Unmanned aerial vehicle rapid composite control method and system based on fuzzy asymptotic

Abstract

The invention belongs to the technical field of unmanned aerial vehicle flight control, and discloses an unmanned aerial vehicle rapid composite control method and system based on fuzzy asymptotic. In the aspect of steady-state precision, the method breaks through the inherent limitation that traditional finite time control can only achieve bounded neighborhood convergence, and the reliability and execution capacity of the system in a high-precision task are remarkably improved. In the aspect of transient response, a nonlinear feedback mechanism in a fractional power form is introduced, so that the system can obtain a super-linear convergence rate in an initial stage or when encountering large-deviation disturbance. In terms of robustness and realizability, a structure combining low-pass filtering and dynamic error compensation is adopted in the method, and implementation difficulty caused by high-order differential calculation is effectively avoided; meanwhile, parameter perturbation, unmodeled dynamics, external wind disturbance and the like are estimated and compensated in combination with an adaptive fuzzy system, and the adaptive capacity and anti-interference performance of the system in a strong-coupling and nonlinear environment are remarkably enhanced.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) flight control technology, specifically relating to a rapid composite control method and system for UAVs based on fuzzy asymptotics. Background Technology

[0002] With its inherent advantages such as vertical take-off and landing capability, simple structure and low operating cost, quadcopter drones have become a core technology in the civilian and scientific fields, and are widely used in logistics distribution, air transport, infrastructure inspection and crop protection.

[0003] Among the key technologies ensuring the reliable operation of quadcopter drones, trajectory tracking control is a fundamental prerequisite. Its performance directly determines whether the drone can accurately track a predetermined path or a real-time reference trajectory, thereby ensuring the successful completion of various tasks. However, actual drone operation is limited by its inherent system limitations and environmental influences, such as parameter uncertainties, continuous external disturbances, underactuated characteristics, and strong coupling between position and attitude dynamics. These factors can significantly reduce tracking performance and may even lead to system instability. Therefore, designing a high-performance tracking controller is crucial for ensuring the stable operation of drones.

[0004] Various control strategies have been developed to address the trajectory tracking control problem of quadrotor unmanned aerial vehicles (UAVs). Among traditional methods, linear control is widely used, with PID control being the most representative. More advanced control strategies have also been extensively studied, including sliding mode control, adaptive backstepping control, neural network control, and robust control. Backstepping control, as a recursive design technique, has received widespread attention due to its stability guarantees and adaptive capabilities for nonlinear systems.

[0005] However, most advanced control strategies, including finite-time control methods, can typically only guarantee that the tracking error converges to a bounded neighborhood of the origin within a finite time, and cannot achieve asymptotic convergence to zero. Although this neighborhood can be made sufficiently small by adjusting parameters, a steady-state error still theoretically exists. For applications requiring absolute zero steady-state error, such as agile formation flying and precision inspection, this limitation is unacceptable. While asymptotic tracking control methods can guarantee that the error eventually converges to zero, their transient response is often slow.

[0006] Therefore, there is an urgent need for a comprehensive quadcopter UAV control scheme that can combine the characteristics of rapid convergence in finite time with asymptotically zero error steady-state performance. Summary of the Invention

[0007] The purpose of this invention is to propose a fast composite control method for unmanned aerial vehicles (UAVs) based on fuzzy asymptotics, in order to solve the altitude and attitude tracking control problem of UAVs under the conditions of external disturbances and system uncertainties. This method, by designing an adaptive update law, can theoretically guarantee the asymptotic convergence of tracking error while ensuring that the system has a fast transient response capability.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: A fast composite control method for unmanned aerial vehicles based on fuzzy asymptotics includes the following steps: Step 1. Establish a dynamic model of the UAV with parameter uncertainties and external disturbances; Step 2. Based on the UAV dynamics model established in Step 1, construct a fuzzy asymptotic fast composite controller for the UAV under conditions of parameter uncertainty and external disturbance; Step 3. Use the fuzzy asymptotic fast composite controller constructed in Step 2 to achieve trajectory tracking control of the UAV.

[0009] Furthermore, based on the fuzzy asymptotic-based rapid composite control method for UAVs, this invention also proposes a corresponding fuzzy asymptotic-based rapid composite control system for UAVs, the technical solution of which is as follows: A rapid composite control system for unmanned aerial vehicles (UAVs) based on fuzzy asymptotics includes: The dynamics modeling module is used to establish dynamic models of UAVs with parameter uncertainties and external disturbances; The controller construction module is used to build a fuzzy asymptotic fast composite controller for the UAV under conditions of parameter uncertainty and external disturbance, based on the UAV dynamic model established by the dynamic modeling module. And a tracking control module, which uses a fuzzy asymptotic fast composite controller built with the controller building module to achieve trajectory tracking control of the UAV.

