A performance constraint control method, device and equipment of a variant unmanned aerial vehicle and a medium

Abstract

The application provides a performance constraint control method, device and equipment of a variable unmanned aerial vehicle and a medium, and belongs to the field of unmanned aerial vehicle control. The method comprises the following steps: obtaining a current state and a preset expected state of a target umbrella telescopic variable unmanned aerial vehicle (PRVQ); determining an error vector according to the current state and the preset expected state, and setting a sliding surface based on the error vector; constructing an obstacle Lyapunov function according to the error vector and the sliding surface, and determining a derivative expression of the obstacle Lyapunov function; determining a performance boundary of the PRVQ according to the dynamic characteristics of the PRVQ and the convergence speed of the preset expected state; determining a time-varying performance function of the PRVQ according to the performance boundary and the convergence trajectory of the preset expected state; determining a control law expression of the PRVQ through Lyapunov stability theory according to the derivative expression of the obstacle Lyapunov function, the performance boundary and the time-varying performance function; and performing stability control on the PRVQ according to the control law expression. In this way, the key link of the PRVQ stability control strategy is constructed and implemented, and the stability of the PRVQ is ensured.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of unmanned aerial vehicle control, and particularly relates to a performance constraint control method and device for a morphing unmanned aerial vehicle, a computer device and a storage medium. BACKGROUND

[0002] Multi-rotor unmanned aerial vehicles have been widely used in power line inspection, oil pipeline inspection, leakage detection, and can also be used for reconnaissance, monitoring and battlefield intelligence collection in complex terrain and urban environments due to their simple structure, strong flexibility and low cost. However, when facing complex and narrow environments, the size of the fixed structure of the multi-rotor unmanned aerial vehicle cannot be adjusted, which limits its flight range and passability. The morphing unmanned aerial vehicle can change its structure, and when performing reconnaissance and detection tasks in a specific environment, it can effectively avoid collision according to the specific task characteristics, thereby increasing flight safety. The structural transformation of the morphing unmanned aerial vehicle is suitable for application in space-limited tasks, can flexibly shuttle and quickly respond to task requirements; by reducing the arm span, it helps the unmanned aerial vehicle to quickly turn and reduce the turning radius, thereby improving the maneuverability and flexibility of the unmanned aerial vehicle, so that it is no longer constrained by the task environment, thereby improving the versatility of the unmanned aerial vehicle. Therefore, it is of practical significance to study the morphing unmanned aerial vehicle and its control method.

[0003] The control core of the morphing unmanned aerial vehicle is to directly change the shape and structure of the aircraft during flight while achieving accurate control of flight characteristics. When the morphing unmanned aerial vehicle is flying, some variables of the system need to be constrained due to safety flight and physical limitations during the structural change process.

[0004] Various morphing unmanned aerial vehicles and their control methods have been proposed in the prior art, such as a two-dimensional multi-link variable structure unmanned aerial vehicle, a linear quadratic form (LQI) controller is used in the control part to compensate for uncertain items in attitude motion, and a PID controller is used for position control; or changing the structure in various situations to improve the stability and efficiency of transporting the payload; there is also a morphing unmanned aerial vehicle with rotatable arms, and the aerodynamic effects caused by the overlap of the propellers and the main part of the morphing quadrotor aircraft during flight are studied. There is also an active morphing quadrotor aircraft, and a flight controller is designed based on active disturbance rejection control (ADRC) technology to suppress the influence of internal and external disturbances on the system.

[0005] The control methods described above can all be used to control the structural changes of morphing UAVs. However, due to the structural changes that occur during flight, morphing UAVs may pose potential risks to stability and safety. LQI controllers themselves perform poorly in complex situations with high inertia and hysteresis in UAV attitude control systems, posing safety hazards. PID controllers may have large initial errors, failing to meet the dynamic performance stability requirements of morphing UAVs in position control. Structural changes may introduce additional mechanical complexity and weight, potentially leading to safety risks during flight. The overlap between the propeller and main body of a morphing quadcopter may cause complex aerodynamic effects, potentially affecting the UAV's flight performance and stability. Active morphing based on ADRC technology may introduce additional mechanical complexity and weight, impacting the UAV's safety and stability.

[0006] Therefore, existing technologies for controlling variant drones present safety and stability issues. Summary of the Invention

[0007] To address the safety and stability issues during the mutating process of a mutating drone, this invention provides a performance constraint control method, apparatus, computer device, and storage medium for mutating drones.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] First, a performance constraint control method for a variant unmanned aerial vehicle (UAV) is provided, the method comprising:

[0010] Obtain the current state and preset desired state of the target parasol-type telescopic variant UAV PRVQ;

[0011] The error vector is determined based on the current state and the preset desired state, and the sliding surface is set based on the error vector;

[0012] Based on the performance constraints of the sliding surface, the sliding surface is mapped to the dynamic geometric frame;

[0013] Based on the error vector and the dynamic geometric framework, construct the barrier Lyapunov function and determine the derivative expression of the barrier Lyapunov function;

[0014] The performance boundary of PRVQ is determined based on the dynamic characteristics of PRVQ and the convergence rate of the preset desired state; the convergence rate of the preset desired state is the pre-set PRVQ response rate.

[0015] The time-varying performance function of PRVQ is determined based on the convergence trajectory of the performance boundary and the preset desired state.

