Hovering control method and device for tilting rotor type short-distance vertical take-off and landing aircraft

Abstract

The invention relates to the technical field of flight control, in particular to a tilting rotor type short-distance vertical take-off and landing aircraft hovering control method and device, and the method comprises the steps: constructing a short-distance vertical take-off and landing aircraft hovering nonlinear kinetic model comprising a tilting rotor power system; the short-distance vertical take-off and landing aircraft hovering nonlinear kinetic model is converted into a high-order all-wheel-drive system model so as to design a state tracking controller; designing an adaptive law based on a neural network, and estimating a nonlinear term in the short-range vertical take-off and landing aircraft hovering nonlinear dynamic model; and feeding back the non-linear term to a state tracking controller so as to perform hovering mode stability control. Therefore, the problems of low decoupling efficiency, low robust control performance and the like of multiple execution mechanisms of the aircraft due to the fact that a suspension control method based on dynamic inverse needs a large amount of linearization processing, the dynamic characteristic difference of the actuators is obvious and the closed-loop performance of the controller needs to be verified through repeated simulation and experiments in the related technology are solved.

Description

Technical Field

[0001] The present application relates to the field of flight control technology, and in particular to a method and device for controlling the hovering of a tiltrotor short-takeoff and vertical landing aircraft. Background Art

[0002] The tilt-rotor short-distance vertical take-off and landing aircraft achieves vertical take-off and landing and hovering functions through the coordinated control of multiple actuators such as the left main rotor, right main rotor, left tail rotor, and right tail rotor, combining the advantages of high-speed cruising of fixed-wing aircraft and flexible take-off and landing of helicopters.

[0003] In related technologies, the hovering control of such complex power systems mainly adopts a dynamic inversion-based method, which linearizes the nonlinear model at multiple equilibrium points and relies on a precise control allocation matrix to achieve coordinated control of various actuators.

[0004] However, such methods have significant limitations. First, nonlinear models must be linearized at a large number of operating points, making the modeling process cumbersome and difficult to meet real-time control requirements. Second, the dynamic characteristics of actuators in actual systems vary significantly (for example, the main rotor and tail rotor have different dynamic characteristics depending on their size), making it extremely difficult to accurately obtain the control allocation matrix, which can easily lead to allocation mismatch problems. Furthermore, controllers designed using traditional methods require repeated simulation and experimental verification of closed-loop performance, resulting in long development cycles and high costs. Therefore, a hovering control method that simplifies the model processing, achieves dynamic decoupling of actuators, and improves control robustness is urgently needed.

[0005] In summary, in the relevant technologies, the hovering control method based on dynamic inversion requires a large amount of linearization processing, the dynamic characteristics of the actuators vary significantly, and the controller needs to verify the closed-loop performance through repeated simulations and experiments, which leads to low decoupling efficiency of the aircraft's multiple actuators and low robust control performance; improvements are urgently needed. Summary of the Invention

[0006] The present application provides a hover control method and device for a tiltrotor short take-off and landing aircraft to solve the problems in related technologies, such as low decoupling efficiency of multiple actuators of the aircraft and low robust control performance, due to the fact that the hover control method based on dynamic inversion requires a large amount of linearization processing, the dynamic characteristics of the actuators vary significantly, and the controller needs to verify the closed-loop performance through repeated simulation and experiments.

[0007] A first aspect of the present application provides a method for controlling the hovering of a tilt-rotor short take-off and landing aircraft, comprising the following steps: constructing a nonlinear dynamic model of the hovering of the short vertical take-off and landing aircraft including a tilt-rotor power system using at least one of the nonlinear relationships among aircraft speed, Euler angles, angular velocity, thrust, and torque; converting the nonlinear dynamic model of the hovering of the short vertical take-off and landing aircraft into a high-order all-wheel drive system model to design a state tracking controller; designing an adaptive law based on a neural network to estimate the nonlinear terms in the nonlinear dynamic model of the hovering of the short vertical take-off and landing aircraft; and feeding back the nonlinear terms to the state tracking controller to perform hovering modal stabilization control.

