Unmanned aerial vehicle navigation angle control method

Abstract

The invention discloses a method for controlling the navigation angle of an unmanned aerial vehicle. The method mainly comprises the steps of collecting real-time flight data of the unmanned aerial vehicle, designing a sliding mode surface according to a flight error, carrying out secondary iteration on the sliding mode surface, controlling the unmanned aerial vehicle, judging whether a new tracking error meets a design requirement or not and the like. The sliding mode surface can be designed according to the unmanned aerial vehicle data monitored in real time, and the iterative learning algorithm is fused into the sliding mode control framework, so that the control input is optimized by using the dynamic feedback of historical error data without depending on the accurate physical parameters or complex dynamic model of the unmanned aerial vehicle, and the control precision is improved. The sliding mode buffeting is effectively inhibited, the anti-interference capability of the system is enhanced, meanwhile, the adaptive performance of iterative learning enables a controller to respond to the task environment change in real time, the attitude tracking precision is remarkably improved, and the method can still realize high-stability control under the scene that physical parameters are unknown or strong external disturbance exists. And a robust and efficient solution is provided for unmanned aerial vehicle attitude tracking in a complex task.

Description

Technical Field

[0001] The present invention relates to the field of UAV control, and specifically to a method for controlling the navigation angle of a UAV. Background Technique

[0002] In recent years, due to its vertical takeoff and landing, stable hovering, and multi-scene adaptability, quadrotor UAVs have been widely used in fields such as aerial photography, inspection, and agriculture. Their attitude control accuracy has become a key technical challenge. Currently, sliding mode control is widely adopted due to its strong robustness and anti-interference characteristics. By designing a sliding mode surface to drive the error to converge, it can theoretically handle model uncertainties. In the prior art, some studies have improved sliding mode control through adaptive algorithms or disturbance observers (such as CN112987666A), or attempted to apply ILC to linear systems (such as JP2020157833A), but most solutions still rely on accurate dynamic models or do not deeply solve the control problems of strongly nonlinear systems.

[0003] However, since the design of the sliding mode controller is based on the accurate dynamic model of the system, and the quadrotor UAV is a typical nonlinear, strongly coupled, and time-varying system, its dynamic model is easily affected by motor nonlinear response, aerodynamic effects, sensor noise, and environmental disturbances (such as wind disturbance and load changes), making it difficult to accurately model it completely through theoretical derivation or experimental identification. At the same time, due to the switching characteristics of sliding mode control, high-frequency chattering is easily induced during operation, resulting in actuator wear and energy loss. For complex task scenarios (such as multi-UAV cooperation and dynamic trajectory tracking), a sliding mode controller with fixed parameters is difficult to adaptively optimize, limiting its application effect in real environments. Summary of the Invention

[0004] To solve the above problems, the present invention provides the following technical solutions:

[0005] 1. A method for controlling the navigation angle of a UAV, characterized by including the following steps:

[0006] S1. Technicians set the desired attitude angle;

[0007] S2. Calculate the angle tracking error of the UAV during navigation;

[0008] S3. Technicians select control parameters;

[0009] S4. Construct a sliding mode surface based on the tracking error data;

[0010] S5. Perform the first iteration on the sliding mode surface data;

[0011] S6. Perform the second iteration on the data of the first iteration;

[0012] S7. Output the data after the second iteration to the controller. The controller issues a control signal to act on the drone, and the drone adjusts its attitude.

[0013] S8. Calculate the tracking error of the drone after adjusting its attitude.

[0014] S9. Determine whether the new tracking error is less than the design requirement. If it is greater than the design requirement, continue to jump to step S4. If it is less than the design requirement, end the step.

[0015] Based on the above technical solution, the present invention can also be improved as follows.

[0016] Further, step S2 includes the following steps:

[0017] S2-1. Collect the angular information during the K flights of the drone.

[0018] S2-2. Subtract the angular information from the desired angular information to obtain the angular tracking error.

[0019] Further, in step S2, the angular information includes the roll angle, pitch angle, and yaw angle during the k iteration periods;

[0020] The roll angle during the k iteration periods is denoted as The pitch angle is denoted as θ k and the yaw angle is denoted as Ψ k The collected angular information is denoted as

[0021] The desired angular information is denoted as The angular tracking error is denoted as The angular tracking error information is denoted as

[0022] Further, step S4 includes the following steps:

[0023] S4-1. Perform a differential operation on the angular tracking error to obtain the angular error differential term:

[0024]

[0025] where △t represents the sampling time and △e represents the time derivative of the angular tracking error;

[0026] S4-2. Establish a sliding mode surface equation, and the formula is as follows:

[0027]

[0028] where μ is a designed control parameter greater than zero and s is the sliding mode surface.

[0029] Further, the formula for one iteration in step S5 is:

[0030] η k = η k-1 + s k T Γs k

[0031] where K and Γ represent control parameters greater than zero.

[0032] Further, the formula for two iterations in step S5 is:

[0033] u k = Ks k + s k η k

[0034] where u k is the data input to the controller in the k-th experiment.

[0035] An angle controller, which adopts the UAV navigation angle control method, and the angle controller is a PID controller.

