Hover control method and device for tilt-rotor short take-off and landing aircraft

Abstract

The application relates to the field of flight control technology, in particular to a hovering control method and device for a short take-off and vertical landing aircraft of a tilt-rotor type, wherein the method comprises the following steps: constructing a hovering nonlinear dynamics model of the short take-off and vertical landing aircraft containing a tilt-rotor power system; converting the hovering nonlinear dynamics model of the short take-off and vertical landing aircraft into a high-order full-drive system model to design a state tracking controller; designing an adaptive law based on a neural network to estimate nonlinear terms in the hovering nonlinear dynamics model of the short take-off and vertical landing aircraft; and feeding back the nonlinear terms to the state tracking controller to perform hovering mode stable control. Thus, the problems that the hovering control method based on dynamic inversion needs a large amount of linearization processing, the actuator dynamic characteristics are significantly different, the controller needs to verify the closed-loop performance through repeated simulation and experiment, and the multi-actuator decoupling efficiency of the aircraft is low and the robust control performance is poor are solved.

Description

Technical Field

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

[0002] Tiltrotor-type short takeoff and vertical landing (STOVL) aircraft achieve vertical takeoff and landing and hovering functions through the coordinated control of multiple actuators, including the left main rotor, right main rotor, left tail rotor, and right tail rotor, combining the advantages of high-speed cruise of fixed-wing aircraft and flexible takeoff and landing of helicopters.

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

[0004] However, such methods have significant limitations: First, nonlinear models require linearization at numerous operating points, making the modeling process cumbersome and difficult to meet real-time control requirements; second, the dynamic characteristics of actuators in real-world systems vary significantly (e.g., the main propeller and tail propeller differ in size and dynamic characteristics), making it extremely difficult to accurately obtain the control allocation matrix and easily leading to allocation mismatch problems; furthermore, controllers designed using traditional methods require repeated simulations and experiments to verify closed-loop performance, resulting in long development cycles and high costs. Therefore, there is an urgent need for a hovering control method that can simplify the model processing flow, achieve dynamic decoupling of actuators, and improve control robustness.

[0005] In summary, among the related technologies, hovering control methods based on dynamic inversion require extensive linearization processing, exhibit significant differences in actuator dynamic characteristics, and require repeated simulations and experiments to verify the closed-loop performance of the controller. This leads to problems such as low decoupling efficiency of multiple actuators and poor robust control performance in aircraft; these issues urgently need to be addressed. Summary of the Invention

[0006] This application provides a hovering control method and device for a tiltrotor short takeoff and vertical landing aircraft to solve the problems in related technologies, such as the need for extensive linearization processing, significant differences in actuator dynamic characteristics, and the need for repeated simulations and experiments to verify the closed-loop performance of the controller, which leads to low decoupling efficiency and poor robust control performance of the aircraft's multiple actuators.

[0007] The first aspect of this application provides a hovering control method for a tiltrotor short takeoff and landing (STOVL) aircraft, comprising the following steps: constructing a nonlinear dynamic model of STOVL hovering that includes a tiltrotor propulsion system using at least one of the nonlinear relationships of aircraft speed, Euler angle, angular velocity, thrust, and torque; converting the nonlinear dynamic model of STOVL hovering into a high-order all-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 STOVL hovering; and feeding the nonlinear terms back to the state tracking controller for hovering modal stabilization control.

[0008] Through the above technical solutions, the embodiments of this application can construct a hovering nonlinear dynamic model of a short takeoff and landing (STOVL) aircraft including a tiltrotor propulsion system and convert this model into a high-order all-drive system model. The hovering model of the tiltrotor STOVL aircraft is upgraded and decoupled using high-order all-drive system theory, reducing the model dimensionality, simplifying the complex multi-point linearization process in traditional dynamic inverse methods, and eliminating the dependence on precise control allocation matrices. Furthermore, a neural network adaptive law is introduced to estimate the nonlinear terms in the model in real time. These nonlinear terms are then fed back to the state tracking controller for 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 in repeated simulations and experimental verifications in traditional methods, and significantly reducing the development cycle and cost.