[0016] The control law expression for PRVQ is determined based on the derivative expression of the barrier Lyapunov function, the performance boundary, and the time-varying performance function.

[0017] The PRVQ is subjected to stability control based on the control law expression.

[0018] Optionally, determining the error vector based on the current state and the preset desired state, and setting the sliding surface based on the error vector includes:

[0019] Compare the current state of PRVQ with the preset expected state, calculate the error value of each state variable, and form an error vector;

[0020] Based on the error vector, the state change trajectory that guides the current state of PRVQ to the desired state is determined, and the state change trajectory is determined to be a sliding surface.

[0021] Optionally, constructing the barrier Lyapunov function based on the error vector and the dynamic geometric framework includes:

[0022] Based on the boundary of the dynamic geometric framework, adjustment operators and barrier operators are introduced to perform boundary constraints; the adjustment operators and barrier operators respectively constrain the performance of the sliding surface from different angles;

[0023] Construct the general formula for the barrier Lyapunov function based on the aforementioned adjustment operator and barrier operator;

[0024] Based on the current state, select the type of the obstacle Lyapunov function formula and construct the obstacle Lyapunov function by combining it with the constraint boundary of the sliding surface.

[0025] Optionally, the performance boundary of PRVQ is determined based on the dynamic characteristics of PRVQ and the convergence rate of the preset desired state, including:

[0026] Perform kinetic analysis on the PRVQ to determine its kinetic characteristics;

[0027] Set the convergence rate of the desired PRVQ state according to the preset control requirements;

[0028] By combining the dynamic characteristics and convergence rate of PRVQ, the constraints that PRVQ must satisfy during the adjustment process are determined, and the performance boundary of PRVQ is obtained based on the constraints.

[0029] Optionally, determining the time-varying performance function of PRVQ based on the convergence trajectory of the performance boundary and the desired state includes:

[0030] Based on the desired convergence rate and performance boundary, plan the convergence trajectory of the PRVQ state trajectory.

[0031] By combining the convergence trajectory and performance boundary, the performance changes of PRVQ during the adjustment process are evaluated, and the time-varying performance function is obtained.

[0032] Optionally, stability control of PRVQ based on the control law expression includes:

[0033] Get the real-time status of PRVQ;

[0034] The current control input for PRVQ is determined based on the real-time state and the control law expression.

[0035] Real-time stability control of PRVQ is performed based on the control input.

[0036] Secondly, a performance constraint control device for a variant unmanned aerial vehicle is provided, the device comprising:

[0037] The acquisition module is used to acquire the current state and preset desired state of the target parasol-type telescopic variant UAV PRVQ;

[0038] A determination module is used to: determine an error vector based on the current state and a preset desired state; set a sliding surface based on the error vector; map the sliding surface to a dynamic geometric frame based on the performance constraints of the sliding surface; construct a barrier Lyapunov function based on the error vector and the dynamic geometric frame; determine the derivative expression of the barrier Lyapunov function; determine the performance boundary of the PRVQ based on the dynamic characteristics of the PRVQ and the convergence rate of the preset desired state; the convergence rate of the preset desired state is a pre-set PRVQ response rate; determine the time-varying performance function of the PRVQ based on the performance boundary and the convergence trajectory of the preset desired state; and determine the control law expression of the PRVQ using Lyapunov stability theory based on the derivative expression of the barrier Lyapunov function, the performance boundary, and the time-varying performance function.

[0039] The control module is used to perform stability control on PRVQ according to the control law expression.

[0040] Additionally, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the aforementioned performance constraint control method for a variant of an unmanned aerial vehicle.

[0041] Finally, a computer device is provided, 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 performance constraint control method of the aforementioned variant UAV.

[0042] The performance constraint control method for a variant unmanned aerial vehicle provided by this invention has the following beneficial effects:

[0043] First, by acquiring the current state and the preset desired state of the umbrella-type telescopic variant UAV, necessary input information is provided for subsequent control strategies, ensuring targeted adjustments to the UAV based on actual conditions. Second, by calculating the error vector between the current and desired states, the direction and magnitude of the PRVQ adjustment can be clarified. Setting a sliding surface is to achieve fast and stable error convergence, improving the system's robustness. Mapping the sliding surface to a dynamic geometric framework allows for a more intuitive understanding and analysis of the UAV's control process, helping to optimize control strategies while considering performance constraints, ensuring that the UAV does not exceed its physical or performance limitations during control, thereby improving flight safety and stability. Constructing a barrier Lyapunov function is to evaluate the system's stability, and its derivative is used to express... By observing the changing trends of the system state using formulas, it is beneficial to accurately assess the current state of the PRVQ. Then, by considering the dynamic characteristics of the PRVQ and the convergence rate of the desired state, reasonable performance boundaries can be set to ensure that the UAV maintains its physical and performance characteristics during control, avoiding overload or instability. Furthermore, by setting a reasonable time-varying performance function, stable performance output is ensured during control, improving control accuracy and stability. Finally, by combining Lyapunov stability theory, this control law expression ensures the UAV maintains stability during control while satisfying various constraints and performance requirements, enhancing its ability to adapt to complex environments and improving safety and control performance. In this way, the constraints and control of the variant UAV system variables are completed through these steps, improving the stability and safety of variant control during PRVQ flight. Attached Figure Description

[0044] To more clearly illustrate the embodiments and design schemes of the present invention, the accompanying drawings required for this embodiment will be briefly described below. The drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a flowchart illustrating a performance constraint control method for a variant unmanned aerial vehicle provided by the present invention according to an exemplary embodiment.

