Rotor unmanned aerial vehicle elastic rope hanging flight adaptive control method

Abstract

The present application belongs to the technical field of unmanned aerial vehicle control, and more particularly relates to a kind of elastic rope hanging flight self-adaptive control method of rotor unmanned aerial vehicle, i.e., the control method of quadrotor unmanned aerial vehicle flying transportation by hanging object with elastic rope. To achieve the control of quadrotor unmanned aerial vehicle position while quickly suppressing the swing of hanging load under the condition of unknown system mass parameters. The present application designs Lyapunov equation based on energy function method and parameter adaptive method, and then designs the controller to realize the control of hanging unmanned aerial vehicle. The present application is mainly applied to the flight transportation control of hanging load of rotor unmanned aerial vehicle.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) control technology, and more specifically, relates to an adaptive control method for a quadcopter UAV carrying a load during flight, namely, a control method for a quadcopter UAV to transport objects by suspending them with elastic ropes. Background Technology

[0002] Quadcopter drones possess flexible maneuverability and good stability, enabling them to quickly complete tasks such as aerial transport and retrieval. In recent years, with the rapid development of electronic technology, quadcopter drones have been widely used in military, civilian, rescue, and transportation fields. As an extension of drone applications, quadcopter-based payload transport systems also possess flexible performance and a stable structure, making them highly valuable in rescue and material transportation, and showing promising development prospects.

[0003] Currently, the problems of load swing and UAV positioning control in quadrotor UAV sling flight systems have attracted widespread attention from research teams both domestically and internationally. A research team from Beijing University of Aeronautics and Astronautics has proposed a multi-observer-based anti-disturbance control method. Compared with the classical PID control method, this method exhibits better robustness and significantly improves the system's anti-interference performance (Journal: Control Engineering Practice; Authors: Guo, Kexin, Jia Jindou, Yu Xiang, Guo Lei and Xie Lihua; Title: Multiple observers based anti-disturbance control for a quadrotor UAV against payload and winddisturbances; Pages: 104560). Based on energy analysis, Liang Xiao et al. from Nankai University designed a novel nonlinear controller, achieving stable control of the system (Conference: Proceedings of the IEEE International Conference on Advanced Robotics and Mechatronics (ICARM); Authors: Liang, Xiao, Yu Hai, Zhang Zhuang, Wang Yang, Sun Ning and Fang Yongchun; Publication Date: 2020; Title: Unmanned Quadrotor Transportation Systems with Payload Hoisting / Lowering: Dynamics Modeling and Controller Design; Pages: 666-671).

[0004] To achieve trajectory control and sway suppression for quadcopters, Alkomy et al. from the University of York (Journal: NonlinearDynamics; Authors: Alkomy Hassan and Shan Jinjun; Title: Vibration reduction of aquadrotor with a cable-suspended payload using polynomial trajectories; Pages: 3713-3735) analyzed the impact of polynomial trajectories on load sway in a suspended system and proposed a polynomial trajectory method that is most effective in suppressing load sway. Muthusamy et al. (Journal: IEEE Transactions on Industrial Electronics; Authors: Muthusamy Praveen Kumar, Garratt Matthew, Pota Hemanshu and Muthusamy Rajkumar; Title: Real-Time Adaptive Intelligent Control System for Quadcopter Unmanned Aerial Vehicles with Payload Uncertainties; Pages: 1641-1653) introduced a brain-based emotion learning algorithm into a quadcopter suspended system and proposed an intelligent control method. Experimental results show that the suspension system has good trajectory tracking ability and robustness in the presence of external interference.