[0009] Optionally, in one embodiment of this application, the state of the hovering nonlinear dynamics model of the short takeoff and landing aircraft includes at least one of forward velocity, lateral velocity, vertical velocity, roll angle, pitch angle, yaw angle, roll rate, pitch rate, and yaw 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 solutions, the embodiments of this 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 this application, the state tracking controller is:

[0012]

[0013] Where U represents control input, e X For state tracking error, B represents the block diagonal characteristic matrix. x f represents the corresponding model input matrix. x This corresponds to the nonlinear term in the model.

[0014] Through the above technical solutions, the embodiments of this application can design a state tracking controller based on a high-order all-drive system, which can then be used to track hovering state commands and adjust control inputs, thereby further realizing hovering stability control of the aircraft.

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

[0016]

[0017] Where ρ and σ are the parameters to be designed, W is the neural network weight vector, and Φ is the neural network fundamental vector function matrix. For the nonlinear term f x The estimated value, e X This represents the state tracking error.

[0018] Through the above technical solution, the embodiments of this 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, thereby significantly enhancing the robustness of the model.

[0019] A second aspect of this application provides a hovering control device for a tiltrotor short takeoff and landing (STOVL) aircraft, comprising: a construction module for constructing a nonlinear dynamic model of STOVL hovering that includes a tiltrotor propulsion system using at least one of the nonlinear relationships of aircraft speed, Euler angle, angular velocity, thrust, and torque; a design module for converting the STOVL hovering nonlinear dynamic model into a high-order all-drive system model to design a state tracking controller; an estimation module for estimating the nonlinear terms in the STOVL hovering nonlinear dynamic model based on an adaptive law designed using a neural network; and a control module for feeding back the nonlinear terms to the state tracking controller for hovering modal stabilization control.

[0020] Through the above technical solutions, the embodiments of this application can construct a hovering nonlinear dynamic model of a short takeoff and landing (STOVL) aircraft including a tiltrotor propulsion system and convert this model into a high-order all-drive system model. The hovering model of the tiltrotor STOVL aircraft is upgraded and decoupled using high-order all-drive system theory, reducing the model dimensionality, simplifying the complex multi-point linearization process in traditional dynamic inverse methods, and eliminating the dependence on precise control allocation matrices. Furthermore, a neural network adaptive law is introduced to estimate the nonlinear terms in the model in real time. These nonlinear terms are then fed back to the state tracking controller for 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 in repeated simulations and experimental verifications in traditional methods, and significantly reducing the development cycle and cost.

[0021] Optionally, in one embodiment of this application, the state of the hovering nonlinear dynamics model of the short takeoff and landing aircraft includes at least one of forward velocity, lateral velocity, vertical velocity, roll angle, pitch angle, yaw angle, roll rate, pitch rate, and yaw 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 solutions, the embodiments of this 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 this application, the state tracking controller is:

[0024]

[0025] Where U represents control input, e X For state tracking error, B represents the block diagonal characteristic matrix. x f represents the corresponding model input matrix. x This corresponds to the nonlinear term in the model.

[0026] Through the above technical solutions, the embodiments of this application can design a state tracking controller based on a high-order all-drive system, which can then be used to track hovering state commands and adjust control inputs, thereby further realizing hovering stability control of the aircraft.

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

[0028]

[0029] Where ρ and σ are the parameters to be designed, W is the neural network weight vector, and Φ is the neural network fundamental vector function matrix. For the nonlinear term f x The estimated value, e X This represents the state tracking error.

[0030] Through the above technical solution, the embodiments of this 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, thereby significantly enhancing the robustness of the model.

[0031] A third aspect of this application provides an electronic device, including: 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 tiltrotor short takeoff and vertical landing aircraft as described in the above embodiments.