[0008] Through the above technical solution, the embodiment of the present application can construct a nonlinear dynamic model of the hovering of a short-distance vertical take-off and landing aircraft including a tilt-rotor power system and convert the model into a high-order all-wheel drive system model. The hovering model of the tilt-rotor short-distance vertical take-off and landing aircraft is upgraded and decoupled through the high-order all-wheel drive system theory, thereby reducing the model dimension, simplifying the complex multi-point linearization process in the traditional dynamic inverse method, and eliminating the dependence on the precise control allocation matrix; further introducing the neural network adaptive law to estimate the nonlinear terms in the model in real time; and then feeding back the nonlinear terms to the state tracking controller to perform hovering modal stability closed-loop control; the performance of the closed-loop system can be predicted through the parameter matrix, reducing the number of iterations of repeated simulation and experimental verification in the traditional method, and greatly reducing the development cycle and cost.

[0009] Optionally, in one embodiment of the present application, the state of the short take-off and landing aircraft hovering nonlinear dynamic model includes at least one of forward speed, lateral speed, vertical speed, roll angle, pitch angle, yaw angle, roll angle rate, pitch angle rate, and yaw angle rate, and the control input includes at least one of main rotor thrust, right main rotor thrust, left tail rotor thrust, and right tail rotor thrust.

[0010] Through the above technical solution, the embodiment of the present application can achieve high-precision hovering control by reasonably defining state variables, laying a good data foundation for complex tasks.

[0011] Optionally, in one embodiment of the present application, the state tracking controller is:

[0012]

[0013] Where U represents the control input, e X is the state tracking error, represents the block diagonal feature matrix, B x Represents the corresponding model input matrix, f x is the nonlinear term of the corresponding model.

[0014] Through the above technical solution, the embodiment of the present application can design a state tracking controller based on a high-order all-wheel drive system, which is then used to track hovering state instructions and adjust control inputs, thereby further achieving hovering stability control of the aircraft.

[0015] Optionally, in one embodiment of the present application, the neural network design adaptive law is:

[0016]

[0017] Among them, ρ, σ are the parameters to be designed, W is the neural network weight vector, Φ is the neural network basic vector function matrix, is the nonlinear term f x The estimated value of X is the state tracking error.

[0018] Through the above technical solution, the embodiment of the present application can introduce the neural network adaptive law to estimate the nonlinear terms in the model in real time, and further feed the nonlinear terms back to the state tracking controller, thereby significantly enhancing the robustness of the model.

[0019] A second aspect of the present application provides a tilt-rotor short take-off and landing aircraft hovering control device, including: a construction module for constructing a short vertical take-off and landing aircraft hovering nonlinear dynamic model including a tilt-rotor power system using at least one of the nonlinear relationships among aircraft speed, Euler angle, angular velocity, thrust and torque; a design module for converting the short vertical take-off and landing aircraft hovering nonlinear dynamic model into a high-order all-wheel drive system model to design a state tracking controller; an estimation module for designing an adaptive law based on a neural network to estimate the nonlinear terms in the short vertical take-off and landing aircraft hovering nonlinear dynamic model; and a control module for feeding back the nonlinear terms to the state tracking controller to perform hovering modal stability control.

[0020] Through the above technical solution, the embodiment of the present application can construct a nonlinear dynamic model of the hovering of a short-distance vertical take-off and landing aircraft including a tilt-rotor power system and convert the model into a high-order all-wheel drive system model. The hovering model of the tilt-rotor short-distance vertical take-off and landing aircraft is upgraded and decoupled through the high-order all-wheel drive system theory, thereby reducing the model dimension, simplifying the complex multi-point linearization process in the traditional dynamic inverse method, and eliminating the dependence on the precise control allocation matrix; further introducing the neural network adaptive law to estimate the nonlinear terms in the model in real time; and then feeding back the nonlinear terms to the state tracking controller to perform hovering modal stability closed-loop control; the performance of the closed-loop system can be predicted through the parameter matrix, reducing the number of iterations of repeated simulation and experimental verification in the traditional method, and greatly reducing the development cycle and cost.

[0021] Optionally, in one embodiment of the present application, the state of the short take-off and landing aircraft hovering nonlinear dynamic model includes at least one of forward speed, lateral speed, vertical speed, roll angle, pitch angle, yaw angle, roll angle rate, pitch angle rate, and yaw angle rate, and the control input includes at least one of main rotor thrust, right main rotor thrust, left tail rotor thrust, and right tail rotor thrust.

[0022] Through the above technical solution, the embodiment of the present application can achieve high-precision hovering control by reasonably defining state variables, laying a good data foundation for complex tasks.