[0036] A UAV navigation attitude control system, an angle controller, further includes:

[0037] A navigation data sensor, which is used to collect UAV attitude and motion data in real time;

[0038] An autopilot module, which is used to control the UAV to complete roll commands and pitch commands;

[0039] A control module, which includes a position controller, a speed controller, an angle controller, and an angular velocity controller connected in series in sequence. The angular velocity controller can output a control signal to the autopilot module

[0040] Further, the control module adopts a cascade control method, and takes the output of one controller as the desired input information of the next-level controller.

[0041] Further, the position controller is a proportional controller, and the speed controller is a PID controller.

[0042] Beneficial effects

[0043] Compared with the prior art, the present invention can design a sliding surface according to the real-time monitored UAV data, and integrate an iterative learning algorithm into the sliding mode control framework, so as to optimize the control input by using the dynamic feedback of historical error data without relying on the accurate physical parameters or complex dynamic models of the UAV, effectively suppressing the sliding mode chattering and enhancing the anti-interference ability of the system.

[0044] Meanwhile, the adaptability of iterative learning enables the controller to respond to changes in the task environment in real time, significantly improving the attitude tracking accuracy. In scenarios where physical parameters are unknown or there are strong external disturbances, this method can still achieve high-stability control, providing a robust and efficient solution for UAV attitude tracking in complex tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0046] Figure 1 Flowchart of the inventive method;

[0047] Figure 2 Schematic diagram of the flight control framework of a quadrotor UAV based on cascade control;

[0048] Figure 3 Control block diagram of the attitude controller of a quadrotor UAV based on model-free sliding mode control; DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] To facilitate the understanding of the present invention, the following will describe the present invention more comprehensively with reference to the relevant drawings. Several embodiments of the present invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0050] Please refer to Figures 1 - 3 , a method for controlling the navigation angle of a UAV, comprising the following steps:

[0051] S1. Technicians set the desired attitude angle;

[0052] S2. Calculate the angle tracking error of the UAV during navigation;

[0053] S2-1. Collect the angle information of the UAV during K flights;

[0054] S2-2. Subtract the angle information from the desired angle information to obtain the angle tracking error;

[0055] Wherein, the angle information includes the roll angle, pitch angle, and yaw angle in k iteration periods;

[0056] The roll angle in the k iteration periods is denoted as The pitch angle is denoted as θ k , and the yaw angle is denoted as Ψk The collected angle information is denoted as

[0057] The desired angle information is denoted as The angle tracking error is denoted as The angle tracking error information is denoted as

[0058] S3. The technician selects the control parameters;

[0059] S4. Construct a sliding mode surface based on the tracking error data;

[0060] S4-1. Perform a differential operation on the angle tracking error to obtain the angle error differential term:

[0061]

[0062] where, △t represents the sampling time, and △e represents the time derivative of the angle tracking error;

[0063] S4-2. Establish the sliding mode surface equation, and the formula is as follows:

[0064]

[0065] where, μ is a designed control parameter greater than zero, and s is the sliding mode surface;

[0066] S5. Perform the first iteration on the sliding mode surface data;

[0067] The formula is:

[0068]

[0069] where, K and Γ represent control parameters greater than zero;

[0070] S6. Perform the second iteration on the data of the first iteration;

[0071] The formula is:

[0072] u k = Ks k + s k η k

[0073] where, u k is the data input to the controller in the k-th experiment.

[0074] S7. Output the data after the second iteration to the controller, and the controller issues a control signal to act on the UAV, and the UAV adjusts its attitude;

[0075] S8. Calculate the tracking error of the UAV after adjusting its attitude;

[0076] S9. Determine whether the new tracking error is less than the design requirement ε. If it is greater than the design requirement, continue to jump to step S4. If it is less than the design requirement, end the step, where ε is greater than 0.

[0077] Compared with the traditional sliding mode control that relies on an accurate dynamic model, this method introduces an iterative learning algorithm, constructs a model-free control framework, and cooperates with the iterative learning technology that realizes dynamic feedback through historical attitude tracking error data to directly optimize the control input. It does not need to rely on physical models such as the mass, inertia, and aerodynamic parameters of the quadrotor UAV, and can still maintain high-precision control in scenarios where physical parameters are unknown or dynamically changing, significantly reducing the complexity of model identification and parameter calibration.

[0078] At the same time, due to the insensitivity of sliding mode control to matching disturbances, this method can still ensure the exponential convergence characteristic of the attitude error in the presence of high-frequency disturbances or unmodeled dynamics. Compared with the traditional sliding mode control technology that drives the system state to converge along a predetermined trajectory by designing a sliding mode surface, it can also achieve stable tracking in a more complex environment.

[0079] This method accumulates historical error information through the integral term in iterative learning to adjust the reference value of the control input in real time and suppress the chattering phenomenon of sliding mode control. At the same time, the proportional term quickly responds to the current error change. Combining with the dynamic convergence characteristic of the sliding mode surface, the system can adapt to the changes in the task environment and gradually optimize the control performance in repetitive tasks (such as inspection and formation flight).