[0046] Figure 2 This is a schematic diagram of a PRVQ model provided by the present invention according to an exemplary embodiment, wherein (a) is an overall schematic diagram of PRVQ and (b) is a partial schematic diagram of PRVQ.

[0047] Figure 3This is a schematic diagram of a dynamic geometric framework provided by the present invention according to an exemplary embodiment; wherein, (a) is a schematic diagram of a dynamic BLF geometric framework, and (b) is a schematic diagram of the change of performance boundaries in the dynamic geometric framework.

[0048] Figure 4 This is a block diagram of a performance constraint control device for a variant unmanned aerial vehicle according to an exemplary embodiment of the present invention. Detailed Implementation

[0049] To enable those skilled in the art to better understand and implement the technical solutions of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be construed as limiting the scope of protection of the present invention.

[0050] Based on a dynamic geometric framework, this invention transforms system state constraints into geometric constraints, designs a novel general formula for a tangential barrier Lyapunov function (BLF), and combines it with predefined time control to control the parachute retractable variable quadrotor (PRVQ) model designed in this invention.

[0051] The technical solutions provided by the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0052] First, this invention provides a performance constraint control method for a variant unmanned aerial vehicle (UAV), specifically as follows: Figure 1 As shown, it includes the following steps:

[0053] S101. Obtain the current state and preset desired state of the target parasol-type telescopic variant UAV PRVQ.

[0054] In this step, the sensor system integrated on the PRVQ (such as a GPS module, attitude sensor, altimeter, etc.) can be used to collect real-time status information of the UAV, including its position, speed, and attitude angles (pitch, roll, yaw). This sensor data is read and analyzed by a data processing unit (such as a microcontroller or computer).

[0055] For preset desired states, the desired states of PRVQ can be set in the control system according to task planning or user input, including target position, target velocity, target attitude angle, etc. These desired state values ​​are usually represented in the form of coordinates, velocity vectors, and angle values, and stored in the memory of the control system.

[0056] S102. Determine the error vector based on the current state and the preset desired state, and set the sliding surface based on the error vector.

[0057] Sliding mode control is a nonlinear control strategy. Its core idea is to design an appropriate sliding surface so that the system state reaches and remains on the sliding surface within a finite time, thereby achieving control over the system performance. In sliding mode control, the design of the sliding surface is crucial, as it directly determines the stability and performance of the system.

[0058] In this step, the current state of the PRVQ is compared with the preset desired state, and the error value of each state variable is calculated to form an error vector. The error vector represents the deviation between the current state and the desired state of the PRVQ. Based on the error vector, the state change trajectory that guides the current state of the PRVQ to the desired state is determined, and this state change trajectory is identified as the sliding surface. The sliding surface is typically a hyperplane associated with the error vector, used to guide the state trajectory of the PRVQ to slide along this plane and eventually converge to the desired state. The design of the sliding surface takes into account the dynamic characteristics and control requirements of the system.

[0059] S103. Based on the performance constraints of the sliding surface, map the sliding surface to the dynamic geometric frame.

[0060] Dynamic geometric frameworks are a method that transforms constraint problems into geometric relationships, allowing us to understand and address constraint problems by observing and analyzing changes in geometry.

[0061] In this step, the boundaries of the dynamic geometric framework are the geometric constraints that the sliding surface should satisfy. These boundaries can be straight lines, curves, or more complex geometries, depending on the system's constraints and performance requirements. This invention designs a dynamic BLF geometric framework based on PRVQ, which will be described in detail later. Mapping the sliding surface to the preset boundaries in the dynamic geometric framework involves matching or transforming the equations of the sliding surface with the equations of the preset boundaries to ensure that the sliding surface satisfies all constraints. During the mapping process, the parameters of the sliding surface and the preset boundaries need to be adjusted to ensure their matching degree and system performance, including adjusting the slope and intercept of the sliding surface, and the shape and position of the preset boundaries. Finally, the correctness and effectiveness of the mapping results can be verified through simulation or experiments, including checking whether the system state can reach and remain on the sliding surface within a finite time, and whether the system satisfies all performance constraints.

[0062] In the steps described above, the dynamic BLF geometric framework is a method for transforming system state constraints into geometric constraints. This framework transforms complex BLF design problems into intuitive geometric relationship problems, greatly simplifying the BLF design process. It provides a flexible and efficient method for designing logarithmic, tangent, and quadratic fractional BLF functions, adaptable to the needs and constraints of different systems.

[0063] S104. Construct the barrier Lyapunov function based on the error vector and the dynamic geometric framework, and determine the derivative expression of the barrier Lyapunov function.

[0064] In this step, a barrier Lyapunov function is constructed using the error vector and dynamic geometric framework. This function is used to evaluate the distance between the PRVQ state trajectory and the sliding surface, as well as the stability of the system. The selection and design of the barrier Lyapunov function should satisfy the conditions of the Lyapunov stability theorem. The derivative expression of the barrier Lyapunov function is obtained by taking its derivative. This derivative expression is used for subsequent control law design and stability analysis.