[0010] Furthermore, based on the aforementioned rapid composite control method for UAVs based on fuzzy asymptotics, this invention also proposes a computer device, which includes a memory and one or more processors. The memory stores executable code, and when the processor executes the executable code, it implements the steps of the above-mentioned fast composite control method for UAVs based on fuzzy asymptotes.

[0011] The present invention has the following advantages: As described above, this invention relates to a fast composite control method for unmanned aerial vehicles based on fuzzy asymptotics. The core objective of this method is to strictly ensure that the tracking error eventually converges asymptotically to zero over time in complex environments with uncertainties in model parameters and external disturbances, while also taking into account fast transient response capabilities.

[0012] In terms of steady-state accuracy, the method of this invention breaks through the inherent limitation of traditional finite-time control, which can only achieve convergence in a bounded neighborhood. Through rigorous stability design and analysis, it achieves asymptotic zeroing of tracking error, theoretically eliminating steady-state deviation and significantly improving the reliability and execution capability of the system in high-precision tasks such as precision inspection and formation coordination.

[0013] In terms of transient response, the method of this invention introduces a nonlinear feedback mechanism in the form of fractional powers, which enables the system to obtain a superlinear convergence rate in the initial stage or when encountering large deviation disturbances. Its dynamic response speed is significantly better than that of traditional asymptotically stable controllers, effectively balancing the dual requirements of speed and accuracy.

[0014] In terms of robustness and feasibility, the method of this invention adopts a structure that combines low-pass filtering with dynamic error compensation, which effectively avoids the implementation difficulties caused by high-order differential calculations. At the same time, by combining an adaptive fuzzy system to estimate and compensate for parameter perturbations, unmodeled dynamics and external wind disturbances, the system's adaptive capability and anti-interference performance in strongly coupled and nonlinear environments are significantly enhanced.

[0015] In summary, the method of this invention, by taking asymptotic tracking to zero as its core objective and organically integrating rapid convergence drive, uncertainty compensation, and engineering feasibility design, provides UAVs with a trajectory tracking control solution that combines high precision, high dynamics, and strong robustness. Attached Figure Description

[0016] Figure 1 This is a flowchart of a rapid composite control method for unmanned aerial vehicles based on fuzzy asymptotics, as described in an embodiment of the present invention.

[0017] Figure 2 This is a control block diagram of a fast composite control method for unmanned aerial vehicles based on fuzzy asymptotics in an embodiment of the present invention.

[0018] Figure 3 This is a trajectory curve showing the difference between the drone's altitude and the desired altitude obtained in the simulation of this invention.

[0019] Figure 4 This is a graph showing the altitude tracking error of the UAV obtained in the simulation of this invention.

[0020] Figure 5 This is a trajectory curve of the UAV's roll angle and the desired roll angle obtained in the simulation of this invention.

[0021] Figure 6 This is a graph showing the roll angle tracking error of the UAV obtained in the simulation of this invention.

[0022] Figure 7 This is a trajectory curve of the UAV's pitch angle versus the desired pitch angle obtained in the simulation of this invention.

[0023] Figure 8 This is a graph showing the pitch angle tracking error of the UAV obtained in the simulation of this invention.

[0024] Figure 9 This is a trajectory curve of the UAV's yaw angle and the desired yaw angle obtained in the simulation of this invention.

[0025] Figure 10 This is a graph showing the yaw angle error of the UAV obtained in the simulation of this invention.

[0026] Figure 11 This is a trajectory curve showing the difference between the drone's altitude and the desired altitude obtained in the experiment of this invention.

[0027] Figure 12 This is a graph showing the altitude tracking error of the UAV obtained in the experiment of this invention. Detailed Implementation

[0028] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments: Example 1 This invention proposes a fast composite control method for unmanned aerial vehicles (UAVs) based on fuzzy asymptotics. This method is applicable to flight environments with parameter uncertainties and external disturbances, and can achieve high-precision, finite-time asymptotically stable trajectory tracking control.

[0029] First, existing quadcopter UAV trajectory tracking control methods can only guarantee that the tracking error is bounded and confined to a neighborhood near the origin, failing to achieve zero-error tracking performance. Although this neighborhood can be made sufficiently small through parameter adjustment, the tracking error itself does not asymptotically converge to zero. To meet the practical engineering requirements of high-precision flight missions, this invention introduces an asymptotic tracking control scheme into the UAV controller design. The advantage of the asymptotic convergence control method is that it ensures that the tracking error gradually decreases over time and eventually asymptotically converges to zero.

[0030] Secondly, in practical applications such as agile formation flying and high-precision industrial inspection, quadcopter UAV systems not only require strict steady-state accuracy but also rapid transient response. To overcome these limitations, this invention also introduces a finite-time strategy into the UAV controller design. The finite-time control strategy, based on fractional-power virtual control signals, improves convergence performance by accelerating the system's state convergence away from the equilibrium point, thereby significantly shortening the settling time and enhancing anti-interference capabilities.