[0032] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described hovering control method for a tiltrotor short takeoff and vertical landing aircraft.

[0033] The fifth aspect of this application provides a computer program product that stores a computer program that, when executed by a processor, implements the above-described hovering control method for tiltrotor short takeoff and vertical landing aircraft.

[0034] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

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

[0036] Figure 1 This is a flowchart of a hovering control method for a tiltrotor short takeoff and vertical landing aircraft according to an embodiment of this application;

[0037] Figure 2 This is a schematic diagram of the power system configuration of a tiltrotor short takeoff and vertical landing aircraft according to a specific embodiment of this application;

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

[0039] Figure 4 This is a schematic diagram of a hovering control method for a tiltrotor short takeoff and vertical landing aircraft based on an all-drive system according to a specific embodiment of this application.

[0040] Figure 5 This is a schematic flowchart of a hovering control method for a tiltrotor short takeoff and vertical landing aircraft based on an all-drive system, according to a specific embodiment of this application.

[0041] Figure 6 This is a schematic diagram of a hovering control device for a tiltrotor short takeoff and vertical landing aircraft according to an embodiment of this application;

[0042] Figure 7 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0043] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0044] The hovering control method and apparatus for a tiltrotor short takeoff and vertical landing (SVTOL) aircraft according to embodiments of this application are described below with reference to the accompanying drawings. Addressing the issues raised in the background section regarding the related technologies, such as the need for extensive linearization processing, significant differences in actuator dynamic characteristics, and the requirement for repeated simulations and experimental verification of the controller's closed-loop performance in dynamic inverse-based hovering control methods, which lead to low decoupling efficiency and poor robust control performance of the aircraft's multiple actuators, this application provides a hovering control method for a tiltrotor SVTOL aircraft. In this method, a nonlinear dynamic model of the SVTOL aircraft's hovering, including the tiltrotor propulsion system, can be constructed and converted into a high-order all-drive system model. The design state and... This paper describes a state tracking controller for a tiltrotor short takeoff and vertical landing (STOVL) aircraft. By employing high-order all-drive system theory, the hovering model is upgraded and decoupled, reducing model dimensionality, simplifying the complex multi-point linearization process in traditional dynamic inverse methods, and eliminating dependence on precise control allocation matrices. Furthermore, an adaptive neural network law is introduced to estimate nonlinear terms in the model in real time. These nonlinear terms are then fed back to the state tracking controller for hovering modal stability closed-loop control. The closed-loop system performance can be predicted through the parameter matrix, reducing the number of iterations in traditional methods involving repeated simulations and experimental verifications, significantly lowering development cycles and costs. This addresses the problems in related technologies, such as the need for extensive linearization in dynamic inverse-based hovering control methods, significant differences in actuator dynamic characteristics, and the requirement for repeated simulations and experimental verification of controller closed-loop performance, which lead to low decoupling efficiency and poor robust control performance of multiple actuators in aircraft.

[0045] Specifically, Figure 1 This is a flowchart illustrating a hovering control method for a tiltrotor short takeoff and vertical landing aircraft provided in an embodiment of this application.

[0046] like Figure 1 As shown, the hovering control method for this tiltrotor short takeoff and vertical landing aircraft includes the following steps:

[0047] In step S101, a nonlinear dynamic model of hovering for a short takeoff and landing aircraft, including a tiltrotor propulsion system, is constructed using at least one of the nonlinear relationships of aircraft speed, Euler angle, angular velocity, thrust, and torque.

[0048] Understandably, tiltrotor propulsion systems are an advanced aviation propulsion technology that combines the characteristics of fixed-wing aircraft and helicopters. Its core lies in switching between vertical takeoff and landing and high-speed forward flight modes by tilting the rotor.

[0049] like Figure 2 As shown, the power system of a tiltrotor short takeoff and vertical landing aircraft consists of: a left main rotor, a right main rotor, a left tail rotor, and a right tail rotor.