[0005] Several research teams have also investigated different scenarios involving the suspension rope. Sierra et al. (Journal: Expert Systems with Applications; Authors: Sierra-García Jesus Enrique, Santos Matilde; Title: Intelligent control of an UAV with a cable-suspended load using aneural network estimator; Pages: 115380) analyzed different conditions such as rope tension and slack, and proposed a nonlinear hybrid control method based on neural networks to achieve stable control of a quadrotor UAV and reduce the sway of the suspended load. Goodarzi et al. (Journal: International Journal of Control, Automation and Systems; Authors: Goodarzi Farhad A, Lee Daewon and Lee Taeyoung; Title: Geometric control of a quadrotor UAV transporting a payload connected via flexiblecable; Pages: 1486-1498) proposed a geometric nonlinear controller and modeled the flexible rope as a series system of five links, achieving precise positioning of the quadrotor UAV and suppressing the sway of the suspended load. Yang et al. (Journal: Asian Journal of Control; Authors: Yang Yunxiao, Zhang Dong, Xi Houyin and Zhang Guoqing; Title: Anti-swing control and trajectory planning of quadrotorsuspended payload system with variable length cable; Pages: 2424-2436) set the cable length to be variable and designed a trajectory planning control scheme based on the coupled dynamics model of the system. The effectiveness of the control scheme was verified through simulation experiments. Summary of the Invention

[0006] To overcome the shortcomings of existing technologies, this invention aims to propose an adaptive control method for the flight of a quadcopter drone suspended by an elastic rope, which can control the position of the quadcopter drone while quickly suppressing the swaying of the suspended load under the condition that the system mass parameters are unknown.

[0007] The technical solution adopted in this invention is an adaptive control method for elastic rope-suspended flight of a rotary-wing UAV. The steps are as follows: designing the Lyapunov equation based on the energy function method and the parameter adaptive method; realizing the semi-negative definiteness of the first derivative of the Lyapunov equation by designing a controller; and achieving asymptotic stability of the closed-loop system by the controller according to the Lyapunov stability theorem.

[0008] The further specific steps are as follows: First, force analysis is performed on both the quadcopter drone and the suspended object to obtain a nonlinear dynamic model of the quadcopter drone's suspension system.

[0009]

[0010] The variables and parameters in the formula are defined as follows: (x, y, z) represents the position coordinates of the quadcopter UAV. The accelerations corresponding to the three-axis positions of the quadcopter drone are represented by α, where α is the angle between the projection of the suspending cable onto the xoz plane and the vertical direction, and β is the angle between the projection of the suspending cable onto the yoz plane and the vertical direction. The angular velocity represents the angle of the rope's swing. The angular acceleration represents the swing angle of the rope, and L is the real-time length of the suspension rope. This represents the change in velocity along the length of the rope. (This represents the change in acceleration due to the length of the rope), M and m are the masses of the quadcopter and the suspended object, respectively, and F... x ,F y ,F z Let x, y, and z represent the lift magnitudes of the quadcopter UAV along the x, y, and z axes, respectively, where g is the acceleration due to gravity. For the elastic suspension rope, the initial length of the rope is L0, k is the rope elasticity coefficient, and the expression for the rope tension T is as follows: T = k(L - L0).

[0011] For the suspended UAV model in equation (1), the following controller is designed:

[0012]

[0013] In equation (2), k x ,k y ,k z ,k dx ,k dy ,k dz ,k zl ξ are both positive control gains. The position error of the quadcopter UAV in the x, y, and z axes is denoted as e. x ,e y ,e z The target location of the drone is denoted as (x T ,y T ,z TIf the position error of the UAV is given by: e x =xx T ,e y =yy T ,e z =zz T . An adaptive estimate of the total mass (W = m + M) of the quadcopter UAV sling system is provided, with the following adaptive update rate:

[0014]

[0015] Where τ is a constant greater than zero, and the mapping function The definition is as follows:

[0016]

[0017] k x ,k z ,k dx ,k dz ,k zl ξ affects the convergence rate of the swing angle α; k x ,k y ,k z ,k dx ,k dy ,k dz ,k zl ξ affects the convergence rate of the swing angle β; k,k x ,k dx The tracking convergence speed affecting the horizontal x-displacement of a quadrotor; k,k y ,k dy The tracking convergence speed affected by the horizontal y-displacement of the quadrotor; k,k z ,k dz The tracking convergence speed affects the vertical z-displacement of the quadrotor.

[0018] The steps to prove the asymptotic convergence properties of the controller are as follows:

[0019]

[0020] In the formula x L =x - Lsinαcosβ,y L =y-Lsinβ,z L =z-Lcosαcosβ(x) L ,y L ,z L (The coordinates of the suspended load).

