the body frame z-axis. According to the momentum moment theorem and Euler's equation, the attitude dynamics equation of the multi-rotor UAV can be obtained as shown below:

[0052]

[0053] where J = diag([J xx J yy J zz ) represents the inertia tensor of the multi-rotor UAV; represents the attitude angular acceleration of the multi-rotor UAV; ω = [p q r] T , represents the attitude angular velocity of the multi-rotor UAV; represents the control moment of the multi-rotor UAV; M ext = M a +d, represents the other moments of the multi-rotor UAV except the control moment, including the aerodynamic moment M a and the disturbance moment d.

[0054] Step S2: Set the control moment in the attitude dynamics equation of the multi-rotor UAV as the dependent variable and perform a first-order Taylor expansion at any point;

[0055] Step S3: Add the expectation to the expanded dynamics equation to obtain an improved incremental nonlinear dynamic inverse controller; thus, reasonably transform it into an incremental form and construct the relationship between the angular acceleration increment and the control moment increment;

[0056] Set the control moment in the attitude dynamics equation of the multi-rotor UAV as the dependent variable, and perform a first-order Taylor expansion at any point, including:

[0057]

[0058] Among them, represents the partial derivative operation, represents the control moment at the current moment, represents the attitude angular acceleration at the current moment, ω0 represents the attitude angular velocity at the current moment, represents the other moments at the current moment, O 2 represents the high-order remainder term of the first-order Taylor expansion.

[0059] The attitude angular rate ω and the other moments M except the control moment ext can be regarded as slow variables. When the controller frequency is high enough and the time interval is short enough, the two terms related to ω and M ext and the high-order remainder term O 2 can be ignored. Under the above assumptions, the above equation can be simplified as:

[0060]

[0061] Substitute the expected value into the above equation to obtain the improved incremental nonlinear dynamic inverse controller:

[0062]

[0063] Among them, represents the expected control moment of the multi-rotor UAV, represents the control moment estimated through the functional relationship g(Ω) between the control moment of the multi-rotor UAV and the motor speed of the multi-rotor UAV. Ω is the vector composed of the motor speeds of each multi-rotor UAV, represents the expected attitude angular acceleration of the multi-rotor UAV.

[0064] In summary, the INDI attitude control block diagram is as shown in Figure 2 shown below. The anti-disturbance flight control method and principle of the multi-rotor UAV of the present invention will be further described in conjunction with Figure 2 as follows:

[0065] First, the meanings and functions of each parameter involved in Figure 2 will be described one by one (for the convenience of detailed description of the figure and to reflect the technical solution of the present invention and the Figure 2 integrity of the parameters in, the meanings and functions of the above-described parameters will be described again in this embodiment). Specifically, Figure 2 the parameters involved in include:

[0066] φref and θ ref respectively represent the desired roll angle and pitch angle of the multi-rotor UAV; φ and θ respectively represent the current roll angle and pitch angle of the multi-rotor UAV; P φ and P θ and P r and P p and P q respectively represent the proportional coefficients in the proportional controller, which are used to adjust the output according to the error. In some alternative implementation manners, the above proportional controller can be set as a PID controller; P ref and q ref and r ref respectively represent the desired roll, pitch and yaw angular velocities; p, q and r respectively represent the roll, pitch and yaw angular velocities; respectively represent the desired roll, pitch and yaw angular accelerations; represents the desired attitude angular acceleration of the multi-rotor UAV, that is, the target angular velocity that the multi-rotor UAV expects to reach during flight; J = diag([J xx J yy J zz ) represents the inertia tensor of the multi-rotor UAV; represents the attitude angular acceleration of the multi-rotor UAV; ω = [p q r] T , represents the attitude angular velocity of the multi-rotor UAV; Ω represents the vector composed of the rotational speeds of each multi-rotor UAV motor, which is the actual angular velocity measurement value obtained from the motor or other sensors; represents the desired control torque of the multi-rotor UAV, represents the control torque estimated through the functional relationship g(Ω) between the multi-rotor UAV control torque and the multi-rotor UAV motor rotational speed; Ω is the vector composed of the rotational speeds of each multi-rotor UAV motor; d represents the disturbance torque, S represents the Laplace transform of the differential link, represents an adder for performing addition operations. The + sign indicates that the signal gain entering the adder is +1, the - sign indicates that the signal gain entering the adder is -1, and those not marked are defaulted to a gain of +1. [φ, θ] represents the vector composed of the roll angle and pitch angle, and PWM solution represents a pulse width modulation solution unit for converting the control signal into a pulse width modulation signal that the motor can understand.

