Rolling wing aircraft trajectory tracking control method and device

Abstract

The invention discloses a rolling wing aircraft trajectory tracking control method and device, and the method comprises the steps: obtaining an expected trajectory of an unmanned aerial vehicle, obtaining an expected position and a first attitude vector r (k) within discrete time according to the expected trajectory, and enabling the position and the first attitude vector r (k) to be used for inputting a nonlinear prediction model of the unmanned aerial vehicle; establishing a state space model according to the dynamics and kinematics models of the unmanned aerial vehicle, and optimizing the nonlinear prediction model according to the state space model, so that the nonlinear prediction model outputs an optimal control quantity for controlling the unmanned aerial vehicle; and controlling the rotating speed and the control angle of the cycloidal propeller of the unmanned aerial vehicle according to the optimal control quantity. According to the invention, control input can be optimized, and accurate trajectory tracking is realized.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicles, and in particular to a method and device for tracking and controlling the trajectory of a rolling-wing aircraft. Background Art

[0002] Quadrotors are widely used in reconnaissance, logistics, transportation, and environmental monitoring due to their simple mechanical structure and excellent flight performance. However, traditional quadrotors use a fixed propeller shaft design, and their position and attitude are strongly coupled, making it difficult to achieve flexible control in complex tasks. Traditional control methods are difficult to balance accuracy and adaptability when faced with nonlinear, strongly coupled, and constrained flight missions, especially in complex environments where trajectory tracking performance may be limited.

[0003] The cycloid propeller is a new type of rotary power device consisting of multiple blades arranged in parallel around the propeller axis. While each blade revolves around the propeller axis, it also makes pitch oscillation motion around its own pitch axis, generating unsteady aerodynamic forces. The size and direction of the thrust vector can be quickly adjusted by adjusting the eccentric mechanism. It has the characteristics of high efficiency, instantaneous adjustable thrust direction and low noise.

[0004] The H-configuration quadcopter with cycloidal propellers as the power or lift source can realize vector control of the thrust direction by utilizing the 360° instantaneous adjustable thrust direction of the cycloidal propellers, which improves the maneuverability and flexibility of the aircraft and decouples position and attitude control to a certain extent. However, since the cycloidal aircraft has more complex dynamic characteristics, designing an efficient control algorithm has become a key technical difficulty.

[0005] There are few studies on control algorithms for gyrocopters in the prior art, and most of the existing studies are based on the traditional PID control method. Although this method performs well in simple tasks, it lacks sufficient ability to cope with the nonlinearity, coupling and external disturbances of the system; it is difficult to handle complex inputs and state constraints, resulting in insufficient trajectory tracking performance in high-speed flight or complex environments. Summary of the invention

[0006] In view of the above technical problems, the present invention provides a trajectory tracking control method and device for a gyrocopter, which can optimize control input and achieve accurate trajectory tracking.

[0007] According to a first aspect of the present invention, a trajectory tracking control method for a gyrocopter is provided, comprising: obtaining a desired trajectory of an unmanned aerial vehicle, obtaining a desired position and a first attitude vector r(k) in discrete time according to the desired trajectory, wherein the position and the first attitude vector r(k) are used to input a nonlinear prediction model of the unmanned aerial vehicle; establishing a state space model according to the dynamic and kinematic models of the unmanned aerial vehicle, optimizing the nonlinear prediction model according to the state space model so that the nonlinear prediction model outputs an optimal control quantity for controlling the unmanned aerial vehicle; and controlling the rotation speed and steering angle of the cycloidal propeller of the unmanned aerial vehicle according to the optimal control quantity.

[0008] In an optional implementation manner, optimizing the nonlinear prediction model according to the state space model includes:

[0009] Inputting the output data of the nonlinear prediction model into a state space model, calculating an error vector based on the output of the state space model, and constructing an objective function based on the error vector and the first posture vector r(k);

[0010] The nonlinear prediction model is optimized based on the objective function.

[0011] In an optional implementation, the calculating an error vector based on the output of the state-space model, and constructing an objective function based on the error vector and the first posture vector r(k), include:

[0012] The error vector is obtained by calculating the difference between the output second posture vector y(k) of the state space model and the expected first posture vector r(k): e(k)=r(k)-y(k);

[0013] The objective function is:

[0014]

[0015] Where (k+i|k) is the predicted value of k+1 at time k, N p is the prediction interval, N c is the control interval, Q is the state error weight matrix, R represents the input weight matrix, Q t is the UAV terminal weight; Δu represents the control amount increment.