[0050] 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 and right main rotors and the thrust difference between the left and right tail rotors generate a rolling torque that adjusts the aircraft's rolling 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 application provides a description of the variables for the power system of a tiltrotor short takeoff and vertical landing (STOVL) aircraft, including: 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 points of the left and right tail rotors to the center of mass. T ,y T .

[0054] In actual implementation, the hovering model of a thrust vectoring short takeoff and landing (STOVL) aircraft can be established using the following formula:

[0055]

[0056] Where u, v, w are the aircraft velocities, 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 I represents the moment of inertia of an aircraft along its three axes. xz F is the product of inertia. x ,Fy ,F z M represents the thrust acting on the aircraft in the body coordinate system. x M y M z The torque acting on the aircraft is represented in the body coordinate system, and the specific calculation is as follows:

[0057]

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

[0059] Through the above technical solutions, the embodiments of this application can achieve high precision, strong robustness and energy optimization in the hovering control of tiltrotor aircraft through nonlinear dynamic modeling, thereby providing a theoretical basis for autonomous take-off and landing and complex missions, and describing the nonlinear dynamic characteristics of aircraft speed, attitude angle and angular velocity.

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

[0061] Among them, the high-order all-drive system model can be understood as a modeling method that can accurately describe the dynamic behavior of multivariable, strongly coupled, nonlinear systems. It is applicable to advanced aircraft such as tiltrotor aircraft that have complex dynamic coupling and omnidirectional control capabilities.

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

[0063] In short takeoff / hovering mode, the aircraft's main state variables u, v, w are the forward velocity, lateral velocity, and vertical velocity, respectively; while the control input is U = [F ML ,F MR ,F TL ,F TR ] T Define the state vector x:=[x1,x2,x3] T , where x1:=u, x2:=v, x3:=w.

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

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

[0066] First state:

[0067]

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

[0069] Second state:

[0070]

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

[0072] The third state:

[0073]

[0074] Where f3 is the total nonlinear term for the corresponding state.

[0075] The equations are rearranged into a compact format as follows:

[0076]

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

[0078]

[0079] Next, define the hovering state tracking error. Define the state expansion vector. If the desired state command is given, then the state tracking error is e. X =XX c

[0080] Finally, a thrust vectoring short takeoff and vertical landing (STOVL) aircraft hover controller, i.e., a state tracking controller, was designed. The hover controller design is as follows:

[0081]

[0082] in, Indicates that by A k The resulting block matrix Let be 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 solutions, the embodiments of this application can design a hover controller for tiltrotor VTOL aircraft based on a high-order all-drive system, which can then be used to track hovering state commands and adjust control inputs. By using high-order all-drive system theory to upgrade and decouple the hovering model of tiltrotor VTOL aircraft, the model dimensionality is reduced, the complex multi-point linearization process in the traditional dynamic inverse method is simplified, and the dependence on precise control allocation matrix is ​​eliminated, significantly improving the controller design efficiency.

[0084] In step S103, the nonlinear terms in the hovering nonlinear dynamics model of a short takeoff and landing aircraft are estimated based on the adaptive law designed by the neural network.

[0085] Understandably, in the high-order all-drive model control of tiltrotor propulsion systems, adaptive laws can serve as the core technology for handling model uncertainties, external disturbances, and dynamic changes in actuators. By adjusting controller parameters or model estimation parameters online, the system can maintain stability and tracking performance even in unknown or time-varying environments.

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

[0087]

[0088] Where ρ and σ are the parameters to be designed, W is the neural network weight vector, and Φ is the neural network fundamental vector function matrix. For the nonlinear term f x The estimated value.

[0089] Through the above technical solution, the embodiments of this 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, thereby significantly enhancing the robustness of the model.