[0067] For the system composed of the above parameters, in combination with Figure 2 , a detailed description is given of how the embodiment of the present invention adopts proportional control for both the outer-loop attitude control and attitude angular rate control of the multi-rotor UAV to improve the response speed of the controller and ensure that the UAV can quickly and stably complete large maneuver flight tasks:

[0068] The above system according to the embodiments of the present invention first starts from the desired attitude angle, and obtains an error signal by comparing it with the actually measured attitude angle. Then, these error signals are adjusted by a proportional controller to generate a desired attitude angular velocity signal. The desired angular velocity is compared with the actually measured attitude angular velocity to obtain an error signal, which enters the proportional controller for adjustment to generate a desired attitude angular acceleration signal. Subsequently, the increment of the desired attitude angular acceleration signal relative to the actually measured attitude angular acceleration signal is calculated and multiplied by the inertia tensor matrix to obtain an increment of the control torque. On this basis, the control torque at the current moment is added to obtain the desired control torque. Then, the desired control torque is resolved into PWM in the PWM resolution module. Next, these instructions are converted into control signals for the motor through the PWM demodulation module, so as to drive the motor to adjust the attitude of the UAV to reduce the error and achieve precise attitude control. The goal of the entire process is to achieve autonomous and stable flight of the UAV and a rapid response to the expected attitude.

[0069] Specifically, as an example, for the INDI controller that only considers the inner-loop attitude angular acceleration, its control block diagram is as Figure 3 shown. The following further explains its principle in combination with Figure 3 :

[0070] First, the meanings and functions of each parameter involved in Figure 3 are explained one by one (for the convenience of detailed description of this figure, to reflect the technical solution of the present invention and the Figure 3 integrity of the parameters in, the meanings and functions of the above-described and explained parameters are described again in this embodiment). Specifically, Figure 3 the parameters involved in include:

[0071] represents the desired attitude angular acceleration of the multi-rotor UAV, that is, the target angular velocity that the multi-rotor UAV expects to reach during flight; J = diag([J xx J yy J zz ) represents the inertia tensor of the multi-rotor UAV; represents the control torque estimated through the functional relationship g(Ω) between the control torque of the multi-rotor UAV and the motor speed of the multi-rotor UAV, where Ω is a vector composed of the motor speeds of each multi-rotor UAV; represents the desired control torque of the multi-rotor UAV; g -1 represents the inverse functional relationship between the control torque of the multi-rotor UAV and the motor speed of the multi-rotor UAV; h represents the functional relationship between PWM and the motor speed; h -1Represents the inverse function relationship between PWM and motor speed; A(s) represents the motor dynamics transfer function; PWM is the abbreviation of Pulse width modulation, representing the pulse width modulation technology, which is an analog control method. Its principle is to modulate the bias of the transistor base or MOS transistor gate according to the change of the corresponding load to achieve the change of the conduction time of the transistor or MOS transistor, so as to achieve the change of the output of the switching regulated power supply. In some realizable ways, PWM can also represent the relevant module or relevant hardware device of pulse width modulation; Ω represents the vector composed of the rotational speeds of each multi-rotor UAV motor, which is the actual angular velocity measurement value obtained from the motor or other sensors; g represents the functional relationship between the control torque of the multi-rotor UAV and the rotational speed of the multi-rotor UAV motor. Represents the control torque of the multi-rotor UAV; M ext = M a + d, represents other torques of the multi-rotor UAV except the control torque, including the aerodynamic torque M a and the disturbance torque d; J -1 Represents the inverse operation of the inertia tensor of the multi-rotor UAV; Represents the attitude angular acceleration of the multi-rotor UAV; S represents the Laplace transform of the differential link, Represents the Laplace transform of the integral link, Represents an adder for performing addition operations. The + sign indicates that the signal gain entering the adder is +1, the - sign indicates that the signal gain entering the adder is -1, and those not marked default to a gain of +1. The motor module includes: A(s), h, and g to form a feedback loop for closed-loop control; the UAV part includes J -1 and