[0080] Example 1

[0081] To illustrate the usage scenario of this method in detail, a kind of angle controller is disclosed here. It adopts the above-mentioned UAV navigation angle control method. Specifically, the angle controller is a PID controller. The PID algorithm has a simple structure and only needs a small amount of multiplication and addition operations to complete the error calculation and output update in real time, ensuring the efficient drive of the underlying actuator.

[0082] Specifically, the angular velocity PID controller can detect the deviation between the current angular velocity and the target value in real time through the proportional term (P) and quickly generate an adjustment signal to ensure that the UAV can quickly respond to the attitude change instruction. The integral term (I) can gradually correct the continuous deviation caused by motor nonlinearity, friction resistance, or external disturbances by accumulating historical angular velocity errors. The derivative term (D) can adjust the control output in advance by predicting the change trend of the angular velocity error, effectively suppressing system overshoot and mechanical oscillation, so that the UAV has the ability of quick response and dynamic adjustment.

[0083] Example 2:

[0084] Different from Embodiment 2, in order to further reduce the complexity of the system during actual application, as shown in the figure, an unmanned aerial vehicle (UAV) navigation attitude control system includes:

[0085] A navigation data sensor, which is used to collect UAV attitude and motion data in real time;

[0086] An autopilot module, which is used to control the UAV to complete roll commands and pitch commands;

[0087] A control module, which includes a position controller, a speed controller, an angle controller, and an angular velocity controller connected in series in sequence. The angular velocity controller can output a control signal to the autopilot module.

[0088] The control module adopts a cascade control method, taking the output of one controller as the desired input information of the next-level controller. The position controller is a proportional controller, and the speed controller is a PID controller. Through the hierarchical and progressive transmission of control signals, iterative learning and sliding mode operation are limited to the angle control layer, avoiding the computational burden brought by full-state optimization, thereby ensuring the real-time performance of the system on the embedded platform and meeting the requirements of high-dynamic response of the UAV.

[0089] In the description of the present technology, it should also be noted that unless otherwise clearly specified and limited, the terms "set", "install", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present technology can be understood according to specific situations.

[0090] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. A method for controlling the navigation angle of an unmanned aerial vehicle, characterized in that, It includes the following steps: S1. Technicians set the expected attitude angle; S2. Calculate the angle tracking error of the UAV during flight; S3. Technicians select control parameters; S4. Construct a sliding mode surface based on the tracking error data; S5. Perform the first iteration on the sliding mode surface data; S6. Perform the second iteration on the data of the first iteration; S7. Output the data after the second iteration to the controller. The controller issues a control signal to act on the UAV, and the UAV adjusts its attitude; S8. Calculate the tracking error of the UAV after adjusting the attitude; S9. Determine whether the new tracking error is less than the design requirement. If it is greater than the design requirement, continue to jump to step S4. If it is less than the design requirement, end the steps.

2. The method for controlling the navigation angle of an unmanned aerial vehicle according to claim 1, wherein: The step S2 includes the following steps: S2-1. Collect the angle information of the UAV during K flights; S2-2. Subtract the angle information from the expected angle information to obtain the angle tracking error.

3. A method for controlling the navigation angle of a drone according to claim 1, characterized in that: In the step S2, the angle information includes the roll angle, pitch angle, and yaw angle in k iteration periods; The roll angle in the k-th iteration period is denoted as The pitch angle is denoted as θ k , and the yaw angle is denoted as Ψ k , and the acquired angle information is denoted as The said desired angle information is denoted as The said angle tracking error is denoted as The angle tracking error information is denoted as 4. A method for controlling the navigation angle of a drone according to claim 1, characterized in that: The step S4 includes the following steps: S4-1. Perform a differential operation on the angle tracking error to obtain the angle error differential term: where △t represents the sampling time and △e represents the time derivative of the angle tracking error; S4-2. Establish a sliding mode surface equation, and the formula is as follows: where μ is a designed control parameter greater than zero and s is the sliding mode surface.

5. A method for controlling the navigation angle of an unmanned aerial vehicle according to claim 1, characterized in that: The formula for the first iteration in the step S5 is: where K and Γ represent control parameters greater than zero.

6. A method for controlling the navigation angle of a drone according to claim 1, characterized in that: The formula for the second iteration in the step S5 is: u k = Ks k + s k η k where, u k is the data input to the controller in the k-th experiment.

7. An angle controller, which adopts the unmanned aerial vehicle navigation angle control method described in any one of claims 1 to 6, is characterized in that: The angle controller is a PID controller.

8. A UAV navigation attitude control system, including the angle controller described in claim 7, characterized in that, It also includes: A navigation data sensor for real-time collection of UAV attitude and motion data; An autopilot module for controlling the UAV to complete roll commands and pitch commands; A control module including a position controller, a speed controller, an angle controller, and an angular velocity controller connected in series in sequence. The angular velocity controller can output a control signal to the autopilot module.

9. The attitude control system for an unmanned aerial vehicle according to claim 8, wherein: The control module adopts a cascade control method, and the output of one controller is used as the expected input information of the next-level controller.

10. A UAV navigation attitude control system according to claim 9, characterized in that: The position controller is a proportional controller, and the speed controller is a PID controller.

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