[0065] Specifically, based on the boundary of the dynamic geometric framework, adjustment operators and obstacle operators are introduced to perform boundary constraints; the adjustment operators and obstacle operators respectively constrain the performance of the sliding surface from different angles; the general formula of the obstacle Lyapunov function is constructed according to the adjustment operators and obstacle operators; the type of the obstacle Lyapunov function general formula is selected according to the current state, and the obstacle Lyapunov function is constructed in combination with the constraint boundary of the sliding surface.

[0066] In one embodiment, firstly, the system state of the parachute-type telescopic variant unmanned aerial vehicle (PRVQ) is defined, including key variables such as position and attitude. Next, based on mission requirements and flight safety, constraint boundaries of the system state, i.e., upper and lower performance boundaries, are set. This invention proposes a dynamic geometric framework that transforms the constraint problem of the system state into geometric relationships, thereby simplifying the design process of the BLF (Body Function Function). In this framework, constraints 's' and their upper and lower performance boundaries are set, and the constraints of the system state are intuitively represented through geometric relationships. Then, based on the dynamic geometric framework, general formulas conforming to logarithmic, tangent, and quadratic fractional BLFs are derived. These formulas have similar structural features, including adjustment operators and barrier operators, used to adjust the constrained quantities of the system and prevent the system state from exceeding the constraint boundaries. An appropriate BLF type (such as tangent) is selected according to the system state and constraints. Using the derived BLF formulas, combined with system parameters and constraint boundaries, a specific BLF function is constructed. Then, the derivative expression of the constructed BLF function is obtained. This derivative expression will be used in subsequent control law design to ensure that the system state always remains within the constraint boundaries. Furthermore, this invention incorporates a predefined time control method, which, by designing a performance function with a decay term, achieves the control objective of the system state tending towards the desired value within a predefined time. This further improves the robustness and performance of the control system.

[0067] S105. Determine the performance boundary of PRVQ based on the dynamic characteristics of PRVQ and the convergence rate of the preset desired state.

[0068] The convergence speed of the preset desired state is the pre-set PRVQ response speed.

[0069] Specifically, a dynamic analysis can first be performed on the PRVQ to determine its dynamic characteristics. For example, factors such as the PRVQ's mechanical structure, mass distribution, and power system can be considered to analyze its dynamic characteristics. These characteristics include the UAV's mass, moment of inertia, and aerodynamic parameters.

[0070] Next, based on the preset control requirements, the convergence rate of the desired PRVQ state is set. The convergence rate reflects the speed and efficiency with which PRVQ adjusts from the current state to the desired state.

[0071] Finally, combining the dynamic characteristics and convergence rate of PRVQ, the constraints that PRVQ must satisfy during the adjustment process are determined, and the performance boundary of PRVQ is obtained based on these constraints. These constraints include limitations on velocity, acceleration, and attitude angle change rate.

[0072] S106. Based on the convergence trajectory of the performance boundary and the preset expected state, determine the time-varying performance function of PRVQ.

[0073] In this step, the convergence trajectory of the PRVQ state trajectory can be planned based on the convergence rate and performance boundaries of the desired state. The convergence trajectory describes the adjustment process of the PRVQ from the current state to the desired state. The performance changes of the PRVQ during the adjustment process are evaluated by combining the convergence trajectory and performance boundaries, resulting in a time-varying performance function. This function is used to evaluate the performance changes of the PRVQ during the adjustment process, including changes in velocity, acceleration, attitude angles, and other states. The selection and design of the time-varying performance function should meet the mission requirements and control requirements.

[0074] S107. Based on the derivative expression of the barrier Lyapunov function, the performance boundary, and the time-varying performance function, determine the control law expression of PRVQ using Lyapunov stability theory.

[0075] In this step, the control law for the PRVQ is designed using the derivative expression of the barrier Lyapunov function, its performance boundary, and its time-varying performance function, combined with Lyapunov stability theory. The control law describes the relationship between the PRVQ control input and the current state, desired state, performance boundary, and time-varying performance function. Through mathematical derivation and calculation, the control law expression for the PRVQ is obtained. This expression guides the PRVQ control system in generating appropriate control inputs to achieve stable control.

[0076] S108. Perform stability control on PRVQ according to the control law expression.

[0077] In this step, it is necessary to obtain the real-time state of the PRVQ; determine the current control input of the PRVQ based on the real-time state and the control law expression; and perform real-time stability control of the PRVQ based on the control input.

[0078] Specifically, the control input calculated from the control law expression is applied to the PRVQ control system. The control input can be commands in the form of motor speed, servo deflection angle, etc., used to adjust the UAV's position and attitude. By continuously acquiring the current state information of the PRVQ and calculating the control input according to the control law expression, real-time stability control of the PRVQ is performed. During the control process, it is necessary to monitor changes in the UAV's state and adjust the control input as needed to cope with external disturbances and uncertainties. Simultaneously, it is also necessary to evaluate and provide feedback on the control effect to continuously optimize the control law and improve control performance.