[0031] Specifically, Embodiment 1 describes a fuzzy asymptotic fast composite control method for a quadrotor UAV. This method, during the altitude and attitude tracking process of the quadrotor UAV, utilizes steps such as designing virtual control signals, low-pass filters, error compensation systems, and actual control signals to ensure that the tracking errors of the UAV's altitude and attitude subsystems converge to zero within a finite time. This invention not only ensures that the tracking error asymptotically converges to zero but also improves the transient performance of quadrotor UAV trajectory tracking under external disturbances and system uncertainties, and all signals in the closed-loop system are bounded.

[0032] The control method proposed in this invention will be further described below.

[0033] like Figure 1 As shown, the fast composite control method for UAVs based on fuzzy asymptotics specifically includes the following steps: Step 1. Establish a dynamic model of the quadcopter UAV with parameter uncertainties and external disturbances. The quadcopter UAV dynamic model includes an altitude subsystem and an attitude subsystem, where the attitude subsystem includes a roll subsystem, a pitch subsystem, and a yaw subsystem.

[0034] In this embodiment, step 1 specifically includes: The dynamic model of a UAV with parameter uncertainties and external disturbances, i.e., the continuous-time system model of a UAV, is defined as follows: (1) in, Indicates the altitude of the quadcopter drone; , , These represent the roll angle, pitch angle, and yaw angle of a quadcopter drone, respectively. Indicates the mass of the drone; This represents the control input of the altitude subsystem. This indicates the control input of the motor in the roll subsystem. This represents the control input of the pitch subsystem. This represents the control input to the yaw subsystem; g represents gravitational acceleration. This represents the air drag coefficient of the altitude subsystem. This represents the air drag coefficient of the roll subsystem. This represents the air drag coefficient of the pitch subsystem. This represents the air resistance coefficient of the yaw subsystem; Describes the external perturbation function of the altitude subsystem. This represents the external disturbance function of the roll subsystem. This represents the external disturbance function of the pitch subsystem. This represents the external disturbance function of the yaw subsystem; This represents the distance from the motor of the quadcopter drone to its center of mass; , , These represent the moments of inertia of the roll, pitch, and yaw axes, respectively.

[0035] make , , , , , , , , , , , Then the continuous-time system model of the quadcopter UAV shown in formula (1) can be rewritten as: (2) in, , , , Indicates the actual control signal. , , , This indicates an external disturbance.

[0036] , , , All of these are computable parts of the system model. , , , .

[0037] , , , It refers to the unknown part in the system model. , , , .

[0038] Step 2. Based on the quadrotor UAV dynamics model established in Step 1, construct a fuzzy asymptotic fast composite controller for the quadrotor UAV under conditions of parameter uncertainty and external disturbance. Here, parameter uncertainty refers to system uncertainty.

[0039] Step 2, the process of constructing a fuzzy asymptotic fast composite controller for the UAV under conditions of parameter uncertainty and external disturbances, is as follows: In this embodiment, the fuzzy asymptotic fast composite controller includes a height controller and an attitude controller. Step 2.1 is the construction process of the height controller, and step 2.2 is the construction process of the attitude controller.

[0040] Step 2.1. For the height subsystem, design a virtual control signal containing fractional power feedback terms to accelerate the convergence speed of the system when it is far from the equilibrium point. Introduce a low-pass filter to process the virtual control signal to avoid the computational burden caused by high-order derivatives, and simultaneously design a dynamic compensation mechanism to offset the deviation introduced by the filter. Use a fuzzy logic system to approximate the unknown nonlinear dynamics and external disturbances in the height subsystem, and design an adaptive update law to adjust the parameters of the fuzzy system.

[0041] Step 2.1 specifically involves: A low-pass filter, as shown in formula (3), is introduced to process the virtual control signal: (3) in, This represents the output signal of the filter; It is used to represent the four subsystems of a quadcopter drone. The corresponding altitude subsystem, When corresponding to the roll subsystem, The corresponding pitch subsystem The corresponding yaw subsystem; ; Indicates the intermediate values ​​and driving signals of the filter; and For the gain of the filter, and It is a positive number; This refers to a virtual control signal that serves as an input signal.

[0042] A dynamic compensation mechanism is designed synchronously, and the tracking error of the compensation is defined. for: (4) in, To track errors, This is a dynamic compensation signal.

[0043] Tracking error is defined as: (5) in, The first subsystem representing the height subsystem One state, This represents the desired signal of the height subsystem. This represents the virtual expectation signal of the height subsystem.

[0044] Define virtual control signals for: (6) in, Design parameters representing feedback gain; , , It is a positive number, and ; Indicates a time-varying signal. , For positive integers, The time variable represents the dynamic evolution of the system.

[0045] Define actual control signals for: (7) in, This represents the influence coefficient of the control input; Design parameters representing feedback gain; and For positive integers, Define intermediate values; for: (8) in, and They represent the adaptive update law respectively. and The estimated value.

[0046] ,in Represents the basis functions.

[0047] definition , , , .