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

[0091] In the above embodiments, by introducing a neural network adaptive law to estimate the nonlinear terms in the model in real time, the estimated nonlinear terms can be further fed back to the state tracking controller for hovering mode stability control.

[0092] like Figure 4As shown, the hovering control method for tiltrotor short takeoff and vertical landing (STOVL) aircraft based on an all-drive system in this application includes: a tiltrotor STOVL aircraft model (STOVL aircraft hovering nonlinear dynamics model), a tiltrotor STOVL aircraft hovering controller (state tracking controller), and a neural network adaptive law.

[0093] The embodiments of this application can feed back nonlinear terms to the state tracking controller, dynamically compensate for coupling effects and unmodeled dynamics, and then achieve robust stability of hovering attitude through closed-loop feedback, significantly improving the control performance of the tiltrotor system.

[0094] Optionally, in one embodiment of this application, the state of the hovering nonlinear dynamics model of a short takeoff and landing aircraft includes at least one of forward velocity, lateral velocity, vertical velocity, roll angle, pitch angle, yaw angle, roll rate, pitch rate, and yaw 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 practical applications, the nonlinear dynamics model of a short takeoff and landing (STOVL) aircraft in hovering mode needs to accurately describe its multibody coupling, strong nonlinearity, and environmental disturbance characteristics; the definition and modeling of its state variables must comprehensively consider the interactions of aerodynamics, power, and control systems. Therefore, the states in the nonlinear dynamics model of a STOVL aircraft hovering can include state variables such as velocity (e.g., forward velocity, lateral velocity), attitude angles (e.g., roll angle, yaw angle), and angular velocity.

[0096] Through the above technical solutions, the embodiments of this 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 this application, the state tracking controller is:

[0098]

[0099] Where U represents control input, e X For state tracking error, B represents the block diagonal characteristic matrix. x f represents the corresponding model input matrix. x This corresponds to the nonlinear term in the model.

[0100] In actual implementation, the embodiments of this application can construct a state tracking controller based on parameters such as state tracking error and corresponding model nonlinear terms.

[0101] Through the above technical solutions, the embodiments of this application can design a state tracking controller based on a high-order all-drive system, which can then be used to track hovering state commands and adjust control inputs, thereby further realizing hovering stability control of the aircraft.

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

[0103]

[0104] Where ρ and σ are the parameters to be designed, W is the neural network weight vector, and Φ is the neural network fundamental vector function matrix. For the nonlinear term f x The estimated value, e X This represents the state tracking error.

[0105] In actual implementation, the embodiments of this application can establish an adaptive law for neural network design based on parameters such as neural network weight vector and state tracking error.

[0106] Through the above technical solution, the embodiments of this 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, thereby achieving robust stability of the hovering attitude of the aircraft through closed-loop feedback.

[0107] As a specific example, such as Figure 5 As shown, the hovering control process for a tiltrotor short takeoff and vertical landing (STOVL) aircraft based on an all-drive system is as follows:

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

[0109] Step Two: Convert the model into a high-order all-wheel drive system and design a hover controller; this step specifically includes:

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

[0111] Step 2.2: Reorganize the equations into a compact format;

[0112] Step 2.3: Define the hovering tracking error;

[0113] Step 2.4: Design a hovering controller for a tiltrotor short takeoff and vertical landing aircraft;

[0114] Step 3: Introduce a 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 the dependence on precise mathematical models, enhance robustness to nonlinearity, significantly shorten the controller iteration verification time, and effectively improve the dynamic response performance of hovering attitude control.