[0023] Optionally, in one embodiment of the present application, the state tracking controller is:

[0024]

[0025] Where U represents the control input, e X is the state tracking error, represents the block diagonal feature matrix, B x Represents the corresponding model input matrix, f x is the nonlinear term of the corresponding model.

[0026] Through the above technical solution, the embodiment of the present application can design a state tracking controller based on a high-order all-wheel drive system, which is then used to track hovering state instructions and adjust control inputs, thereby further achieving hovering stability control of the aircraft.

[0027] Optionally, in one embodiment of the present application, the neural network design adaptive law is:

[0028]

[0029] Among them, ρ, σ are the parameters to be designed, W is the neural network weight vector, Φ is the neural network basic vector function matrix, is the nonlinear term f x The estimated value of X is the state tracking error.

[0030] Through the above technical solution, the embodiment of the present application can introduce the neural network adaptive law to estimate the nonlinear terms in the model in real time, and further feed the nonlinear terms back to the state tracking controller, thereby significantly enhancing the robustness of the model.

[0031] A third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the hovering control method for a tilt-rotor short vertical take-off and landing aircraft as described in the above embodiment.

[0032] A fourth aspect of the present application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned method for controlling the hovering of a tilt-rotor short vertical take-off and landing aircraft.

[0033] A fifth aspect of the present application provides a computer program product, which stores a computer program that, when executed by a processor, implements the above-mentioned method for hovering control of a tilt-rotor short-distance vertical take-off and landing aircraft.

[0034] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0036] Figure 1 This is a flow chart of a method for hovering control of a tilt-rotor short vertical take-off and landing aircraft provided according to an embodiment of the present application;

[0037] Figure 2 Schematic diagram of a power system for a tiltrotor short take-off and vertical landing aircraft according to a specific embodiment of the present application;

[0038] Figure 3 This is a schematic diagram illustrating variables of a power system of a tiltrotor short-takeoff and vertical landing aircraft according to a specific embodiment of the present application;

[0039] Figure 4 This is a schematic structural diagram of a method for controlling a hovering of a tiltrotor short-distance vertical take-off and landing aircraft based on an all-wheel drive system according to a specific embodiment of the present application;

[0040] Figure 5 This is a flow chart of a method for hovering control of a tiltrotor short-distance vertical take-off and landing aircraft based on an all-wheel drive system according to a specific embodiment of the present application;

[0041] Figure 6 Schematic diagram of a hovering control device for a tiltrotor short vertical take-off and landing aircraft according to an embodiment of the present application;

[0042] Figure 7 A schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0043] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0044] The following describes the tilt-rotor short-distance vertical take-off and landing aircraft hovering control method and device of the embodiment of the present application with reference to the accompanying drawings. In view of the related technologies mentioned in the above background technology center, since the hovering control method based on dynamic inversion requires a lot of linearization processing, the dynamic characteristics of the actuators vary significantly, and the controller needs to verify the closed-loop performance through repeated simulation and experiments, which leads to the problems of low decoupling efficiency of the aircraft's multiple actuators and low robust control performance, the present application provides a tilt-rotor short-distance vertical take-off and landing aircraft hovering control method. In this method, a nonlinear dynamic model of the short-distance vertical take-off and landing aircraft hovering including a tilt-rotor power system can be constructed and the model can be converted into a high-order full-drive system model, and the design state can be followed. tracking controller; through the high-order all-wheel drive system theory, the hovering model of the tilt-rotor short-distance vertical take-off and landing aircraft is upgraded and decoupled, reducing the model dimension, simplifying the complex multi-point linearization process in the traditional dynamic inversion method, and eliminating the dependence on the precise control allocation matrix; further introducing the neural network adaptive law to estimate the nonlinear terms in the model in real time; and then feeding the nonlinear terms back to the state tracking controller to perform hovering modal stability closed-loop control; the performance of the closed-loop system can be predicted through the parameter matrix, reducing the number of iterations of repeated simulation and experimental verification in the traditional method, significantly reducing the development cycle and cost. This solves the problems in related technologies such as low decoupling efficiency of the aircraft's multiple actuators and low robust control performance due to the need for a large amount of linearization processing, significant differences in the dynamic characteristics of the actuators, and the need for the controller to verify the closed-loop performance through repeated simulation and experiments.

[0045] Specifically, Figure 1 A schematic flow chart of a hovering control method for a tilt-rotor short take-off and landing aircraft provided in an embodiment of the present application.