[0048] in, Represents the weight vector. For positive integers, Represents the adaptive update law The estimation error, Represents the adaptive update law The estimation error.

[0049] For any preset constant , Employing a fuzzy logic system For functions Approximate it to satisfy ,in .

[0050] Using Young's inequality, we obtain: (9) in, , to Representing the weight vectors respectively The first to the first Each element.

[0051] basis functions Right now Represented as: .

[0052] in, , Indicates the number of elements. Describing basis functions The component vectors in the vector.

[0053] The dynamic compensation signal is defined as: (10) Choose the Lyapunov function for: (11) Lyapunov function The derivative is expressed as: (12) virtual control signal and dynamic compensation signal Substituting into formula (12), we get: (13) Choose the Lyapunov function for: (14) in, and For parameters.

[0054] Lyapunov function The derivative is expressed as: (15) Using fuzzy logic control for approximation, it can be rewritten as: (16) Substituting formula (16) into formula (15), we get: (17) control signal Substituting into formula (17), formula (15) is rewritten as follows: (18) The adaptive update laws are designed as follows: (19) (20) Therefore, we get: (twenty one) Therefore, the expression for the altitude controller in the constructed fuzzy asymptotic fast composite controller for UAVs under conditions of existence parameter uncertainty and external disturbance is as follows: .

[0055] Step 2.2. For the attitude subsystem, a similar design approach as the altitude subsystem is adopted to construct the corresponding desired reference signal, filtering stage, and dynamic compensation mechanism. Specifically, a virtual control signal containing a fractional power feedback term is designed. A low-pass filter is introduced to process the virtual control signal, and a dynamic compensation mechanism is designed simultaneously. A fuzzy logic system is used to approximate the unknown nonlinear dynamics and external disturbances in the attitude subsystem, and an adaptive update law is designed to adjust the parameters of the fuzzy system.

[0056] Step 2.2 specifically involves: This corresponds to the first subsystem, namely the altitude subsystem. This corresponds to the second subsystem, namely the roll subsystem. This corresponds to the third subsystem, namely the pitch subsystem. This corresponds to the fourth subsystem, the yaw subsystem; in step 2.2 The range of values ​​is .

[0057] Define the tracking error to be compensated for: (twenty two) in, To track errors, This is a dynamic compensation signal.

[0058] Tracking error is defined as: (twenty three) in, Indicates the first The first subsystem One state, and The first The expected signal and the virtual expected signal of each subsystem.

[0059] Define virtual control signals for: (twenty four) in, Design parameters representing feedback gain; and It is a positive number; This indicates a time-varying signal.

[0060] Define actual control signals for: (25) in, This represents the influence coefficient of the control input; Design parameters representing feedback gain; , For positive integers, Define intermediate values; for: (26) in, and Represents the adaptive update law and The estimated value.

[0061] definition ,in Represents the basis functions.

[0062] definition , , , .

[0063] in, Represents the weight vector. For positive integers, Represents the adaptive update law The estimation error, Represents the adaptive update law The estimation error.

[0064] The dynamic compensation signal is defined as: (27) Choose the Lyapunov function for: (28) Lyapunov function The derivative is expressed as: (29) virtual control signal and dynamic compensation signal Substituting into formula (29), we get: (30) Choose the Lyapunov function for: (31) in, and For parameters.

[0065] Lyapunov function The derivative is expressed as: (32) Using fuzzy logic control for approximation, it can be rewritten as: (33) Substituting formula (33) into formula (32), we get: (34) control signal and dynamic compensation signal Substituting into formula (34), we get: (35) The adaptive update laws are designed as follows: (36) (37) Therefore, we get: (38) Therefore, the expression for the attitude controller in the constructed fuzzy asymptotic fast composite controller for the UAV under conditions of existence parameter uncertainty and external disturbance is as follows: .

[0066] In step 2 of this embodiment, after completing the design of the fuzzy asymptotic fast composite controller, a stability analysis is also performed on the UAV controlled by the fuzzy asymptotic fast composite controller. That is, the stability analysis of the constructed fuzzy asymptotic fast composite controller is performed to prove that the system state is bounded in a finite time and that the tracking error eventually converges asymptotically to zero.

[0067] In this embodiment, the stability analysis process is as follows: Step 2.3. Construct a composite Lyapunov function and prove that all signals in the closed-loop system remain bounded in finite time, and that the tracking error can converge to an adjustable set in finite time.

[0068] Step 2.3 specifically involves: Choose the Lyapunov function for: (39) Lyapunov function The derivative of is expressed as: (40) make ,in , ,but Established.

[0069] in, , , .

[0070] Substituting the relation into formula (40), we get: (41) in, .

[0071] .

[0072] Choose the Lyapunov function for: (42) Lyapunov function The first derivative is: (43) According to the boundedness condition ,in Given positive integers, we get: (44) in, , ; The parameter is , and satisfies .

[0073] Dynamic compensation signal Able to complete within a limited time Converging to set : .