[0021] Taking the first derivatives of the kinetic and potential energy equations in equation (5) with respect to time, we get:

[0022]

[0023] Combining the energy equations in equation (6), the first derivative of the total energy of the system is:

[0024]

[0025] The Lyapunov candidate function is constructed as follows:

[0026]

[0027] Where τ is a constant greater than zero. Bias in system quality estimation V is about e x ,e y ,e z , For a positive definite function, taking the first time derivative of both sides of equation (8) simultaneously yields:

[0028]

[0029] Substituting equations (2-4) and (7) into (9) and simplifying, we get:

[0030]

[0031] Therefore It is about The negative definite function. Therefore, we can conclude that the system is asymptotically stable, i.e.

[0032]

[0033] Numerical simulation steps for stability control and regulation control were performed to test the control performance of the adaptive parameter update rate of the adaptive controller (2) and equation (3) for the quadcopter sling flight system.

[0034] The advantages of the above technical solution, which differ from existing technologies, are:

[0035] This invention designs an adaptive control method for the flight of a quadcopter drone suspended by an elastic rope. Under the condition of unknown system mass parameters, it achieves flight control of a quadcopter drone suspended by an elastic rope. While ensuring that the quadcopter drone reaches the designated position, it can quickly suppress the swaying of the suspended load, achieving the asymptotic convergence of the load's sway angle to zero as the quadcopter drone asymptotically converges to the target position. Attached Figure Description

[0036] Figure 1 This is a simplified structural diagram of a quadcopter drone sling system;

[0037] Figure 2This is a control effect diagram from a numerical simulation of regulation and control, where:

[0038] 'a' represents the position change curve of the quadcopter UAV in the control simulation.

[0039] b is the curve showing the change in the swing angle of the suspension rope in the control simulation.

[0040] c is the quality estimate in regulation and control simulation. The curve of change,

[0041] d represents the curve showing the change in rope length and total lift of the quadcopter UAV in the control simulation.

[0042] Figure 3 This is a control effect diagram from a numerical simulation of stable control, where:

[0043] 'a' represents the position change curve of the quadcopter UAV in the stability control simulation.

[0044] b is the curve showing the change in the swing angle of the suspension rope in the stability control simulation.

[0045] c is the mass estimate in the stability control simulation. The curve of change,

[0046] d represents the curves showing the changes in rope length and total lift of the quadcopter UAV in the stability control simulation. Detailed Implementation

[0047] To explain in detail the technical content, structural features, objectives, and effects of the technical solution, the following description is provided in conjunction with specific embodiments and accompanying drawings.

[0048] See Figure 1 The diagram shown is a simplified structural diagram of a quadcopter UAV sling system. The adaptive control method for quadcopter UAV sling-mounted flight involves the following steps: designing Lyapunov equations based on the energy function method and parameter adaptive method, and then designing a controller to control the sling-mounted UAV.

[0049] The technical problem to be solved by the present invention is to design an adaptive controller for a quadcopter unmanned aerial vehicle (UAV) system based on an elastic rope suspending a load, so as to quickly suppress the swaying of the suspended load when the quadcopter UAV reaches the designated position.

[0050] The technical solution adopted in this invention is as follows: Based on the energy function method and parameter adaptive method, Lyapunov equations are designed, and then a controller is designed to control the suspended UAV, including the following steps:

[0051] Step S1: Perform force analysis on the quadcopter drone and the suspended object respectively to obtain the nonlinear dynamic model of the quadcopter drone suspension system:

[0052]

[0053] The variables and parameters in the formula are defined as follows: (x, y, z) represents the position coordinates of the quadcopter UAV. The accelerations corresponding to the three-axis positions of the quadcopter drone are represented by α, where α is the angle between the projection of the suspending cable onto the xoz plane and the vertical direction, and β is the angle between the projection of the suspending cable onto the yoz plane and the vertical direction. The angular velocity represents the angle of the rope's swing. The angular acceleration represents the swing angle of the rope, and L is the real-time length of the suspension rope. This represents the change in velocity along the length of the rope. (This represents the change in acceleration due to the length of the rope), M and m are the masses of the quadcopter and the suspended object, respectively, and F... x ,F y ,F z Let x, y, and z represent the lift magnitudes of the quadcopter UAV along the x, y, and z axes, respectively, where g is the acceleration due to gravity. For the elastic suspension rope, the initial length of the rope is L0, k is the rope elasticity coefficient, and the expression for the rope tension T is as follows: T = k(L - L0).