[0046] like Figure 1 As shown, the hovering control method of the tilt-rotor short-distance vertical take-off and landing aircraft includes the following steps:

[0047] In step S101 , a nonlinear dynamic model of a short take-off and landing aircraft hovering including a tiltrotor power system is constructed using at least one of the nonlinear relationships among aircraft speed, Euler angles, angular velocity, thrust, and torque.

[0048] It can be understood that the tilt-rotor power system is an advanced aviation propulsion technology that combines the characteristics of fixed-wing aircraft and helicopters. Its core lies in achieving switching between vertical take-off and landing and high-speed forward flight mode through rotor tilt.

[0049] like Figure 2 As shown, the power system of the tilt-rotor short-distance vertical take-off and landing aircraft includes: a left main rotor, a right main rotor, a left tail rotor and a right tail rotor.

[0050] Among them, the thrust generated by the left main rotor, right main rotor, left tail rotor and right tail rotor provides vertical lift;

[0051] The thrust difference between the left main rotor and the right main rotor, and the thrust difference between the left tail rotor and the right tail rotor generate a rolling moment to adjust the aircraft's roll attitude;

[0052] The thrust difference between the left and right main rotors and the left and right tail rotors generates a pitching moment that adjusts the aircraft's pitch attitude.

[0053] Figure 3 This is a description of the power system variables of the tilt-rotor short-distance vertical take-off and landing aircraft provided in the embodiment of the present application. The variables include: left main rotor thrust F ML , right main propeller thrust F MR , left tail rotor thrust F TL and right tail rotor thrust F TR The forward and lateral distances x from the thrust points of the left and right main propellers to the center of mass M ,y M ; The forward and lateral distances x from the thrust action points of the left and right tail rotors to the center of mass T ,y T .

[0054] In actual implementation, the embodiment of the present application can establish a hovering model of a thrust vectoring short-distance vertical take-off and landing aircraft through the following formula:

[0055]

[0056] Among them, u, v, w are the aircraft speeds, φ, θ, ψ are the Euler angles, p, q, r are the angular velocities, and m is the aircraft mass. c5=(I z -I x ) / I y , c6=I xz / I y , c7=1 / I y , I x ,I y ,I z Represents the moment of inertia of the aircraft along three axes, I xz is the product of inertia. x ,Fy ,F z is the thrust force on the aircraft expressed in the body coordinate system, M x ,M y ,M z is the expression of the moment acting on the aircraft in the body coordinate system. The specific calculation is:

[0057]

[0058] Among them, F ML is the left main propeller thrust, F MR is the right main propeller thrust, F TL is the left tail rotor thrust and F TR is the right tail rotor thrust; x M ,y M is the forward and lateral distance from the thrust action points of the left and right main propellers to the center of mass; x T ,y T are the forward and lateral distances from the thrust application points of the left and right tail rotors to the center of mass; g is the acceleration due to gravity.

[0059] Through the above technical solution, the embodiment of the present application can achieve high precision, strong robustness and energy optimization of the hovering control of the tilt-rotor aircraft through nonlinear dynamic modeling, providing a theoretical basis for autonomous take-off and landing and complex tasks, and used to describe the nonlinear dynamic characteristics of aircraft speed, attitude angle and angular velocity.

[0060] In step S102 , the nonlinear dynamic model of the short vertical take-off and landing aircraft hovering is converted into a high-order all-wheel drive system model to design a state tracking controller.

[0061] Among them, the high-order all-wheel drive system model can be understood as a modeling method that can accurately describe the dynamic behavior of multi-variable, strongly coupled, and nonlinear systems. It can be applied to advanced aircraft such as tilt-rotor aircraft with complex power coupling and omnidirectional control capabilities.

[0062] Specifically, the controller design can be implemented in the embodiment of the present application through the following scheme:

[0063] In the short takeoff / hover mode, the main state variables of the aircraft are forward velocity, lateral velocity, and vertical velocity respectively; and the control input is U = [F ML ,F MR ,F TL ,F TR ] T ; Define state vector x: = [x1, x2, x3] T , where x1:=u, x2:=v, x3:=w.

[0064] First, the hovering model of the tilt-rotor short-takeoff and vertical landing aircraft is converted into a high-order all-wheel drive system model. The process is as follows.