[0074] Convergence time for: .

[0075] in, It is a positive number; It is a positive number, and ; Representing Lyapunov functions The initial value.

[0076] Lyapunov function Able to do within a limited time Converging inward to the set : .

[0077] in, express Lyapunov function at time t The value; .

[0078] .

[0079] according to , ,as well as and The boundedness of, obtain and Bounded.

[0080] Compensated tracking error Able to converge to the set in a finite time : .

[0081] According to the relation , It is stable over a finite time and satisfies .

[0082] That is, tracking error In a limited time Converging inward to the set : .

[0083] in, .

[0084] Step 2.4. Based on Step 2.3, using Barbalat's lemma, it is further proved that when time approaches infinity, the tracking error and its derivative both converge asymptotically to zero, thus achieving true asymptotic tracking performance.

[0085] Step 2.4 specifically involves: In the interval Integrating equation (41) yields the expression shown in equation (45): (45) in, express Lyapunov function at time t The value of the Lyapunov function The initial value is ; This indicates a dynamic compensation signal.

[0086] According to formula (45), the inequality shown in formula (46) holds: (46) Based on the convergence analysis of Barbalat's lemma, we obtain: (47) In the interval Integrating equation (44) yields the expression shown in equation (48): (48) According to formula (48), the inequality shown in formula (49) holds: (49) Based on the convergence analysis of Barbalat's lemma, we obtain: (50) Combination Relation as well as asymptotic convergence.

[0087] Obtain the tracking error signal That is, as time approaches infinity, the tracking error and its derivative asymptotically converge to zero.

[0088] Step 3. Use the fuzzy asymptotic fast composite controller constructed in Step 2 to achieve trajectory tracking control of the quadcopter UAV; that is, use the fuzzy asymptotic fast composite controller to achieve high-precision tracking control of the quadcopter UAV's altitude and attitude to the desired trajectory.

[0089] like Figure 2 As shown, using the QBall2 experimental platform, the program first calculates real-time data for virtual control signals, fuzzy logic control signals, and adaptive control signals using the attitude and altitude data transmitted back by the quadcopter UAV. Then, using the virtual control signal as input to a filter, the desired velocity signal is calculated, and the control signal is derived. The calculated control signal is then transmitted to the quadcopter UAV to achieve control.

[0090] The control method of this invention not only has excellent anti-interference ability, but also has strong practicality. This method not only improves the tracking accuracy of quadcopter UAVs, enabling the tracking error to approach zero within a finite time, but also accelerates the state convergence of the system when it is far from the equilibrium point by establishing a fractional power virtual control signal, thereby improving the convergence performance, significantly shortening the settling time and enhancing robustness.

[0091] In addition, to verify the effectiveness of the method proposed in this invention, the following specific experiments are also provided: In the simulation, the airframe parameters of the quadcopter UAV are shown in Table 1. The parameter selection in Table 1 corresponds to... Figures 3 to 12 .

[0092] Table 1. Airframe parameters of the quadcopter UAV

[0093] External disturbances are defined as: .

[0094] The air drag coefficient is defined as: .

[0095] The initial state is defined as: [ =[0,0,0,0].

[0096] in, , , , These represent the initial altitude, roll angle, pitch angle, and yaw angle of the drone, respectively.

[0097] The desired signal is defined as: , , , .

[0098] The parameter selection for the controller in the control method is shown in Table 2. The parameter selection in Table 2 corresponds to... Figures 3 to 10 .

[0099] Table 2 Parameter Selection of Controller in Control Method

[0100] In the experiment, the initial state is defined as: [ ]=[0].

[0101] Expectation height Designed as follows: .

[0102] The parameter selection for the controller in the control method is shown in Table 3. The parameter selection in Table 3 corresponds to... Figure 11 and Figure 12 .

[0103] Table 3 Parameter Selection of Controller in Control Method

[0104] The UAV was controlled using the methods of this invention, namely the CFBFTAFAT method, the asymptotic tracking control method (CFBAFAT method), and the finite-time control method (CFBFTAF method). The simulation results are as follows: Figures 3 to 10 As shown; using the fuzzy asymptotic fast composite control method of the present invention, the experimental results are as follows. Figure 11 and Figure 12 As shown; where the roll angle is the same as the lateral roll angle. To track the desired trajectory, The tracking trajectories are obtained from simulations using different algorithms. For the tracking error of the altitude subsystem, For the tracking error of the roll subsystem, For pitch subsystem tracking error, This refers to the tracking error of the yaw subsystem.

[0105] Figure 3 , Figure 5 , Figure 7 , Figure 9 as well as Figure 11 The curves show that the quadcopter UAV controlled by the method of the present invention can quickly and well complete the desired altitude and attitude tracking control.