[0016] In an optional embodiment, establishing a state space model according to the dynamics and kinematics models of the drone includes:

[0017] According to the dynamics and dynamics model of the UAV, a discrete state space model is established, and the discrete state space model is optimized to obtain the state space model.

[0018] In an optional implementation manner, optimizing the discrete state space model to obtain the state space model includes:

[0019] The desired position in discrete time, the first attitude vector r(k) and the current position and second attitude vector of the UAV are taken as state variables, the output of the nonlinear prediction model is taken as input, and the disturbance coefficient of the UAV is added to optimize the discrete state space model to obtain the state space model.

[0020] In an optional embodiment, establishing a state space model according to the dynamics and kinematics models of the drone further includes:

[0021] Using the increment of the state variable and the output of the nonlinear prediction model x(k)=[Δx s (k) T , y(k) T ] T Reconstruct the state space equation; the state space equation is:

[0022]

[0023] Write the kinetic model in matrix form to obtain the coefficient matrix A s , B s , C s , and due to the reconstruction of the state space, we can get the reconstructed matrices A, B, C, where I is the unit vector.

[0024] A second aspect of the present invention provides a trajectory tracking control device for a gyroplane, comprising:

[0025] A data acquisition module, used to acquire a desired trajectory of the UAV, and obtain a desired position and a first attitude vector r(k) in discrete time according to the desired trajectory, wherein the position and the first attitude vector r(k) are used to input into a nonlinear prediction model of the UAV;

[0026] A prediction module, used to establish a state space model according to the dynamics and kinematics models of the UAV, and optimize the nonlinear prediction model according to the state space model so that the nonlinear prediction model outputs an optimal control variable for controlling the UAV;

[0027] A control module is used to control the rotation speed and steering angle of the cycloidal propeller of the drone according to the optimal control amount.

[0028] According to a third aspect of the present invention, there is provided an electronic device, comprising:

[0029] At least one processor; and at least one memory communicatively connected to the processor, wherein: the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the method described in the first aspect of the embodiment of the present invention.

[0030] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a computer, the method according to the first aspect of the embodiment of the present invention is executed.

[0031] According to a fifth aspect of the present invention, a non-transitory computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method described in the first aspect of the embodiment of the present invention is implemented.

[0032] The present invention introduces the dynamics and kinematics models of the UAV to establish a state-space model as a conditional constraint, and optimizes the nonlinear prediction model used for UAV tracking through the state-space model of the UAV, thereby improving the dynamic response speed and tracking accuracy. The present invention has the technical advantage of achieving precise trajectory tracking by optimizing control input, meeting the market demand for high-performance flight in complex flight missions and dynamic environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic diagram of the H-configuration quadcopter coordinate system and the ground coordinate system.

[0034] Figure 2 The figure is a flow chart of a trajectory tracking control method of a gyroplane in an embodiment of the present invention.

[0035] Figure 3 It is a structural block diagram of the nonlinear model predictive control algorithm in an embodiment of the present invention.

[0036] Figure 4 The figure is a schematic diagram of three-dimensional simulation trajectory tracking of a UAV based on the trajectory tracking control method of a gyroplane in an embodiment of the present invention.

[0037] Figure 5 The module diagram of a trajectory tracking control device for a gyroplane in an embodiment of the present invention is shown in FIG. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0039] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0040] The method based on nonlinear model predictive control (NMPC, a closed-loop optimization control strategy based on a nonlinear model) has become an ideal choice for roller-wing aircraft trajectory tracking control due to its optimization, multivariable control capabilities and good handling of system constraints. The roller-wing aircraft trajectory tracking control method proposed in the present invention can fully utilize the vector propulsion advantages of the roller-wing aircraft, optimize the control input, and achieve accurate trajectory tracking. By introducing the dynamic and kinematic models of the roller-wing aircraft, the present invention can significantly improve the dynamic response speed and tracking accuracy, and meet the high-performance flight requirements in complex flight missions and dynamic environments.

[0041] To control the movement of a drone, we must first establish a digital model of the drone's movement. The more accurate the model is, the more accurate the description of the drone's movement is, and the better the tracking and control effect of the drone is. The dynamics and kinematics models are the models for controlling the drone's movement.