[0065] First, derive each state and establish a direct relationship between the state and the control variable:

[0066] First state:

[0067]

[0068] in, are the intermediate transition nonlinear term and the total nonlinear term of the corresponding state respectively.

[0069] Second state:

[0070]

[0071] in, are the intermediate transition nonlinear term and the total nonlinear term of the corresponding state respectively.

[0072] The third state:

[0073]

[0074] Among them, f3 is the total nonlinear term of the corresponding state.

[0075] The equation is organized into a compact form as follows:

[0076]

[0077] Among them, f x :=[f1,f2,f3] T ;

[0078]

[0079] Then, define the hover state tracking error. Define the state expansion vector is the desired state instruction, then the state tracking error is e X =XX c

[0080] Finally, a thrust vectoring short-distance vertical take-off and landing aircraft hovering controller, namely a state tracking controller, is designed. The hovering controller is designed as follows:

[0081]

[0082] in, Indicated by A k The block matrix composed of is the characteristic matrix of the corresponding state of the closed-loop system, k = 1, 2, 3, m1 = 3, m2 = 3, m3 = 1.

[0083] Through the above technical solution, the embodiment of the present application can design a tilt-rotor type vertical short take-off and landing aircraft hovering controller based on a high-order all-wheel drive system, which is then used to track hovering state instructions and adjust control inputs; the tilt-rotor type short take-off and landing aircraft hovering model is upgraded and decoupled through the high-order all-wheel drive system theory, which reduces the model dimension, simplifies the complex multi-point linearization process in the traditional dynamic inverse method, and eliminates the dependence on the precise control allocation matrix, significantly improving the controller design efficiency.

[0084] In step S103 , an adaptive law is designed based on a neural network to estimate nonlinear terms in a nonlinear dynamic model of a short take-off and landing aircraft in hovering.

[0085] It can be understood that in the high-order all-wheel drive model control of the tiltrotor power system, the adaptive law can be used as the core technology to deal with model uncertainties, external disturbances and dynamic changes of the actuator; it can enable the system to maintain stability and tracking performance in unknown or time-varying environments by adjusting the controller parameters or model estimation parameters online.

[0086] In the actual implementation process, regarding the design of the state tracking controller in the above embodiment, the nonlinear term cannot be directly obtained. Therefore, the embodiment of the present application can be obtained by introducing a neural network adaptive law. The design of the neural network adaptive law is as follows:

[0087]

[0088] Among them, ρ, σ are the parameters to be designed, W is the neural network weight vector, Φ is the neural network basic vector function matrix, is the nonlinear term f x estimated value.

[0089] Through the above technical solution, the embodiment of the present application can introduce the neural network adaptive law to estimate the nonlinear terms in the model in real time, and further feed the nonlinear terms back to the state tracking controller, thereby significantly enhancing the robustness of the model.

[0090] In step S104 , the nonlinear term is fed back to the state tracking controller, and the state tracking controller performs hovering mode stabilization control.

[0091] In the above embodiment, after the nonlinear terms in the model are estimated in real time by introducing the neural network adaptive law, the estimated nonlinear terms can be further fed back to the state tracking controller to perform hovering mode stabilization control.

[0092] like Figure 4As shown, the structure of the tilt-rotor short take-off and landing aircraft hovering control method based on the all-wheel drive system in the embodiment of the present application includes: a tilt-rotor short take-off and landing aircraft model (a nonlinear dynamic model of the short take-off and landing aircraft hovering), a tilt-rotor short take-off and landing aircraft hovering controller (a state tracking controller), and a neural network adaptive law.

[0093] The embodiment of the present application can feed back the nonlinear term to the state tracking controller, dynamically compensate for the coupling effect and unmodeled dynamics, and then achieve robust stabilization of the hovering attitude through closed-loop feedback, thereby significantly improving the control performance of the tilt-rotor system.

[0094] Optionally, in one embodiment of the present application, the state of the short vertical take-off and landing aircraft hovering nonlinear dynamic model includes at least one of forward speed, lateral speed, vertical speed, roll angle, pitch angle, yaw angle, roll angle rate, pitch angle rate, and yaw angle rate, and the control input includes at least one of main rotor thrust, right main rotor thrust, left tail rotor thrust, and right tail rotor thrust.