[0106] To further verify the effectiveness of the method of the present invention, its performance was compared with that of the asymptotic tracking control method and the finite-time control method. Altitude and attitude errors were selected for comparison, specifically including altitude tracking error, roll angle tracking error, pitch angle tracking error, and yaw angle tracking error, as shown below. Figure 4 , Figure 6 , Figure 8 , Figure 10 and Figure 12 As shown.

[0107] The comparative results show that the control method proposed in this invention can achieve better convergence speed and control accuracy.

[0108] This invention addresses the challenges of model parameter uncertainty and unknown external disturbances commonly encountered in actual flight. It proposes a composite control method that integrates a fractional power nonlinear feedback mechanism, fuzzy adaptive approximation technology, and an error compensation strategy. Specifically, the fractional power feedback term effectively improves the system's convergence speed; the fuzzy logic system estimates and compensates for system uncertainties; and the error compensation mechanism, in conjunction with the low-pass filtering stage, effectively suppresses the performance loss introduced by filtering while avoiding high-order differential calculations. Crucially, this invention employs rigorous two-stage stability analysis. First, based on Lyapunov theory, it proves that the closed-loop system state is bounded within a finite time and that the tracking error converges to an adjustable set. Then, using Barbalat's lemma, it rigorously proves that the tracking error asymptotically converges to zero as time approaches infinity, fundamentally overcoming the inherent limitation of traditional finite-time control methods that can only achieve neighborhood convergence. The proposed control method not only possesses rapid transient response capabilities but also achieves theoretically zero steady-state tracking error, significantly improving the trajectory tracking accuracy, robustness, and mission reliability of quadcopter UAVs in complex disturbance environments, providing solid theoretical support for high-precision autonomous flight.

[0109] The method of the present invention has the following advantages: First, it achieves true asymptotic tracking. The method of this invention breaks through the limitation that traditional finite-time control can only achieve convergence in a bounded neighborhood. Through convergence analysis based on Barbalat's lemma, it is rigorously proven that the tracking error can eventually converge asymptotically to zero over time, completely eliminating the steady-state error.

[0110] Secondly, it also has a fast transient response. By introducing a fractional power feedback mechanism, the method of this invention can achieve a convergence speed that far exceeds that of traditional linear or asymptotic control methods in the initial stage or when the system is subjected to large disturbances and deviates from the equilibrium point.

[0111] Thirdly, it has strong robustness and practicality. The method of this invention effectively handles model uncertainty and external disturbances through a fuzzy adaptive mechanism, and reduces the implementation complexity of the controller through a dynamic compensation strategy, making it easier to apply in engineering.

[0112] Example 2 This embodiment 2 describes a rapid composite control system for unmanned aerial vehicles (UAVs) based on fuzzy asymptotes. This system is based on the same inventive concept as the rapid composite control method for UAVs based on fuzzy asymptotes in embodiment 1.

[0113] Specifically, the rapid composite control system for unmanned aerial vehicles based on fuzzy asymptotics includes the following modules: The dynamics modeling module is used to build dynamic models of UAVs with parameter uncertainties and external disturbances.

[0114] The controller construction module is used to build a fuzzy asymptotic fast composite controller for the UAV under conditions of parameter uncertainty and external disturbances, based on the UAV dynamic model established by the dynamic modeling module.

[0115] In this embodiment, the controller construction module is specifically used to construct a fast guidance term, a dynamic compensation term, and an uncertainty estimation compensation term, and to generate motor control commands to drive the quadcopter UAV, thereby ensuring that the closed-loop system not only enters the steady-state neighborhood within a finite time, but also continuously reduces the tracking error thereafter, ultimately achieving... Its asymptotic tracking performance.

[0116] And a tracking control module, which uses a fuzzy asymptotic fast composite controller built with the controller building module to achieve trajectory tracking control of the UAV.

[0117] In this embodiment, the fuzzy asymptotic-based UAV fast composite control system also includes a stability analysis module, which is used to perform stability analysis on the UAV controlled by the fuzzy asymptotic fast composite controller.

[0118] It should be noted that the implementation process of the functions and roles of each functional module in the rapid composite control system for unmanned aerial vehicles based on fuzzy asymptotics is detailed in the implementation process of the corresponding steps in the method of Example 1, and will not be repeated here.

[0119] Example 3 This embodiment 3 describes a computer device that includes a memory and one or more processors.

[0120] The memory stores executable code, which, when executed by the processor, is used to implement the steps of the rapid composite control method for UAVs based on fuzzy asymptotics in Embodiment 1 above.

[0121] In this embodiment, the computer device can be any device or apparatus with data processing capabilities, and will not be described in detail here.

[0122] Of course, the above description is only a preferred embodiment of the present invention. The present invention is not limited to the above-described embodiments. It should be noted that any equivalent substitutions or obvious modifications made by those skilled in the art under the guidance of this specification fall within the scope of this specification and should be protected by the present invention.