[0042] like Figure 1 As shown, by defining the body coordinate system (x b ,y b , z b ) and the ground coordinate system (x e ,y e , z e ), the nonlinear prediction model of the UAV based on the dynamics and kinematics models can be expressed as:

[0043]

[0044] Where m represents the mass of the gyroplane, I = [I x , I y , I z ] T represents the moment of inertia around the three axes, lx and ly represent the coordinates of the lever arms, and k represents the lift coefficient; p = [x, y, z] T and Θ = [φ, θ, ψ]T Respectively represent the position and attitude angle of the gyroplane, which are the input data. The input of the model is the rotation speed of the four cycloidal propellers [Ω 1 ,Ω 2 ,Ω 3 , 4 ] T and the steering angle [β 1 , β 2 , β 3 , β 4 ] T (i.e. the direction of the lift vector), the lift vector and the torque around the three axes can be changed in real time by changing the speed and steering angle, and the lift vector and the torque around the three axes can be distributed to the speed and steering angle of the four cycloid propellers to achieve tracking control. x, y, z represent the position coordinates of the drone in the air, and the pitch angle θ, yaw angle ψ, and roll angle Φ represent the flight attitude of the drone in the air. u s (k)=[f x ,f z ,τ x ,τ y , τ z ] T Represents the lift and moment acting on the gyroplane.

[0045] See also Figure 2 The present invention provides a roller aircraft trajectory tracking control method, which includes the following steps.

[0046] Step 100: Obtain the desired trajectory of the UAV, and track and control the desired position and first attitude vector r(k) in discrete time according to the desired trajectory of the gyroplane, wherein the position and the first attitude vector r(k) are used to input the nonlinear prediction model of the UAV.

[0047] In an embodiment of the present invention, the drone is an H-configuration quadcopter, and its desired trajectory is generated by a trajectory generator. The trajectory can be freely set by the user, and the trajectory generator generates data of the desired trajectory according to the setting.

[0048] Then, the desired trajectory is processed into discrete trajectory data based on the same time interval, and the desired trajectory of each time interval corresponds to a control interval, which is used for tracking and controlling the UAV.

[0049] In the prior art, the expected position and the expected first attitude vector r(k) of each control interval are usually input into the nonlinear prediction model of the UAV, and the output of the nonlinear prediction model is used to control the rotation speed and steering angle of the UAV's cycloidal propeller. However, the accuracy of the tracking and control method is not high.

[0050] Step 200: Establish a state space model according to the dynamics and kinematics models of the UAV, and optimize the nonlinear prediction model according to the state space model so that the nonlinear prediction model outputs an optimal control variable for controlling the UAV.

[0051] In this step, the expected position in discrete time, the first attitude vector r(k), the current position of the drone, and the second attitude vector predicted by the model are used as state variables, the output of the nonlinear prediction model is used as input, and the disturbance coefficient of the drone is added to establish a discrete state space model, and the discrete state space model is linearly optimized to obtain the state space model. Then, a nonlinear prediction model is designed based on the state space model to obtain the optimal control quantity.

[0052] For example, the position and the position change rate and the attitude and the attitude change rate is the state variable, the lift and moment u s (k) = [f x ,f z ,τ x ,τ y , τ z ] T is the input, ω s (k) represents the disturbance of the system at time k, and the discrete state space equation is established. p = [x, y, z] T represents the position of the gyroplane, Θ = [φ, θ, ψ] T Indicates the position and attitude angle of the gyroplane.

[0053] The discrete state space equation is expressed as:

[0054] Specifically, the output data of the nonlinear prediction model is input into the state space model, and the error vector is calculated based on the output of the state space model, and an objective function is constructed based on the error vector and the first posture vector r(k) (the objective function is the function that needs to be minimized or maximized in a convex optimization problem); the nonlinear prediction model is optimized based on the objective function.

[0055] The present invention can obtain the optimal control quantity (ie, rotation speed and steering angle) by solving the nonlinear prediction model for optimizing the objective function, thereby achieving the optimal lift and torque control output.

[0056] Step 300: Control the rotation speed and steering angle of the cycloidal propeller of the UAV according to the optimal control quantity.

[0057] The optimized nonlinear prediction model outputs the position to control the lift of the drone, and the output attitude vector controls the three-axis torque of the drone, and determines the target yaw angle, target roll angle and target pitch angle of the drone according to the external disturbance torque. Finally, the rotation speed and control angle of the four cycloid propellers are allocated according to the desired lift and torque.