[0079] Based on the above method, the present invention also provides a formula derivation process for a performance constraint control method for a variant unmanned aerial vehicle, as detailed below:

[0080] For dynamic systems have Let represent the system state, d represent the disturbance, and f(t,x,d) represent the nonlinear function. For this system, if there exists a radially unbounded Lyapunov function V(x), then:

[0081]

[0082] For any state x of the above system, where T c >0 represents the predefined time of the system, η∈(0,1) represents the system parameters, 0<∈<∞, then the trajectory of the system is stable in the predefined time, and the residual set of the system can be represented as:

[0083]

[0084] Where 0 < μ < 1, the adjustment time T pc for:

[0085] Assumption 1: The external disturbance d experienced by the system i It is continuously differentiable and bounded, and there exists a Lipschitz constant ||d||. i ||≤Δ.

[0086] Assumption 2: The quadcopter drone has a rigid structure and its geometric center coincides with its center of gravity.

[0087] 1.2 PRVQ Mathematical Model

[0088] In confined and complex environments, the PRVQ adapts to different environments and flight requirements by varying the length h of the telescopic boom below, thereby changing the boom distance l. A PRVQ coordinate system framework is constructed, where the world coordinate system W:(O) e ,X e ,Y e Z e ) and body coordinate system B:(O b ,X b ,Y b Z b ), PRVQ model and the identifiers of each physical quantity, such as Figure 2 As shown, when the arm span of the PRVQ changes, its dynamic characteristics will change accordingly, especially in terms of attitude control. The PRVQ designed in this invention is equipped with a mechanical telescopic rod underneath for precise control of the quadcopter arm length. The physical quantity of the telescopic rod is m. l Indicates equivalent mass; h represents the length that can be stretched or scalded, and h is subject to physical limitations, for example: h min ≤h≤h max h min and h max This indicates the shortest and longest telescopic lengths of the rod. The change in telescopic rod length h corresponds to the change in angle γ of the sector-shaped rotating link (the specific installation location is shown in the diagram). Figure 2 The physical relationships (highlighted in the text) are as follows:

[0089]

[0090]

[0091] The transmission relationship of the PRVQ is as follows: the telescopic rod drives the sector-shaped rotating link to rotate, causing a change in the length of the UAV arm. The telescopic rod and the sector-shaped rotating link are connected by a rigid link. When the telescopic rod extends by h, it stretches the sector-shaped rotating link on the PRVQ arm downwards by γ degrees, shortening the length of the PRVQ arm. Therefore, the mathematical model of the PRVQ is:

[0092]

[0093] In the formula, x, y, z represent the position coordinates of the PRVQ in 3D space; φ, θ, ψ are defined as roll angle, pitch angle, and yaw angle, respectively; u φ ,u θ ,u ψ Indicates the attitude system control input; I xx (l),I yy (l),I zz(l) represents the moment of inertia of the UAV, which is directly affected by changes in the arm length; m,g represent the mass and gravitational acceleration of the PRVQ; l(t) is the time-varying arm length value, representing the distance between the PRVQ's center of mass and the center of the motor rotor; u f Representing the total lift, PRVQ is divided into 6 control channels p = {x, y, z, φ, θ, ψ}; k p Indicates the air drag coefficient; d p This represents the external, unknown disturbance experienced by the system. It combines the three types of rotational inertia of the machine body:

[0094]

[0095] In the formula m eq For PRVQ equivalent quality; m mo For the mass of the drive motor; m arm For the mass of the machine arm; l eq ,h eq The length, width, and height of the equivalent mass block; l arm This is the equivalent arm length.

[0096] For convenience, let (v1, v 2,3 ,v 4,5,6 If )=(x,y,z,φ,θ,ψ), then the dynamic system (2) can be described as a nonlinear affine system:

[0097]

[0098] In the formula: To control the input,

[0099]

[0100]

[0101]

[0102] Based on the desired angle v required by the control quantity 4d ,v 5d and expected lift u f for:

[0103]

[0104] This invention, starting from geometry, proposes, as follows: Figure 3 (a) shows the dynamic BLF geometric framework, and sets constraints s and their upper and lower performance boundaries. and p (t) < 0, satisfying This simplifies the design of BLF functions. By transforming the constraint problem in BLF construction into geometric relations, the complex process of BLF design is greatly simplified. This framework provides a flexible and efficient method for designing logarithmic, tangent, and quadratic fractional BLF functions, and can adapt to the needs and constraints of different systems. Figure 3 (b) represents the change in performance boundaries within the dynamic geometric framework, with the upper bound being... and the lower realm p The distance between (t) gradually shrinks over time. In the steady state of the system, by tightening the performance boundary, the system response can be forced to be closer to the expectation, thereby improving control accuracy.

[0105] Common BLF forms include: Quadratic Fractional BLF (qfBLF), Logarithmic BLF (lnBLF), and Tangent BLF (tanBLF). Starting with these three BLF configurations, it can be concluded that BLFs all possess similar structural characteristics. For ease of explanation, an adjustment operator is proposed here: Used to adjust the constrained quantities of the system so that after the system moves away from the constraint boundary and reaches an equilibrium state while satisfying the constraint conditions, V B The value tends to zero. Barrier operator: Used when the system state or the constrained quantity approaches the constraint boundary, so that V B The value tends to infinity, thus preventing the system state from exceeding the constraint boundary. Fractional modulation function: It is a fractional function consisting of regulation and barrier operators, used to construct different types of BLF. Then, the BLF and its derivative can be simply expressed as:

[0106]

[0107] Combination Figure 3 The geometric relationships in the diagram and their corresponding... and The categories are shown in Table 1:

[0108] Table 1. Classification of Regulation Operators and Obstacle Operators

[0109]

[0110] This invention is based on Figure 3 As a reference design, a novel tanBLF general formula is proposed, which can be used for asymmetric constraints. Based on the classification table 1 and the characteristics of the three types of BLF structures, the following general formula is derived:

[0111]

[0112] According to equation (9), select V tanBLF Define the barrier Lyapunov function:

[0113]

[0114] Differentiating equation (10) gives:

[0115]

[0116] According to equation (11), since the present invention defines the adjustment operator... Its derivative is The derivative contains s, which can be factored out as a common factor, facilitating control law design. Now analyze equation (9), V tanBLF There are two terms. First, differentiate the first term:

[0117]

[0118]

[0119] Similarly, taking the derivative with respect to the second term:

[0120]

[0121]

[0122] make In summary V tanBLF The derivative is:

[0123]

[0124] Based on the characteristics of the BLF general formula (9), the general formula for the derivative of the barrier Lyapunov function is now given:

[0125]

[0126] For system (6), a control law is designed for PRVQ, and the error is defined as:

[0127]

[0128] In the formula The expected value of the PRVQ state and its derivative are represented by the sliding surface:

[0129]

[0130] In the formula: 0 < η si <1,T si >0 indicates a predefined time, α si ,β si>0. Define the barrier Lyapunov function as:

[0131]

[0132] Taking the derivative of equation (17):

[0133]

[0134]

[0135]

[0136]

[0137]

[0138]

[0139]

[0140] This invention focuses on the design of performance boundaries for tanBLF. When t>T ci There should be The ordinary performance function p(t) = (p0 - p ∞ )e -λt +p ∞ The previous approach did not set a transition time or implemented it through a piecewise function, but this invention designs a performance function with a decay term, which has continuous characteristics.

[0141]

[0142] In the formula, k pi ,T ci ,λ i For design parameters, p0 = k pi T ci +p i∞ T ci This represents the performance function from its initial value p0 to its final value p. ∞ The transition time, w p Used to control the first decay term, when t = T c When, the first attenuation coefficient is To achieve the design objective, w is set here. p =6. The performance boundary derivative is:

[0143]

[0144]

[0145] Then design the control law:

[0146]

[0147] Then the sliding mode approach law:

[0148]

[0149] Substituting equation (33) into equation (21), we get:

[0150]

[0151] In the formula Take c di ≥D i By Lemma 1, we can obtain s i At a predefined time T i The interior tends to zero.

[0152] When s i When = 0, there is Choose the Lyapunov function:

[0153]

[0154] Differentiating, we get:

[0155]

[0156] By Lemma 1, we can obtain that e 1i It will be at the predefined time T si It converges to 0.

[0157] In summary, the system at T si +T i The internal balance is achieved.

[0158] To verify the rationality of the designed PRVQ model and the effectiveness of the time-varying asymmetric tanBLF control method (TA-tBLF) proposed in this invention, two control methods were selected for comparison: predefined time sliding mode control (PTSC) and BLF backstepping control (B-BLFC), and simulation experiments were conducted. The predefined time control parameters are shown in Table 2, where j∈{1,2,3}, k∈{4,5,6}.

[0159] Table 2 Predefined time control parameters

[0160] Symbol Value Symbol Value [CAT sj ]]> [0.5,0.5,1.5] β sj ]]> [0.5,0.5,0.5] [CAT sk ]]> [0.5,0.5,0.5] β sk ]]> [5,3,3] [CAT j ]]> [1,1,2] β j ]]> [3,3,3] [CAT k ]]> [1,1,1] β k ]]> [3,3,3] sj ]]> ​ [0.01,0.01,1] sj ]]> ​ [0.5,0.5,0.4] sk ]]> ​ [1,1,1] sk ]]> ​ [0.5,0.3,0.3] α j ]]> ​ [1,1,3] j ]]> ​ [0.75,0.75,0.75] k ]]> ​ [1,1,1] k ]]> ​ [0.5,0.5,0.4]

[0161] Within 2 seconds, h varies from [0, 0.055] m, corresponding to a γ variation from [0, 1.026] rad, and the arm span l arm For a corresponding decrease of [0.17, 0.11]m, the moment of inertia also decreases accordingly. zzVariation range [0.029, 0.018], I xx ,I yy The range of variation is [0.014, 0.009].

[0162] In the initial stage, all three control methods exhibited no overshoot. TA-TBLF and PTSC methods resulted in rapid attitude error convergence, with a convergence time of approximately 1 second. In contrast, B-BLFC control converged more slowly, with a convergence time of approximately 3 seconds. During arm span switching after 10 seconds, TA-TBLF demonstrated superior control performance. By setting performance boundaries, it minimized the attitude error jump range compared to the other two control methods, while PTSC exhibited a larger attitude error jump range. If PRVQ requires arm span switching during task execution, TA-TBLF control proves more robust, effectively addressing the impact of arm span changes and maintaining stable attitude control.

[0163] Because of the coupling between PRVQ attitude control and position control, changes in arm distance affect not only attitude dynamics but also position characteristics. When the arm distance decreases, the rotational inertia of the PRVQ decreases accordingly, reducing the UAV's disturbance rejection capability and stability. In the 10-20s phase, the position error fluctuates under all three control methods, with PTSC showing the most significant fluctuation. The x and y direction error fluctuation range of PTSC is affected by the turbulent wind field, deviating from zero, while TA-tBLF and B-BLFC controls, due to setting performance boundary constraints on the system state, cause the error to be more concentrated at zero.