Claims

1. A fast composite control method for unmanned aerial vehicles based on fuzzy asymptotics, characterized in that, Includes the following steps: Step 1. Establish a dynamic model of the UAV with parameter uncertainties and external disturbances; Step 2. Based on the UAV dynamics model established in Step 1, construct a fuzzy asymptotic fast composite controller for the UAV under conditions of parameter uncertainty and external disturbance; Step 2 specifically involves: Step 2.

1. For the height subsystem, design a virtual control signal containing a fractional power feedback term; introduce a low-pass filter to process the virtual control signal, and design a dynamic compensation mechanism simultaneously; use a fuzzy logic system to approximate the unknown nonlinear dynamics and external disturbances in the height subsystem, and design an adaptive update law to adjust the parameters of the fuzzy system. Step 2.

2. For the attitude subsystem, design a virtual control signal containing a fractional power feedback term; introduce a low-pass filter to process the virtual control signal, and design a dynamic compensation mechanism simultaneously; use a fuzzy logic system to approximate the unknown nonlinear dynamics and external disturbances in the attitude subsystem, and design an adaptive update law to adjust the parameters of the fuzzy system. Step 3. Use the fuzzy asymptotic fast composite controller constructed in Step 2 to achieve trajectory tracking control of the UAV.

2. The fast composite control method for unmanned aerial vehicles based on fuzzy asymptotics according to claim 1, characterized in that, Step 1 specifically involves: The dynamic model of a UAV with parameter uncertainties and external disturbances, i.e., the continuous-time system model of a UAV, is defined as follows: (1) in, Indicates the altitude of the drone; , , These represent the roll angle, pitch angle, and yaw angle of the UAV, respectively. Indicates the mass of the drone; This represents the control input of the altitude subsystem. This represents the control input of the roll subsystem. This represents the control input of the pitch subsystem. This represents the control input to the yaw subsystem; g represents gravitational acceleration. This represents the air drag coefficient of the altitude subsystem. This represents the air drag coefficient of the roll subsystem. This represents the air drag coefficient of the pitch subsystem. This represents the air resistance coefficient of the yaw subsystem; Describes the external perturbation function of the altitude subsystem. This represents the external disturbance function of the roll subsystem. This represents the external disturbance function of the pitch subsystem. This represents the external disturbance function of the yaw subsystem; This represents the distance from the drone's motor to its center of mass. , , These represent the moments of inertia of the roll, pitch, and yaw axes, respectively. make , , , , , , , , , , , Then the UAV continuous-time system model shown in formula (1) can be rewritten as: (2) in, , , , Indicates the actual control signal. , , , Indicates external disturbance; ， ， ， ； ， ， ， 。 3. The rapid composite control method for unmanned aerial vehicles based on fuzzy asymptotics according to claim 2, characterized in that, Step 2.1 specifically involves: A low-pass filter, as shown in formula (3), is introduced to process the virtual control signal: (3) in, This represents the output signal of the filter. , ; Indicates the intermediate values ​​and driving signals of the filter; and For the gain of the filter, and It is a positive number; This refers to a virtual control signal that serves as an input signal. A dynamic compensation mechanism is designed synchronously, and the tracking error of the compensation is defined. for: (4) in, To track errors, For dynamic compensation signals; Tracking error is defined as: (5) in, The first subsystem representing the height subsystem One state, This represents the desired signal of the height subsystem. This represents the virtual expected signal of the height subsystem; Define virtual control signals for: (6) in, Design parameters representing feedback gain; , , It is a positive number, and ; Indicates a time-varying signal. , For positive integers, The time variable represents the dynamic evolution of the system; Define actual control signals for: (7) in, This represents the influence coefficient of the control input; Design parameters representing feedback gain; and For positive integers, Define intermediate values; for: (8) in, and They represent the adaptive update law respectively. and The estimated value; ,in Describe the basis functions; definition , , , ; in, Represents the weight vector. For positive integers, Represents the adaptive update law The estimation error, Represents the adaptive update law The estimation error; For any preset constant , Employing a fuzzy logic system For functions Approximate it to satisfy ,in ; Using Young's inequality, we obtain: (9) in, , to Representing the weight vectors respectively The first to the first One element; basis functions Right now Represented as: ； in, , Indicates the number of elements. Describing basis functions The subvectors in; The dynamic compensation signal is defined as: (10) Choose the Lyapunov function for: (11) Lyapunov function The derivative is expressed as: (12) virtual control signal and dynamic compensation signal Substituting into formula (12), we get: (13) Choose the Lyapunov function for: (14) in, and For parameters; Lyapunov function The derivative is expressed as: (15) Using fuzzy logic control for approximation, it can be rewritten as: (16) Substituting formula (16) into formula (15), we get: (17) control signal Substituting into formula (17), formula (15) is rewritten as follows: (18) The adaptive update laws are designed as follows: (19) (20) Therefore, we get: (21) Therefore, the expression for the altitude controller of the UAV under conditions of parameter uncertainty and external disturbance is: 。 4. The rapid composite control method for unmanned aerial vehicles based on fuzzy asymptotics according to claim 3, characterized in that, Step 2.2 specifically involves: Define the tracking error to be compensated for: (22) in, To track errors, For dynamic compensation signals; Tracking error is defined as: (23) in, The second subsystem is the roll subsystem, the third subsystem is the pitch subsystem, and the fourth subsystem is the yaw subsystem; Indicates the first The first subsystem One state, and The first The expected signal and virtual expected signal of each subsystem; Define virtual control signals for: (24) in, Design parameters representing feedback gain; and It is a positive number; Indicates a time-varying signal; Define actual control signals for: (25) in, This represents the influence coefficient of the control input; Design parameters representing feedback gain; , For positive integers, Define intermediate values; for: (26) in, and Represents the adaptive update law and The estimated value; definition ,in Describe the basis functions; definition , , , ; in, Represents the weight vector. For positive integers, Represents the adaptive update law The estimation error, Represents the adaptive update law The estimation error; The dynamic compensation signal is defined as: (27) Choose the Lyapunov function for: (28) Lyapunov function The derivative is expressed as: (29) virtual control signal and dynamic compensation signal Substituting into formula (29), we get: (30) Choose the Lyapunov function for: (31) in, and For parameters; Lyapunov function The derivative is expressed as: (32) Using fuzzy logic control for approximation, it can be rewritten as: (33) Substituting formula (33) into formula (32), we get: (34) control signal and dynamic compensation signal Substituting into formula (34), we get: (35) The adaptive update laws are designed as follows: (36) (37) Therefore, we get: (38) Therefore, the expression for the attitude controller of the UAV under the conditions of parameter uncertainty and external disturbance is: 。 5. The rapid composite control method for unmanned aerial vehicles based on fuzzy asymptotics according to claim 4, characterized in that, In step 2, after completing the design of the fuzzy asymptotic fast composite controller, a stability analysis is performed on the UAV controlled by the fuzzy asymptotic fast composite controller. The stability analysis process is as follows: Step 2.