[0054] For the suspended UAV model in equation (1), the following controller is designed:

[0055]

[0056] In equation (2), k x ,k y ,k z ,k dx ,k dy ,k dz ,k zl ξ are both positive control gains. The position error of the quadcopter UAV in the x, y, and z axes is denoted as e. x ,e y ,e z The target location of the drone is denoted as (x T ,y T ,z T If the position error of the UAV is given by: e x =xx T ,e y =yy T ,e z =zz T . An adaptive estimate of the total mass (W = m + M) of the quadcopter UAV sling system is provided, with the following adaptive update rate:

[0057]

[0058] Where τ is a constant greater than zero, and the mapping function The definition is as follows:

[0059]

[0060] k x ,k z ,k dx ,k dz ,k zl ξ affects the convergence rate of the swing angle α; k x ,k y ,k z ,k dx ,k dy ,k dz ,k zl ξ affects the convergence rate of the swing angle β; k,k x ,k dx The tracking convergence speed affecting the horizontal x-displacement of a quadrotor; k,k y ,k dy The tracking convergence speed affected by the horizontal y-displacement of the quadrotor; k,k z ,k dz The tracking convergence speed affects the vertical z-displacement of the quadrotor.

[0061] The steps to prove the asymptotic convergence properties of the controller are as follows:

[0062]

[0063] Taking the first derivatives of the kinetic and potential energy equations in equation (5) with respect to time, we get:

[0064]

[0065] Combining the energy equations in equation (6), the first derivative of the total energy of the system is:

[0066]

[0067] The Lyapunov candidate function is constructed as follows:

[0068]

[0069] Where τ is a constant greater than zero. Bias in system quality estimation V is about e x ,e y ,e z , For a positive definite function, taking the first time derivative of both sides of equation (8) simultaneously yields:

[0070]

[0071] Substituting equations (2-4) and (7) into (9) and simplifying, we get:

[0072]

[0073] Therefore It is about The negative definite function. Therefore, we can conclude that the system is asymptotically stable, i.e.

[0074]

[0075] To verify the effectiveness of the control scheme in this invention, numerical simulation steps of stable control and regulation control were performed to test the control performance of the adaptive parameter update rate of the adaptive controller (2) and equation (3) for the quadcopter suspended flight system. By comparing the control effect with that of the non-elastic rope suspended system, the advantages of the proposed adaptive controller under elastic suspension rope were highlighted.

[0076] I. Introduction to Numerical Simulation

[0077] The relevant parameter settings for the quadcopter UAV sling load system are as follows:

[0078] m = 0.65 kg, M = 1.4 kg

[0079] k=16N / m, L0=0.5m, g=9.8m / s 2 .

[0080] Apply the controller (2) to the system (1) and select the following parameters:

[0081] k x =13,k y =12,k z =24,k dx =5,k dy =5,

[0082] k dz =9,τ=0.01,k zl =0.3, ξ=100, W(0)=1.8.

[0083] II. Regulation and Control Simulation

[0084] Based on the above parameters, the initial state and target position of the system are set as follows:

[0085]

[0086] It can achieve control effects such as Figure 2Figures (a), (b), (c), and (d) show the curves of the UAV's position change, the change in the sling angle, the change in mass estimation, and the change in the sling length versus the total lift of the system, respectively. Solid lines in the figures represent the proposed control effect, while dashed lines represent the control effect of the inelastic rope system. Figure 2 (a), Figure 2 As can be seen in (b), under the action of the adaptive controller proposed in this invention, the control effect of the elastic rope system is significantly better than that of the non-elastic rope system, and the swing angle of the elastic rope is smaller, reducing the risk of quadcopter sling transport. It achieves the ability to quickly suppress the swing of the sling load while controlling the UAV to reach the target position, and compared with the non-elastic rope system, the swing angle of the elastic rope system converges asymptotically to zero more quickly. Figure 2 As can be seen in (c), the adaptive controller can estimate the mass of the system in the direction of gravity online, which improves the system's adaptability to uncertain parameters.