[0058] By simplifying the mathematical expression of the objective function, the present invention can transform the optimization problem of the nonlinear prediction model into a quadratic programming problem, that is, a convex optimization problem (a convex optimization problem refers to a problem of finding a convex function that is minimized on a convex set). Since the convex optimization problem has good mathematical properties, its global optimal solution can be efficiently solved. The control amount of lift and torque corresponding to the first step in the optimal solution of the optimization model is used as the input of the current UAV control to obtain the UAV controller response, and on this basis, the predictive control iteration is continued.

[0059] Please refer to Figure 3 The structural block diagram shown in the figure includes the expected trajectory, position controller, attitude controller, control distributor and UAV model. First, the expected position and attitude are obtained from the expected trajectory; secondly, the model predicts the position controller to output the expected attitude angle (roll) and total thrust for global trajectory tracking based on the expected position and current position. The nonlinear model predicts the attitude controller to output the expected torque based on the expected attitude and current attitude; then the control distributor controls the distribution and outputs the speed and force vector deflection angle of each cycloidal propeller; finally, the closed-loop system feeds back the position and attitude information through the sensor to ensure the accuracy of trajectory tracking.

[0060] Specifically, according to the expected trajectory, the expected position and the first attitude vector r(k) in discrete time are solved. According to the dynamics and dynamics model of the UAV, a discrete state space model is established: is the state variable, u s (k) = [f x ,f z ,τ x ,τ y ,τ z ] T is the input, ω s (ks) represents the disturbance received by the system at time k, and the discrete state space equation is established.

[0061] The discrete state space equation is expressed as:

[0062] Then the discrete state space model is optimized to obtain the state space model:

[0063]

[0064] Furthermore, in order to make the motion trajectory of the UAV meet the smoothness condition, the increment of the state variable and the output of the nonlinear prediction model x(k)=[Δx s (k) T , y(k) T ] T The state-space equations are reconstructed.

[0065] For example, using the increment of the state variable Δx and the nonlinear prediction model output x(k)=[Δx s (k) T , y(k) T ] T The state-space equations are reconstructed.

[0066] The state space equation is:

[0067]

[0068] Write the kinetic model in matrix form to obtain the coefficient matrix A s , B s , C s , and due to the reconstruction of the state space, we can get the reconstructed matrices A, B, C, where I is the unit vector.

[0069] Specifically, based on the kinetic equation:

[0070]

[0071] The model written in matrix form:

[0072]

[0073] Then reconstruct according to the increment of the state vector;

[0074] x(k)=[Δx s (k) T , y(k) T ] T ;

[0075] get:

[0076] Then, a nonlinear predictive model controller is designed according to the state space equation and the optimal control quantity is obtained. The error vector e(k) is obtained by obtaining the state space output y(k) and the desired trajectory vector, and then the objective function expression of the model predictive control is constructed according to the error vector and the input vector.

[0077] In one embodiment, the error vector is obtained by calculating the difference between the output second posture vector y(k) of the state-space model and the expected first posture vector r(k): e(k)=r(k)-y(k).

[0078] The objective function expression of the model predictive control is established according to the error vector e(k) and Δu.

[0079] The objective function is:

[0080]

[0081] Where (k+i|k) is the predicted value of k+1 at time k, N p is the prediction interval, N c is the control interval, Q is the state error weight matrix, R represents the input weight matrix, Q t is the UAV terminal weight; Δu represents the control amount increment.

[0082] Finally, by solving the objective function, the global optimal solution of the nonlinear prediction model for UAV tracking can be efficiently solved. The lift and torque control quantities corresponding to the first step in the global optimal solution of the nonlinear prediction model are used as the current control input and applied to the UAV control system to obtain a response. On this basis, the predictive control iteration is continued to achieve tracking control in multiple control intervals.

[0083] See also Figure 4 , Figure 4 It is a three-dimensional simulation trajectory tracking diagram of a gyroplane based on a spiral line. It can be seen from the figure that the gyroplane trajectory tracking control method proposed in the present invention can achieve the control target well and show excellent performance in the gyroplane trajectory tracking task.

[0084] See also Figure 5 The present invention also provides a roller aircraft trajectory tracking control device, including: a data acquisition module 51, a prediction module 52, and a control module 53.