[0117] The hovering control method for tiltrotor short takeoff and vertical landing (SVTOL) aircraft proposed in this application can construct a nonlinear dynamic model of the SVTOL aircraft's hovering, including the tiltrotor propulsion system, and convert this model into a high-order all-drive system model, designing a state tracking controller. By using high-order all-drive system theory to upgrade and decouple the hovering model of the tiltrotor SVTOL aircraft, the model dimensionality is reduced, simplifying the complex multi-point linearization process in traditional dynamic inverse methods and eliminating the dependence on precise control allocation matrices. Furthermore, a neural network adaptive law is introduced to estimate the nonlinear terms in the model in real time. These nonlinear terms are then fed back to the state tracking controller for 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 in traditional methods involving repeated simulations and experimental verifications, significantly reducing development cycle and cost. This solves the problems in related technologies, such as the need for extensive linearization processing in dynamic inverse-based hovering control methods, significant differences in actuator dynamic characteristics, and the need for repeated simulations and experimental verification of the controller's closed-loop performance, which lead to low decoupling efficiency and poor robust control performance of the aircraft's multiple actuators.

[0118] Next, refer to the appendix. Figure 6 This application describes a hovering control device for a tiltrotor short takeoff and vertical landing (STOVL) aircraft according to embodiments thereof.

[0119] Figure 6 This is a block diagram of a hovering control device for a tiltrotor short takeoff and vertical landing aircraft according to an embodiment of this application.

[0120] like Figure 6 As shown, the hovering control device 10 for the tiltrotor short takeoff and vertical landing aircraft 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 hovering nonlinear dynamics model of a short takeoff and landing aircraft that includes a tiltrotor propulsion system by utilizing at least one of the nonlinear relationships of aircraft speed, Euler angle, angular velocity, thrust and torque.

[0122] Design module 200 is used to convert the hovering nonlinear dynamics model of a short takeoff and landing aircraft into a high-order all-drive system model in order to design a state tracking controller.

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

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

[0125] Optionally, in one embodiment of this application, the state of the hovering nonlinear dynamics model of a short takeoff and landing aircraft includes at least one of forward velocity, lateral velocity, vertical velocity, roll angle, pitch angle, yaw angle, roll rate, pitch rate, and yaw 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 this application, the state tracking controller is:

[0127]

[0128] Where U represents control input, e X For state tracking error, B represents the block diagonal characteristic matrix. x f represents the corresponding model input matrix. x This corresponds to the nonlinear term in the model.

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

[0130]

[0131] Where ρ and σ are the parameters to be designed, W is the neural network weight vector, and Φ is the neural network fundamental vector function matrix. For the nonlinear term f x The estimated value, e X This represents the state tracking error.

[0132] It should be noted that the foregoing explanation of the hovering control method embodiment for tiltrotor short takeoff and vertical landing aircraft also applies to the hovering control device for tiltrotor short takeoff and vertical landing aircraft in this embodiment, and will not be repeated here.

[0133] The hovering control device for tiltrotor short takeoff and vertical landing (SVTOL) aircraft proposed in this application can construct a nonlinear dynamic model of SVTOL aircraft hovering, including a tiltrotor propulsion system, and convert this model into a high-order all-drive system model, designing a state tracking controller. By using high-order all-drive system theory to upgrade and decouple the hovering model of the tiltrotor SVTOL aircraft, the model dimensionality is reduced, simplifying the complex multi-point linearization process in traditional dynamic inverse methods and eliminating the dependence on precise control allocation matrices. Furthermore, a neural network adaptive law is introduced to estimate the nonlinear terms in the model in real time. These nonlinear terms are then fed back to the state tracking controller for 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 in traditional methods involving repeated simulations and experimental verifications, significantly reducing development cycle and cost. This solves the problems in related technologies, such as the need for extensive linearization processing in dynamic inverse-based hovering control methods, significant differences in actuator dynamic characteristics, and the need for repeated simulations and experimental verification of the controller's closed-loop performance, which lead to low decoupling efficiency and poor robust control performance of the aircraft's multiple actuators.

[0134] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0135] The memory 701, the processor 702, and the computer program stored on the memory 701 and executable on the processor 702.

[0136] When the processor 702 executes the program, it implements the hovering control method for tiltrotor short takeoff and vertical landing aircraft provided in the above embodiments.