[0164] The core objective of control system design is to achieve system error convergence. A sliding surface can be viewed as a synthesis of system errors, describing the dynamic deviation between the system state and the desired value. This paper uses a sliding surface constraint (BLF) to constrain the sliding surface, thereby constraining the PRVQ system state while optimizing the system's dynamic response characteristics.

[0165] At the initial moment, due to the large deviation between the initial and desired system values, all three control methods exhibit significant output. While TA-tBLF and PTSC have lower outputs than B-BLFC, they also possess faster convergence speeds. As the deviation decreases, the control laws of all three methods become relatively stable. However, after TA-tBLF stabilizes, the control law exhibits a jump around 0.5s because the time-varying performance function has a transition time T. c Back boundary value from Monotonically decreasing and stable at and p ∞ Therefore, the control law changes and controls the dynamic system to transition to the next stage.

[0166] B-BLFC directly constrains the system error to design the control law. When the arm distance changes, the control law also changes abruptly. In contrast, TA-tBLF constrains the sliding surface, resulting in a smaller abrupt change in the control law and a smoother change.

[0167] Using the above method, firstly, by obtaining the current state and the preset desired state of the PRVQ, necessary input information is provided for the subsequent control strategy. Secondly, by calculating the error vector between the current state and the desired state, the direction and magnitude of the PRVQ adjustment can be clarified. Setting a sliding surface is to achieve fast and stable error convergence, improving the robustness of the system. Constructing a barrier Lyapunov function is to evaluate the stability of the system, and its derivative expression is used to observe the changing trend of the system state, which is beneficial for accurately evaluating the current state of the PRVQ. Then, by considering the dynamic characteristics of the PRVQ and the convergence speed of the desired state, a reasonable performance boundary can be set to ensure that the control strategy is neither too aggressive, leading to system instability, nor too conservative, affecting the response speed. Furthermore, by setting a reasonable time-varying performance function, the control strategy can be further optimized to improve the overall performance of the system. Finally, by combining Lyapunov stability theory, a control law that can guarantee system stability and meet performance requirements can be designed, which helps to achieve precise control of the PRVQ and improve its ability to adapt to complex environments. In this way, by constructing and implementing the key links of the PRVQ stability control strategy through each step, the PRVQ can be ensured to execute tasks accurately and stably.

[0168] Secondly, the present invention also provides a performance constraint control device for a variant unmanned aerial vehicle, such as... Figure 4 As shown, it includes:

[0169] The acquisition module 401 is used to acquire the current state and preset desired state of the target parasol telescopic variant UAV PRVQ.

[0170] The determination module 402 is used to determine the error vector based on the current state and the preset desired state, and set the sliding surface based on the error vector; based on the performance constraints of the sliding surface, map the sliding surface to the dynamic geometric frame; construct the obstacle Lyapunov function based on the error vector and the dynamic geometric frame, and determine the derivative expression of the obstacle Lyapunov function; determine the performance boundary of the PRVQ based on the dynamic characteristics of the PRVQ and the convergence rate of the preset desired state; the convergence rate of the preset desired state is the preset PRVQ response rate; determine the time-varying performance function of the PRVQ based on the performance boundary and the convergence trajectory of the preset desired state; and determine the control law expression of the PRVQ through Lyapunov stability theory based on the derivative expression of the obstacle Lyapunov function, the performance boundary, and the time-varying performance function.

[0171] Control module 403 is used to perform stability control on PRVQ according to the control law expression. The present invention also provides a computer-readable storage medium storing a computer program that can be used to execute the above-described... Figure 1 The steps of the provided performance constraint control method for variant UAVs.

[0172] Using the aforementioned apparatus, firstly, by acquiring the current state and the preset desired state of the PRVQ, necessary input information is provided for subsequent control strategies. Secondly, by calculating the error vector between the current state and the desired state, the direction and magnitude of the PRVQ adjustment can be clarified. Setting a sliding surface is to achieve fast and stable error convergence, improving the system's robustness. Constructing a barrier Lyapunov function is to evaluate the system's stability, and its derivative expression allows observation of the system state's changing trend, facilitating accurate evaluation of the PRVQ's current state. Then, by considering the PRVQ's dynamic characteristics and the convergence speed of the desired state, reasonable performance boundaries can be set to ensure that the control strategy is neither too aggressive, leading to system instability, nor too conservative, affecting response speed. Furthermore, by setting a reasonable time-varying performance function, the control strategy can be further optimized, improving the overall system performance. Finally, by combining Lyapunov stability theory, a control law that guarantees both system stability and performance requirements can be designed, contributing to precise PRVQ control and enhancing its ability to adapt to complex environments. By constructing and implementing the key links of the PRVQ stability control strategy through these steps, the PRVQ can be ensured to execute tasks accurately and stably.

[0173] This invention also provides a computer device. At the hardware level, the computer device includes a processor, an internal bus, a network interface, memory, and non-volatile memory, and may also include other hardware required for various operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then executes it to achieve the above-mentioned functions. Figure 1 The steps of the provided performance constraint control method for variant UAVs.

[0174] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0175] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0176] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0177] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes ​ The steps of the function specified in one or more boxes.