[0072] The above parameters together constitute a system for disturbance rejection flight control of a multi-rotor UAV, aiming to accurately control the angular velocity of the UAV, resist external disturbances, and ensure the stable flight of the multi-rotor UAV.

[0073] For the system composed of the above parameters, the following will detail how to optimize the controller design, improve the disturbance rejection ability of the control closed-loop, enhance the disturbance rejection ability of the control closed-loop, and ensure that the UAV can quickly recover to its original state without instability after being disturbed. The following will be described in detail in combination with Figure 3 :

[0074] From left to right, the signal first passes through an adder to subtract from the actually measured attitude angular acceleration, introduce and compensate the measured signal containing disturbances to obtain the attitude angular acceleration increment. At the same time, multiply this signal by the inertia tensor matrix of the multi-rotor UAV to obtain the control torque increment. Then, multiply this signal by the control torque at the current moment estimated using the motor speed Add them up to obtain the desired control torque After that, the signal enters the PWM calculation module, calculates the PWM, and then transmits it to the motor to control the speed of the motor, so as to achieve the expected control effect, realize the precise speed regulation and stable operation of the multi-rotor UAV, and resist and compensate for external disturbances.

[0075] Combine Figure 3 , the closed-loop transfer function of the attitude angular acceleration loop is calculated by the following formula:

[0076]

[0077] where s represents the Laplace transform of the differential link, A(s) represents the motor dynamics transfer function, and I3 represents the third-order identity matrix.

[0078] Specifically, the above formula indicates that the transfer function of the angular acceleration loop is the same as that of the motor, and its response speed is also consistent with that of the motor. Further, since both the outer-loop angular velocity loop and the attitude angle loop are proportional controls and no other delay links are introduced, it can be seen that the controller proposed by this method has rapidity.

[0079] Step S4: Use the real-time measurement values of the motion variables of the multi-rotor UAV in the improved incremental nonlinear dynamic inverse controller to compensate for the total disturbance of the UAV system and enhance the disturbance rejection ability of the UAV control closed-loop;

[0080] Step S5: Optimize the improved incremental nonlinear dynamic inverse controller by using the closed-loop transfer function of the attitude angular acceleration loop and the disturbance closed-loop transfer function of the attitude angular acceleration loop;

[0081] Combine Figure 3 It can be deduced that the disturbance closed-loop transfer function of the attitude angular acceleration loop includes:

[0082]

[0083] where J -1 represents the inverse operation of the inertia tensor of the multi-rotor UAV.

[0084] It should be noted that as the motor dynamics transfer function, A(s) has a fast response speed and a steady-state gain of 1. Therefore, when the system is disturbed, the attitude angular acceleration will quickly converge to 0 over time.

[0085] Step S6: Use the optimized improved incremental nonlinear dynamic inverse controller to perform closed-loop control on the attitude of the multi-rotor UAV.

[0086] Therefore, according to the attitude dynamics model of the unmanned aerial vehicle (UAV), the present invention constructs an Incremental Nonlinear Dynamic Inversion (INDI) controller by introducing an incremental method, which reduces the dependence on the model, solves the problem of reduced control effectiveness when the model is inaccurate, optimizes the controller design, improves the disturbance rejection ability of the control loop, enhances the disturbance rejection ability of the control loop, and ensures that the UAV can quickly recover to the original state without instability after being disturbed. By using proportional control, the response speed of the controller is increased to ensure that the UAV can quickly and stably complete large maneuver flight tasks. The incremental form of the nonlinear dynamic inversion is derived, the attitude angular acceleration control is derived, and the attitude controller is constructed, enabling the controller to quickly and accurately respond to the given attitude command, with disturbance rejection ability at the same time, ensuring that the aircraft can quickly recover to the original state under severe disturbance and will not become unstable after being disturbed.