[0095] In practice, the nonlinear dynamics model of a hovering short-distance vertical takeoff and landing aircraft must accurately describe its multi-body coupling, strong nonlinearity, and environmental interference characteristics. The definition and modeling of its state variables must comprehensively consider the interactions between aerodynamics, propulsion, and control systems. Therefore, the state variables of the nonlinear dynamics model of a hovering short-distance vertical takeoff and landing aircraft can include velocity (such as forward velocity and lateral velocity), attitude angles (such as roll angle and yaw angle), angular velocity, and other state variables.

[0096] Through the above technical solution, the embodiment of the present application can achieve high-precision hovering control by reasonably defining state variables, laying a good data foundation for complex tasks.

[0097] Optionally, in one embodiment of the present application, the state tracking controller is:

[0098]

[0099] Where U represents the control input, e X is the state tracking error, represents the block diagonal feature matrix, B x Represents the corresponding model input matrix, f x is the nonlinear term of the corresponding model.

[0100] During actual execution, the embodiment of the present application can construct a state tracking controller based on parameters such as the state tracking error and the corresponding model nonlinear term.

[0101] Through the above technical solution, the embodiment of the present application can design a state tracking controller based on a high-order all-wheel drive system, which is then used to track hovering state instructions and adjust control inputs, thereby further achieving hovering stability control of the aircraft.

[0102] Optionally, in one embodiment of the present application, the neural network design adaptive law is:

[0103]

[0104] Among them, ρ, σ are the parameters to be designed, W is the neural network weight vector, Φ is the neural network basic vector function matrix, is the nonlinear term f x The estimated value of X is the state tracking error.

[0105] During the actual execution process, the embodiment of the present application can establish a neural network design adaptive law based on parameters such as the neural network weight vector and the state tracking error.

[0106] Through the above technical solution, the embodiment of the present application can introduce a neural network adaptive law to estimate the nonlinear terms in the model in real time, and then feed the nonlinear terms back to the state tracking controller, so as to achieve robust stabilization of the hovering attitude of the aircraft through closed-loop feedback.

[0107] As a specific example, Figure 5 As shown in FIG, the hovering control process of a tiltrotor short-distance vertical take-off and landing aircraft based on an all-wheel drive system is as follows:

[0108] Step 1: Establish a hovering dynamics model;

[0109] Step 2: Convert the model to a high-level all-wheel drive system and design a hover controller. This step includes:

[0110] Step 2.1: Convert the tiltrotor vertical short take-off and landing aircraft hovering model into a high-order all-wheel drive system model;

[0111] Step 2.2: Arrange the equation into a compact format;

[0112] Step 2.3: Define the hover state tracking error;

[0113] Step 2.4: Design a hover controller for a tiltrotor short take-off and vertical landing aircraft;

[0114] Step 3: Introduce the neural network adaptive law to estimate the nonlinear term;

[0115] Step 4: Replace the corresponding variables of the hovering controller with the information estimated by the neural network adaptive law to achieve closed-loop control.

[0116] Compared with existing technologies, the above control method can simplify the controller design process, reduce dependence on precise mathematical models, enhance robustness to nonlinearities, significantly shorten controller iteration verification time, and effectively improve the dynamic response performance of hovering attitude control.

[0117] According to the hover control method for a tiltrotor short-distance vertical take-off and landing aircraft proposed in an embodiment of the present application, a nonlinear dynamic model of the short-distance vertical take-off and landing aircraft hovering including a tiltrotor power system can be constructed and converted into a high-order all-wheel drive system model to design a state tracking controller. The hover model of the tiltrotor short-distance vertical take-off and landing aircraft is upgraded and decoupled using the high-order all-wheel drive system theory, reducing the model dimension, simplifying the complex multi-point linearization process in traditional dynamic inversion methods, and eliminating the reliance on precise control allocation matrices. A neural network adaptive law is further introduced to estimate the nonlinear terms in the model in real time. The nonlinear terms are then fed back to the state tracking controller to perform hover modal stability closed-loop control. The closed-loop system performance can be predicted using a parameter matrix, reducing the number of iterations required for repeated simulation and experimental verification in traditional methods, significantly reducing the development cycle and cost. This solves the problems in the related art of hover control methods based on dynamic inversion, which require extensive linearization processing, significant differences in actuator dynamic characteristics, and the need for repeated simulation and experimental verification of closed-loop performance, resulting in low decoupling efficiency and poor robust control performance of the aircraft's multiple actuators.