[0178] It should be noted that the above-described specific embodiments enable those skilled in the art to more fully understand the present invention, but do not limit the present invention in any way. Therefore, although the present invention has been described in detail in this specification, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the present invention; and all technical solutions and improvements that do not depart from the spirit and scope of the present invention are covered within the protection scope of the patent of the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A performance constraint control method for a variant unmanned aerial vehicle, characterized in that, The method includes: Obtain the current state and preset desired state of the target parasol-type telescopic variant UAV PRVQ; The error vector is determined based on the current state and the preset desired state, and the sliding surface is set based on the error vector; Based on the performance constraints of the sliding surface, the sliding surface is mapped to the dynamic geometric frame; Based on the error vector and the dynamic geometric framework, construct the barrier Lyapunov function and determine its derivative expression: Where s represents the system constraint quantity, and These represent the upper and lower bounds of the fractional modulation function, respectively. and For barrier operators, p These represent the upper and lower bounds of the constraint, respectively. This represents the first derivative of s with respect to time t; The performance boundary of PRVQ is determined based on the dynamic characteristics of PRVQ and the convergence rate of the preset desired state; the convergence rate of the preset desired state is the pre-set PRVQ response rate. The time-varying performance function of PRVQ is determined based on the convergence trajectory of the performance boundary and the preset desired state. The control law expression for PRVQ is determined based on the derivative expression of the barrier Lyapunov function, the performance boundary, and the time-varying performance function. The PRVQ is subjected to stability control based on the control law expression.

2. The performance constraint control method for a variant unmanned aerial vehicle according to claim 1, characterized in that, Determine the error vector based on the current state and the preset desired state, and set the sliding surface based on the error vector, including: Compare the current state of PRVQ with the preset expected state, calculate the error value of each state variable, and form an error vector; Based on the error vector, the state change trajectory that guides the current state of PRVQ to the desired state is determined, and the state change trajectory is determined to be a sliding surface.

3. The performance constraint control method for a variant unmanned aerial vehicle according to claim 1, characterized in that, Constructing the barrier Lyapunov function based on the error vector and dynamic geometric framework includes: Based on the boundary of the dynamic geometric framework, adjustment operators and barrier operators are introduced to perform boundary constraints; the adjustment operators and barrier operators respectively constrain the performance of the sliding surface from different angles; Construct the general formula for the barrier Lyapunov function based on the aforementioned adjustment operator and barrier operator; Based on the current state, select the type of the obstacle Lyapunov function formula and construct the obstacle Lyapunov function by combining it with the constraint boundary of the sliding surface.

4. The performance constraint control method for a variant unmanned aerial vehicle according to claim 1, characterized in that, Based on the dynamic characteristics of PRVQ and the convergence rate of the preset desired state, the performance boundary of PRVQ is determined as follows: Perform kinetic analysis on the PRVQ to determine its kinetic characteristics; Set the convergence rate of the desired PRVQ state according to the preset control requirements; By combining the dynamic characteristics and convergence rate of PRVQ, the constraints that PRVQ must satisfy during the adjustment process are determined, and the performance boundary of PRVQ is obtained based on the constraints.

5. The performance constraint control method for a variant unmanned aerial vehicle according to claim 1, characterized in that, Based on the convergence trajectory of the performance boundary and the desired state, the time-varying performance function of PRVQ is determined as follows: Based on the desired convergence rate and performance boundary, plan the convergence trajectory of the PRVQ state trajectory. By combining the convergence trajectory and performance boundary, the performance changes of PRVQ during the adjustment process are evaluated, and the time-varying performance function is obtained.

6. The performance constraint control method for a variant unmanned aerial vehicle according to claim 1, characterized in that, Stability control of PRVQ based on the aforementioned control law expression includes: Get the real-time status of PRVQ; The current control input for PRVQ is determined based on the real-time state and the control law expression. Real-time stability control of PRVQ is performed based on the control input.

7. A performance constraint control device for a variant unmanned aerial vehicle, characterized in that, The device includes: The acquisition module is used to acquire the current state and preset desired state of the target parasol-type telescopic variant UAV PRVQ; The determination module is used to determine an error vector based on the current state and a preset desired state, and to set a sliding surface based on the error vector; based on the performance constraints of the sliding surface, to map the sliding surface to a dynamic geometric frame; and to construct a barrier Lyapunov function based on the error vector and the dynamic geometric frame, and to determine the derivative expression of the barrier Lyapunov function. Where s represents the system constraint quantity, and These represent the upper and lower bounds of the fractional modulation function, respectively. and For barrier operators, p These represent the upper and lower bounds of the constraint, respectively. Let s be the first derivative of time t; determine the performance boundary of PRVQ based on its dynamic characteristics and the convergence rate of the preset desired state; the convergence rate of the preset desired state is the pre-set PRVQ response rate; determine the time-varying performance function of PRVQ based on the performance boundary and the convergence trajectory of the preset desired state; determine the control law expression of PRVQ based on the derivative expression of the barrier Lyapunov function, the performance boundary, and the time-varying performance function, using Lyapunov stability theory. The control module is used to perform stability control on PRVQ according to the control law expression.

8. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the method described in any one of claims 1 to 6.

9. A computer device, characterized in that, The method 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 method described in any one of claims 1 to 6.

Images