[0087] Furthermore, this technology will further expand the application fields of UAVs, such as tasks like rapid tracking and reconnaissance, pursuit and escape games, and maneuver confrontation strikes, further expanding and enriching system construction and promoting equipment iteration and multi-system collaborative development; for tasks such as UAV stunt flight performances and complex terrain exploration in civilian scenarios, it will further expand the development of the civilian UAV industry.

[0088] As a preferred example and preferred implementation, the UAV is based on DJI F450, equipped with a CUAV V5+ flight controller, T-MOTOR A2216 motors and their supporting 10-inch propellers, Air 20A electronic speed controllers, and a 4-cell lithium polymer battery. Taking the roll channel as an example, the mass and moment of inertia characteristics of the UAV, as well as the necessary parameters for attitude control, are shown in Table 1.

[0089] Table 1 Necessary parameters of the UAV required for the implementation of the present invention

[0090] Parameter Description Value m UAV mass 1.818 kg <![CDATA[J xx > Moment of inertia of the UAV about the x-axis of the airframe system <![CDATA[0.0156kg·m 2 > <![CDATA[P φ > Roll angle proportional control gain 4 <![CDATA[P p > Roll angular rate proportional control gain 15

[0091] To verify the effectiveness of the algorithm proposed in the present invention, a unit step attitude command is given, and the dynamic response situations of the method of the present invention and the NDI attitude solution method (control group) are compared, as Figure 4 and Figure 5 shown. It can be seen that the response curves of the two methods basically coincide, and both can respond to 95% within 1 s.

[0092] Furthermore, to verify the control effectiveness of the two methods when the aerodynamic model is inaccurate, the actual UAV aerodynamic moment model is respectively changed to 1.2 times and 0.8 times of the original, and compared with the original model, and the control effectiveness of the method of the present invention and the control group when the model changes is compared, as Figure 6 and Figure 7As shown in the figure. It can be seen that when the model changes, the control effectiveness of the NDI method drops significantly, making it impossible to accurately track the desired signal. However, the INDI method can still maintain the control accuracy when the model changes, and the dynamic response is basically unaffected.

[0093] To compare the disturbance rejection capabilities of the two controllers, the responses of the UAV under disturbed conditions are simulated. A step torque disturbance with a final value of 1 Kg·m is given, and the attitude and angular velocity responses of the two are compared, as Figure 8 and Figure 9 shown in the figure. It can be seen that for the NDI method, after being disturbed, its attitude angle cannot converge to 0, the angular rate amplitude is large, and the convergence is slow. In contrast, the attitude angle of the INDI method quickly converges to 0 within 1 s.

[0094] Therefore, aiming at the problems of the high model dependence of the existing flight control method designed based on the nonlinear dynamic inversion theory, which leads to a decrease in control effectiveness when the model is inaccurate, and the poor disturbance rejection ability, which causes the UAV to be prone to instability under disturbed conditions, the present invention introduces an incremental method. By reasonably transforming the aircraft dynamics model, an incremental nonlinear dynamic inversion (INDI) controller is constructed. While ensuring the response speed, the dependence of the method on the model is reduced, the problem of the decrease in control effectiveness when the whole aircraft aerodynamic model is inaccurate is solved, the disturbance rejection ability of the control loop is enhanced, and it is ensured that the UAV can quickly recover to the original state without instability after being disturbed. Finally, it is ensured that the UAV can quickly, stably, accurately, and robustly complete large maneuver flight tasks.