[0118] Next, refer to the attached Figure 6 A tiltrotor short take-off and landing aircraft hovering control device proposed in an embodiment of the present application is described.

[0119] Figure 6 It is a block diagram of a hovering control device for a tilt-rotor short-takeoff and vertical landing aircraft according to an embodiment of the present application.

[0120] like Figure 6 As shown, the tilt-rotor short vertical take-off and landing aircraft hovering control device 10 includes: a construction module 100 , a design module 200 , an estimation module 300 , and a control module 400 .

[0121] The construction module 100 is used to construct a nonlinear dynamic model of a short-distance vertical take-off and landing aircraft hovering including a tilt-rotor power system by using at least one of the nonlinear relationships among aircraft speed, Euler angles, angular velocity, thrust, and torque.

[0122] The design module 200 is used to convert the nonlinear dynamic model of the short vertical take-off and landing aircraft hovering into a high-order all-wheel drive system model to design a state tracking controller.

[0123] The estimation module 300 is used to design an adaptive law based on a neural network and estimate the nonlinear terms in the nonlinear dynamic model of the short take-off and landing aircraft in hovering.

[0124] The control module 400 is used to feed back the nonlinear term to the state tracking controller to perform hovering mode stabilization control.

[0125] Optionally, in one embodiment of the present application, the state of the short vertical take-off and landing aircraft hovering nonlinear dynamic model includes at least one of forward speed, lateral speed, vertical speed, roll angle, pitch angle, yaw angle, roll angle rate, pitch angle rate, and yaw angle rate, and the control input includes at least one of main rotor thrust, right main rotor thrust, left tail rotor thrust, and right tail rotor thrust.

[0126] Optionally, in one embodiment of the present application, the state tracking controller is:

[0127]

[0128] Where U represents the control input, e X is the state tracking error, represents the block diagonal feature matrix, B x Represents the corresponding model input matrix, f x is the nonlinear term of the corresponding model.

[0129] Optionally, in one embodiment of the present application, the neural network design adaptive law is:

[0130]

[0131] Among them, ρ, σ are the parameters to be designed, W is the neural network weight vector, Φ is the neural network basic vector function matrix, is the nonlinear term f x The estimated value of X is the state tracking error.

[0132] It should be noted that the aforementioned explanation of the embodiment of the tilt-rotor short vertical take-off and landing aircraft hovering control method is also applicable to the tilt-rotor short vertical take-off and landing aircraft hovering control device of this embodiment, and will not be repeated here.

[0133] According to the tiltrotor short vertical take-off and landing aircraft hovering control device proposed in the embodiment of the present application, a nonlinear dynamic model of the short vertical take-off and landing aircraft hovering including a tiltrotor power system can be constructed and converted into a high-order all-wheel drive system model to design a state tracking controller. The tiltrotor short vertical take-off and landing aircraft hovering model is upgraded and decoupled by using the high-order all-wheel drive system theory, thereby reducing the model dimension, simplifying the complex multi-point linearization process in the traditional dynamic inversion method, and eliminating the dependence on the precise control allocation matrix. A neural network adaptive law is further introduced to estimate the nonlinear terms in the model in real time. The nonlinear terms are then fed back to the state tracking controller to perform hover modal stability closed-loop control. The closed-loop system performance can be predicted by the parameter matrix, reducing the number of iterations of repeated simulation and experimental verification in the traditional method, significantly reducing the development cycle and cost. Thus, the invention solves the problems in the related art that the hovering control method based on dynamic inversion requires a large amount of linearization processing, the dynamic characteristics of the actuators vary significantly, and the controller needs to verify the closed-loop performance through repeated simulation and experiment, which leads to low decoupling efficiency of multiple actuators of the aircraft and poor robust control performance.

[0134] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0135] Memory 701 , processor 702 , and computer programs stored in the memory 701 and executable on the processor 702 .

[0136] When the processor 702 executes the program, the hovering control method for the tilt-rotor short vertical take-off and landing aircraft provided in the above embodiment is implemented.

[0137] Furthermore, the electronic device further includes:

[0138] The communication interface 703 is used for communication between the memory 701 and the processor 702 .

[0139] The memory 701 is used to store computer programs that can be run on the processor 702 .