3. Construct the Lyapunov function and prove that all signals of the closed-loop system remain bounded in finite time, and that the tracking error can converge to a set in finite time. Step 2.

4. Based on Step 2.3, using Barbalat's lemma, we further prove that as time approaches infinity, the tracking error and its derivative both asymptotically converge to zero.

6. The rapid composite control method for unmanned aerial vehicles based on fuzzy asymptotics according to claim 5, characterized in that, Step 2.3 specifically involves: Choose the Lyapunov function for: (39) Lyapunov function The derivative of is expressed as: (40) make ,in , ,but Established; in, , , ; Substituting the relation into formula (40), we get: (41) in, ; ； Choose the Lyapunov function for: (42) Lyapunov function The first derivative is: (43) According to the boundedness condition ,in Given positive integers, we get: (44) in, , ; The parameter is , and satisfies ; Dynamic compensation signal Able to complete within a limited time Converging to set : ； Convergence time for: ； in, It is a positive number, and ; Representing Lyapunov functions The initial value; Lyapunov function Able to do within a limited time Converging inward to the set : ； in, express Lyapunov function at time t The value; ; ； according to , ,as well as and The boundedness of, obtain and Bounded; Compensated tracking error Able to converge to the set in a finite time : ； According to the relation , It is stable over a finite time and satisfies ; That is, tracking error In a limited time Converging inward to the set : ； in, .

7. The rapid composite control method for unmanned aerial vehicles based on fuzzy asymptotics according to claim 6, characterized in that, Step 2.4 specifically involves: In the interval Integrating equation (41) yields the expression shown in equation (45): (45) in, express Lyapunov function at time t The value of the Lyapunov function The initial value is ; Indicates dynamic compensation signal; According to formula (45), the inequality shown in formula (46) holds: (46) Based on the convergence analysis of Barbalat's lemma, we obtain: (47) In the interval Integrating equation (44) yields the expression shown in equation (48): (48) According to formula (48), the inequality shown in formula (49) holds: (49) Based on the convergence analysis of Barbalat's lemma, we obtain: (50) Combination Relation as well as asymptotic convergence; Obtain the tracking error signal That is, as time approaches infinity, the tracking error and its derivative asymptotically converge to zero.

8. A rapid composite control system for unmanned aerial vehicles based on fuzzy asymptotics, characterized in that, include: The dynamics modeling module is used to establish dynamic models of UAVs with parameter uncertainties and external disturbances; The controller construction module is used to build a fuzzy asymptotic fast composite controller for the UAV under conditions of parameter uncertainty and external disturbance, based on the UAV dynamic model established by the dynamic modeling module. And a tracking control module, which uses a fuzzy asymptotic fast composite controller built with the controller building module to achieve trajectory tracking control of the UAV.

9. A computer device comprising a memory and one or more processors, wherein the memory stores executable code, characterized in that, When the processor executes the executable code, it implements the steps of the rapid composite control method for unmanned aerial vehicles based on fuzzy asymptotes as described in any one of claims 1 to 7.

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