[0085] The data acquisition module 51 is used to obtain the expected trajectory of the UAV, and obtain the expected position and first attitude vector r(k) in discrete time according to the expected trajectory. The position and the first attitude vector r(k) are used to input the nonlinear prediction model of the UAV.

[0086] The prediction module 52 is used to establish a state space model according to the dynamic and kinematic models of the UAV, and optimize the nonlinear prediction model according to the state space model so that the nonlinear prediction model outputs an optimal control variable for controlling the UAV.

[0087] For example, according to the dynamics and dynamics model of the drone, a discrete state space model is established, and the discrete state space model is optimized to obtain the state space model. The desired position in discrete time, the first attitude vector r(k) and the current position and second attitude vector of the drone are used as state variables, the output of the nonlinear prediction model is used as input, and the disturbance coefficient to which the drone is subjected is added, and the discrete state space model is optimized to obtain the state space model.

[0088] The output data of the nonlinear prediction model is input into the state space model, and an error vector is calculated based on the output of the state space model, and an objective function is constructed based on the error vector and the first posture vector r(k); the nonlinear prediction model is optimized based on the objective function. The error vector is obtained by calculating the difference between the output second posture vector y(k) of the state space model and the expected first posture vector r(k): e(k)=r(k)-y(k).

[0089] The control module 53 is used to control the rotation speed and control angle of the cycloidal propeller of the UAV according to the optimal control amount.

[0090] For the description of the roller-wing aircraft trajectory tracking control device, reference may be made to the above-mentioned roller-wing aircraft trajectory tracking control method, which will not be described in detail.

[0091] The present invention also provides an electronic device, comprising:

[0092] At least one processor; and at least one memory in communication with the processor, wherein: the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the above-mentioned roller aircraft trajectory tracking control method. The method includes: obtaining the expected trajectory of the UAV, obtaining the expected position and first attitude vector r(k) in discrete time according to the expected trajectory, and the position and the first attitude vector r(k) are used to input the nonlinear prediction model of the UAV. A state space model is established according to the dynamic and kinematic models of the UAV, the output data of the nonlinear prediction model is input into the state space model, and the error vector is calculated based on the output of the state space model, and the objective function is constructed based on the error vector and the first attitude vector r(k); the nonlinear prediction model is optimized based on the objective function. Finally, the purpose of optimizing the nonlinear prediction model according to the state space model is achieved, so that the nonlinear prediction model outputs the optimal control quantity for controlling the UAV; the rotation speed and steering angle of the cycloidal propeller of the UAV are controlled according to the optimal control quantity.

[0093] The present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned roller trajectory tracking control method is implemented. The method includes: obtaining the expected trajectory of the drone, obtaining the expected position and the first attitude vector r(k) in discrete time according to the expected trajectory, and the position and the first attitude vector r(k) are used to input the nonlinear prediction model of the drone. A state space model is established according to the dynamic and kinematic models of the drone, the output data of the nonlinear prediction model is input into the state space model, and the error vector is calculated based on the output of the state space model, and the objective function is constructed based on the error vector and the first attitude vector r(k); the nonlinear prediction model is optimized based on the objective function. Finally, the purpose of optimizing the nonlinear prediction model according to the state space model is achieved, so that the nonlinear prediction model outputs the optimal control quantity for controlling the drone; the rotation speed and steering angle of the cycloidal propeller of the drone are controlled according to the optimal control quantity.

[0094] The present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it is implemented to execute the above-mentioned roller aircraft trajectory tracking control method. The method includes: obtaining the expected trajectory of the drone, obtaining the expected position and first attitude vector r(k) in discrete time according to the expected trajectory, and the position and the first attitude vector r(k) are used to input the nonlinear prediction model of the drone. A state space model is established according to the dynamic and kinematic models of the drone, the output data of the nonlinear prediction model is input into the state space model, and the error vector is calculated based on the output of the state space model, and the objective function is constructed based on the error vector and the first attitude vector r(k); the nonlinear prediction model is optimized based on the objective function. Finally, the purpose of optimizing the nonlinear prediction model according to the state space model is achieved, so that the nonlinear prediction model outputs the optimal control amount for controlling the drone; the rotation speed and steering angle of the cycloidal propeller of the drone are controlled according to the optimal control amount.