[0140] The memory 701 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0141] If the memory 701, processor 702, and communication interface 703 are implemented independently, the communication interface 703, memory 701, and processor 702 can be interconnected via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 7 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0142] Optionally, in a specific implementation, if the memory 701, the processor 702 and the communication interface 703 are integrated on a chip, the memory 701, the processor 702 and the communication interface 703 can communicate with each other through an internal interface.

[0143] The processor 702 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0144] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for controlling the hovering of a tilt-rotor short-distance vertical take-off and landing aircraft.

[0145] An embodiment of the present application also provides a computer program product, which stores a computer program. When the program is executed by a processor, it implements the above-mentioned tilt-rotor short-distance vertical take-off and landing aircraft hovering control method.

[0146] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0147] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of this application, "N" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0148] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing a custom logical function or process step, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed in a different order than shown or discussed, including performing functions in a substantially simultaneous manner or in a reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application pertain.

[0149] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or N wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program can be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing it in other suitable ways as necessary, and then storing it in a computer memory.

[0150] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiment, the N steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented using hardware, as in another embodiment, it can be implemented using any one or a combination of the following technologies known in the art: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0151] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0152] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0153] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A method for controlling a tiltrotor short take-off and vertical landing aircraft to hover, characterized in that: The following steps are involved: Constructing a nonlinear dynamic model of a short vertical take-off and landing aircraft hovering including a tiltrotor power system using at least one of a nonlinear relationship among aircraft speed, Euler angles, angular velocity, thrust, and torque; Converting the short-takeoff and vertical landing aircraft hovering nonlinear dynamics model into a high-order all-wheel drive system model to design a state tracking controller; Designing an adaptive law based on a neural network to estimate nonlinear terms in the nonlinear dynamics model of the short take-off and landing aircraft hovering; The nonlinear term is fed back to the state tracking controller to perform hovering mode stabilization control.

2. The method according to claim 1, characterized in that The state of the short take-off and landing aircraft hovering nonlinear dynamic model includes at least one of forward velocity, lateral velocity, vertical velocity, roll angle, pitch angle, yaw angle, roll angular rate, pitch angular rate, and yaw angular rate, and the control input includes at least one of main rotor thrust, right main rotor thrust, left tail rotor thrust, and right tail rotor thrust.

3. The method according to claim 1, characterized in that The state tracking controller is: Where U represents the control input, e X is the state tracking error, represents the block diagonal feature matrix, B x Represents the corresponding model input matrix, f x is the nonlinear term of the corresponding model.

4. The method according to claim 1, wherein The neural network design adaptive law is: Among them, ρ, σ are the parameters to be designed, W is the neural network weight vector, Φ is the neural network basic vector function matrix, is the nonlinear term f x The estimated value of X is the state tracking error.

5. A tiltrotor short take-off and vertical landing aircraft hovering control device, characterized in that: include: a construction module for constructing a nonlinear dynamic model of a short vertical take-off and landing aircraft hovering including a tiltrotor power system using at least one of a nonlinear relationship among aircraft speed, Euler angles, angular velocity, thrust, and torque; A design module for converting the short-takeoff and vertical landing aircraft hovering nonlinear dynamics model into a high-order all-wheel drive system model to design a state tracking controller; an estimation module, configured to design an adaptive law based on a neural network and estimate nonlinear terms in a nonlinear dynamic model of a hovering short take-off and landing aircraft; A control module is used to feed back the nonlinear term to the state tracking controller to perform hovering mode stabilization control.

6. The device according to claim 5, characterized in that The state of the short take-off and landing aircraft hovering nonlinear dynamic model includes at least one of forward velocity, lateral velocity, vertical velocity, roll angle, pitch angle, yaw angle, roll angular rate, pitch angular rate, and yaw angular rate, and the control input includes at least one of main rotor thrust, right main rotor thrust, left tail rotor thrust, and right tail rotor thrust.

7. The device according to claim 5, characterized in that The state tracking controller is: Where U represents the control input, e X is the state tracking error, represents the block diagonal feature matrix, B x Represents the corresponding model input matrix, f x is the nonlinear term of the corresponding model.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the hovering control method for a tilt-rotor short vertical take-off and landing aircraft according to any one of claims 1 to 4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the hovering control method for a tilt-rotor short-take-off and vertical landing aircraft according to any one of claims 1 to 4.

10. A computer program product comprising a computer program, characterized in that The computer program is executed to implement the hovering control method for a tilt-rotor short vertical take-off and landing aircraft according to any one of claims 1 to 4.

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