[0137] Furthermore, electronic devices also include:

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

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

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

[0141] If the memory 701, processor 702, and communication interface 703 are implemented independently, then the communication interface 703, memory 701, and processor 702 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 7 The bus is represented by a single thick line, 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, processor 702, and communication interface 703 are integrated on a single chip, then the memory 701, processor 702, and 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 this application.

[0144] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described hovering control method for tiltrotor short takeoff and vertical landing aircraft.

[0145] This application also provides a computer program product storing a computer program that, when executed by a processor, implements the above-described hovering control method for tiltrotor short takeoff and vertical landing aircraft.

[0146] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

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

[0148] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0149] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing 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 (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs 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: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0150] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0151] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

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

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

Claims

1. A hovering control method for a tiltrotor short takeoff and vertical landing (STOVL) aircraft, characterized in that, Includes the following steps: Construct a nonlinear dynamic model of hovering for a short takeoff and landing aircraft that includes a tiltrotor propulsion system by utilizing at least one of the nonlinear relationships of aircraft speed, Euler angle, angular velocity, thrust, and torque. The nonlinear dynamics model of the short takeoff and landing aircraft hovering is converted into a high-order all-drive system model in order to design a state tracking controller; Based on the neural network design adaptive law, the nonlinear terms in the hovering nonlinear dynamics model of the short takeoff and landing aircraft are estimated. The nonlinear term is fed back to the state tracking controller for hovering modal stabilization control; The state tracking controller is: in, Indicates control input, For state tracking error, Represents the block diagonal characteristic matrix. This represents the corresponding model input matrix. This corresponds to the nonlinear term in the model; The adaptive law for neural network design is: in, For the parameters to be designed, For the neural network weight vector, The matrix of fundamental vector functions for neural networks. For nonlinear terms The estimated value, This represents the state tracking error.

2. The method according to claim 1, characterized in that, The state of the hovering nonlinear dynamics model of the short takeoff and landing aircraft includes at least one of forward velocity, lateral velocity, vertical velocity, roll angle, pitch angle, yaw angle, roll rate, pitch rate, and yaw 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. A hovering control device for a tiltrotor short takeoff and vertical landing (STOVL) aircraft, characterized in that, include: The building module is used to construct a hovering nonlinear dynamics model of a short takeoff and landing aircraft containing a tiltrotor propulsion system using at least one of the nonlinear relationships of aircraft speed, Euler angle, angular velocity, thrust and torque. The design module is used to convert the hovering nonlinear dynamics model of the short takeoff and landing aircraft into a high-order all-drive system model in order to design a state tracking controller. The estimation module is used to estimate the nonlinear terms in the hovering nonlinear dynamics model of the short takeoff and landing aircraft based on the design of an adaptive law using a neural network. A control module is used to feed back the nonlinear term to the state tracking controller for hovering mode stabilization control; The state tracking controller is: in, Indicates control input, For state tracking error, Represents the block diagonal characteristic matrix. This represents the corresponding model input matrix. This corresponds to the nonlinear term in the model; The adaptive law for neural network design is: in, For the parameters to be designed, For the neural network weight vector, The matrix of fundamental vector functions for neural networks. For nonlinear terms The estimated value, This represents the state tracking error.

4. The apparatus according to claim 3, characterized in that, The state of the hovering nonlinear dynamics model of the short takeoff and landing aircraft includes at least one of forward velocity, lateral velocity, vertical velocity, roll angle, pitch angle, yaw angle, roll rate, pitch rate, and yaw 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.

5. An electronic device, characterized in that, include: The system includes 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 tiltrotor short takeoff and vertical landing aircraft as described in any one of claims 1-2.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the hovering control method for tiltrotor short takeoff and vertical landing aircraft as described in any one of claims 1-2.

7. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the hovering control method for tiltrotor short takeoff and vertical landing aircraft as described in any one of claims 1-2.

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