[0095] It can be understood that computer-readable storage media may include: any entity or device capable of carrying a computer program, recording media, USB flash drives, mobile hard disks, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), and software distribution media, etc. A computer program includes computer program code. The computer program code may be in source code form, object code form, executable file, or some intermediate form, etc. A computer-readable storage medium may include: any entity or device capable of carrying a computer program code, recording media, USB flash drives, mobile hard disks, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), and software distribution media, etc.

[0096] In certain embodiments of the present invention, the electronic device may include a controller, which is a single-chip microcomputer chip that integrates a processor, a memory, a communication module, etc. The processor may refer to a processor included in the controller. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc.

[0097] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0098] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0099] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A trajectory tracking control method for a gyroplane, characterized in that: include: Acquire an expected trajectory of the UAV, and obtain an expected position and a first attitude vector r(k) in discrete time according to the expected trajectory, wherein the position and the first attitude vector r(k) are used to input a nonlinear prediction model of the UAV; Establishing a state space model according to the dynamics and kinematics models of the UAV, and optimizing the nonlinear prediction model according to the state space model so that the nonlinear prediction model outputs an optimal control variable for controlling the UAV; The rotation speed and steering angle of the cycloidal propeller of the UAV are controlled according to the optimal control amount.

2. The trajectory tracking control method of a gyroplane according to claim 1, characterized in that: The optimizing the nonlinear prediction model according to the state space model comprises: Inputting the output data of the nonlinear prediction model into a state space model, calculating an error vector based on the output of the state space model, and constructing an objective function based on the error vector and the first posture vector r(k); The nonlinear prediction model is optimized based on the objective function.

3. The trajectory tracking control method of a gyroplane according to claim 2, characterized in that: The calculating an error vector based on the output of the state space model and constructing an objective function based on the error vector and the first posture vector r(k) include: The error vector is obtained by calculating the difference between the output second posture vector y(k) of the state space model and the expected first posture vector r(k): e(k)=r(k)-y(k); The objective function is: Where (k+i|k) is the predicted value of k+1 at time k, N p is the prediction interval, N c is the control interval, Q is the state error weight matrix, R represents the input weight matrix, Q t is the UAV terminal weight; Δu represents the control amount increment.

4. The trajectory tracking control method of a gyroplane according to claim 1 or 2, characterized in that: The state space model is established according to the dynamics and kinematics model of the UAV, including: According to the dynamics and dynamics model of the UAV, a discrete state space model is established, and the discrete state space model is optimized to obtain the state space model.

5. The trajectory tracking control method of a gyroplane according to claim 4, characterized in that: The step of optimizing the discrete state space model to obtain the state space model comprises: The desired position in discrete time, the first attitude vector r(k) and the current position and second attitude vector of the UAV are taken as state variables, the output of the nonlinear prediction model is taken as input, and the disturbance coefficient of the UAV is added to optimize the discrete state space model to obtain the state space model.

6. The trajectory tracking control method of a gyroplane according to claim 5, characterized in that: The step of establishing a state space model according to the dynamics and kinematics model of the UAV further includes: Using the increment of the state variable and the output of the nonlinear prediction model x(k)=[Δxs(k) T , y(k) T ] T Reconstruct the state space; the state space equation is: Write the kinetic model in matrix form to obtain the coefficient matrix A s , B s , C s , and due to the reconstruction of the state space, we can get the reconstructed matrices A, B, C, where I is the unit vector.

7. A trajectory tracking control device for a gyroplane, characterized in that: include: A data acquisition module, used to acquire an expected trajectory of the UAV, and obtain an expected position and a first attitude vector r(k) in discrete time according to the expected trajectory, wherein the position and the first attitude vector r(k) are used to input into a nonlinear prediction model of the UAV; A prediction module, used to establish a state space model according to the dynamics and kinematics models of the UAV, and optimize the nonlinear prediction model according to the state space model so that the nonlinear prediction model outputs an optimal control variable for controlling the UAV; A control module is used to control the rotation speed and steering angle of the cycloidal propeller of the drone according to the optimal control amount.

8. An electronic device, characterized in that: include: at least one processor; and at least one memory communicatively connected to the processor, wherein: the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the roller trajectory tracking control method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a computer, the trajectory tracking control method of the rolling-wing aircraft as claimed in any one of claims 1 to 6 is executed.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the trajectory tracking control method of a gyroplane as claimed in any one of claims 1 to 6 is implemented.

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