[0087] III. Stability Control Simulation

[0088] The initial state and target position of the system are selected as follows:

[0089]

[0090] Using the same simulation principles as regulation and control, we can obtain results such as Figure 3 Figures (a), (b), (c), and (d) show the control effect. They represent the UAV's position change curve, the sling angle change curve, the mass estimation change curve, and the sling length versus total system lift curve, respectively. Solid lines in the figures represent the proposed control effect, while dashed lines represent the control effect of the inelastic rope system. Figure 3 (a), Figure 3 As can be seen in (b), compared with the non-elastic rope system, the proposed adaptive controller for the elastic rope system can quickly control the UAV to reach the target position and suppress the swaying of the suspended load more quickly. Figure 3 As can be seen from (c), the adaptive controller can effectively eliminate the influence of unknown quality and has a good control effect.

[0091] The above analysis verifies the effectiveness of the algorithm proposed in this invention.

[0092] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Unless otherwise specified, an element defined by the phrase "comprising..." or "including..." does not exclude the presence of additional elements in the process, method, article, or terminal device that includes said element. Additionally, in this document, "greater than," "less than," "exceeding," etc., are understood to exclude the stated number; "above," "below," "within," etc., are understood to include the stated number.

[0093] Although the above embodiments have been described, those skilled in the art, once they understand the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the above descriptions are merely embodiments of the present invention and do not limit the scope of patent protection of the present invention. Any equivalent structural or procedural transformations made using the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. An adaptive control method for flexible rope-suspended flight of a rotary-wing unmanned aerial vehicle, characterized in that, The adaptive control method combines the energy function method and the parameter adaptive method to design the Lyapunov equation, and then designs a controller for the quadcopter suspension system to control the quadcopter. The adaptive control method performs force analysis on the quadrotor UAV and the suspended object to obtain a nonlinear dynamic model of the quadrotor UAV's suspension system. (1) The definitions of each variable and parameter in the formula are as follows: Represents the position coordinates of the quadcopter drone. The accelerations corresponding to the three-axis positions of the quadcopter drone. Projecting the hanging ropes onto The angle between the surface and the vertical direction. Projecting the hanging ropes onto The angle between the surface and the vertical direction. The angular velocity represents the angle of the rope's swing. Angular acceleration, representing the angle of the rope's swing. This represents the real-time length of the suspension rope. This represents the change in velocity along the length of the rope. This represents the change in acceleration due to the length of the rope. and These are the masses of the quadcopter and the suspended object, respectively. These represent the quadcopter drones along... The magnitude of the lift force of the shaft, For an elastic suspension rope, where gravitational acceleration is the initial length of the rope, the initial length of the rope is... , The elastic coefficient of the rope and the rope tension are given by the following terms: The expression is as follows: ; For the suspended UAV model in equation (1), the following controller is designed: (2) In equation (2), All are positive control gains, quadcopter drones in The positional error in the three directions of the axis is denoted as The target location of the drone is denoted as The positional error of the UAV can be expressed as follows: , The total mass of the quadcopter drone sling system The adaptive estimation is as follows, and the adaptive update rate is as follows: (3) in It is a constant greater than zero, and the mapping function The definition is as follows: (4) Affecting the swing angle The convergence speed; Affecting the swing angle The convergence speed; Affecting the horizontal direction of the quadcopter Displacement tracking convergence speed; Affecting the horizontal direction of the quadcopter Displacement tracking convergence speed; Affecting the vertical direction of the quadcopter Displacement tracking convergence speed.

2. The adaptive control method for elastic rope-suspended flight of a rotary-wing UAV as described in claim 1, characterized in that, The steps that also include proving the asymptotic convergence properties of the controller are: (5) In the formula , The coordinates of the suspended load; Taking the first derivatives of the kinetic and potential energy equations in equation (5) with respect to time, we get: (6) Combining the energy equations in equation (6), the first derivative of the total energy of the system is: (7) The Lyapunov candidate function is constructed as follows: (8) in A constant that is greater than zero. Bias in system quality estimation , It is about For a positive definite function, taking the first time derivative of both sides of equation (8) simultaneously yields: (9) Substituting equations (2-4) and (7) into (9) and simplifying, we get: (10) Therefore It is about The negative definite function indicates that the system is asymptotically stable. (11)。

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