[0095] In summary, in the present invention, by introducing an incremental method and constructing an incremental nonlinear dynamic inversion (INDI) controller, the problem of the high model dependence of the existing flight control method in the prior art, which leads to a decrease in control effectiveness when the model is inaccurate, is solved, thereby reducing the dependence on the model and solving the problem of the decrease in control effectiveness when the model is inaccurate; by optimizing the controller, the disturbance rejection ability of the control loop is improved, and the technical problem that the existing flight control method has poor disturbance rejection ability and causes the UAV to be prone to instability is solved, achieving the enhancement of the disturbance rejection ability of the control loop and ensuring that the UAV can quickly recover to the original state without instability after being disturbed; by adopting proportional control, the response speed of the controller is increased, and the problem that the existing flight control method has a slow response speed and affects the execution of large maneuver flight tasks by the UAV is solved, thereby being able to achieve the purpose of ensuring that the UAV can quickly and stably complete large maneuver flight tasks.

[0096] It should be noted that in this text, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. In addition, in this text, "front", "rear", "left", "right", "upper" and "lower" are all referenced with respect to the placement state shown in the drawings.

[0097] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. And these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A disturbance rejection flight control method for a multi-rotor unmanned aerial vehicle, characterized in that, Including: Based on the theorem of angular momentum and Euler's equation, establish the attitude dynamics equation of a multi-rotor UAV; Set the control torque in the attitude dynamics equation as the dependent variable and perform a first-order Taylor expansion at an arbitrary point; Add the expectation to the expanded dynamics equation to obtain an improved incremental nonlinear dynamic inversion controller; Use the real-time measured values of the motion variables of the multi-rotor UAV in the improved incremental nonlinear dynamic inversion controller to compensate for the total disturbance of the UAV system; Optimize the improved incremental nonlinear dynamic inversion controller using the control closed-loop transfer function of the attitude angular acceleration loop and the disturbance closed-loop transfer function of the attitude angular acceleration loop; Use the optimized improved incremental nonlinear dynamic inversion controller to perform closed-loop control on the attitude of the multi-rotor UAV.

2. The anti-disturbance flight control method for a multi-rotor unmanned aerial vehicle according to claim 1, characterized in that The attitude dynamics equation of the multi-rotor UAV includes: where \(J = \text{diag}([J xx J yy J zz )\) represents the inertia tensor of the multi-rotor UAV; represents the attitude angular acceleration of the multi-rotor UAV; \(\omega=[p\ q\ r] T \), represents the attitude angular velocity of the multi-rotor UAV; represents the control torque of the multi-rotor UAV; \(M ext = M a +d\), represents other torques of the multi-rotor UAV except the control torque, including the aerodynamic torque \(M a and the disturbance torque \(d\).

3. The multi-rotor UAV disturbance rejection flight control method according to claim 1, characterized in that, Setting the control torque in the attitude dynamics equation of the multi-rotor UAV as the dependent variable and performing a first-order Taylor expansion at an arbitrary point includes: Among them, represents the partial derivative operation, represents the control torque at the current moment, represents the attitude angular acceleration at the current moment, and ω0 represents the attitude angular velocity at the current moment, represents the other torques at the current moment, O 2 represents the high-order remainder of the first-order Taylor expansion.

4. The multi-rotor UAV disturbance rejection flight control method according to claim 1, characterized in that Setting the control torque in the attitude dynamics equation of the multi-rotor UAV as the dependent variable, performing a first-order Taylor expansion at an arbitrary point, and obtaining an improved incremental nonlinear dynamic inversion controller after adding the expectation includes: Among them, represents the desired control torque of the multi-rotor UAV, represents the control torque estimated through the functional relationship g(Ω) between the control torque of the multi-rotor UAV and the motor speed of the multi-rotor UAV, where Ω is the vector composed of the motor speeds of each multi-rotor UAV, represents the desired attitude angular acceleration of the multi-rotor UAV.

5. The anti-disturbance flight control method for a multi-rotor unmanned aerial vehicle according to claim 1, characterized in that, The control closed-loop transfer function of the attitude angular acceleration loop is obtained by calculating through the following formula: Where s represents the Laplace transform of the differential link, A(s) represents the motor dynamics transfer function, and I3 represents the third-order identity matrix.

6. The anti-disturbance flight control method for a multi-rotor unmanned aerial vehicle according to claim 1, wherein, The disturbance closed-loop transfer function of the attitude angular acceleration loop includes: Among them, J -1 represents the inverse operation of the inertia tensor of the multi-rotor UAV.

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