Performance constraint control method, device and equipment of variant unmanned aerial vehicle and medium

Abstract

The invention provides a performance constraint control method, device and equipment for a variant unmanned aerial vehicle and a medium, and belongs to the field of unmanned aerial vehicle control, and the method comprises the steps: obtaining a current state and a preset expected state of a target umbrella-type telescopic variant unmanned aerial vehicle PRVQ; determining an error vector according to the current state and a preset expected state, and setting a sliding mode surface based on the error vector; constructing an obstacle Lyapunov function according to the error vector and the sliding mode surface, and determining a derivative expression of the obstacle Lyapunov function; determining the 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 a convergence trajectory of a preset expected state; according to the derivative expression of the obstacle Lyapunov function, the performance boundary and the time-varying performance function, a control law expression of the PRVQ is determined through a Lyapunov stability theory; and performing stability control on the PRVQ according to the control law expression. Therefore, the stability of the PRVQ is ensured by constructing and realizing the key links of the PRVQ stability control strategy.

Description

Technical Field

[0001] The present invention belongs to the field of unmanned aerial vehicle control, and in particular relates to a performance constraint control method, device, computer equipment and storage medium for a variant unmanned aerial vehicle. Background Art

[0002] Multi-rotor UAVs have been widely used in power line inspection, oil pipeline inspection, and leak detection due to their simple structure, strong flexibility, and low cost. They can also be used for reconnaissance, monitoring, and battlefield intelligence collection in complex terrain and urban environments. However, when facing complex and narrow environments, the fuselage size of multi-rotor UAVs with fixed structures cannot be adjusted, which limits their flight range and passability. Variant UAVs can change their structure. When performing reconnaissance and detection tasks in a specific environment, they can effectively avoid collisions according to specific mission characteristics and increase flight safety. The structural transformation of variant UAVs is suitable for tasks with limited space, and can flexibly shuttle and quickly respond to mission requirements; by reducing the arm distance, it helps the UAV to turn quickly and reduce the turning radius, improves maneuverability, and also improves the flexibility of the UAV, so that it is no longer constrained by the mission environment, thereby improving the versatility of the UAV. Therefore, it is of practical significance to study variant UAVs and their control methods.

[0003] The core of the control of the morphing drone is to directly change the shape and structure of the aircraft during flight while achieving precise control of the flight characteristics. When the morphing drone is flying, during the structural change process, some variables of the system need to be constrained for the sake of safe flight and physical limitations.

[0004] In the prior art, a variety of variant drones and control methods thereof have been proposed, such as a two-dimensional multi-link variable structure drone, in which the control part uses a linear quadratic (LQI) controller to compensate for the uncertainty in the attitude motion, and a PID controller is used for position control; or the structure is changed in various situations to improve the stability and efficiency of transporting payloads; there are also variant drones with rotatable arms, and the aerodynamic effects caused by the overlap of the propeller and the main body of the deformable quadcopter during flight are studied. There is also an active deformable quadcopter, and a flight controller is designed based on the active disturbance rejection control (ADRC) technology to suppress the influence of internal and external disturbances on the system.

[0005] The control methods used above can all be used to control the structural changes of the variant drone, but due to the structural changes when the variant drone is flying, there will be hidden dangers in the stability and safety of the variant drone. The LQI controller itself performs poorly in the attitude control system of the drone with complex conditions, large inertia and large lag, and there are safety hazards. The initial error of the PID controller may be large, which makes it impossible to meet the requirements of the dynamic performance stability of the variant drone in position control; and the structural changes may bring additional mechanical complexity and weight, which may bring safety risks during flight; the overlap of the propeller and the main body of the deformable quadcopter may cause complex aerodynamic effects, which may affect the flight performance and stability of the drone; and the active deformation based on ADRC technology may bring additional mechanical complexity and weight, resulting in affecting the safety and stability of the drone.

[0006] Therefore, there are safety and stability issues in the control of variant drones in the prior art. Summary of the invention

[0007] In order to solve the safety and stability problems in the process of variant UAV transformation, the present invention provides a performance constraint control method, device, computer equipment and storage medium for a variant UAV.

[0008] In order to achieve the above object, the present invention provides the following technical solutions:

[0009] First, a performance constraint control method for a variant UAV is provided, the method comprising:

[0010] Obtain the current state and preset expected state of the target parachute telescopic variant UAV PRVQ;

[0011] Determining an error vector according to the current state and a preset desired state, and setting a sliding surface based on the error vector;

[0012] Based on the performance constraints of the sliding surface, mapping the sliding surface into a dynamic geometric framework;

[0013] Constructing an obstacle Lyapunov function according to the error vector and the dynamic geometric framework, and determining a derivative expression of the obstacle Lyapunov function;

[0014] Determine the performance boundary of the PRVQ according to the dynamic characteristics of the PRVQ and the convergence speed of the preset expected state; the convergence speed of the preset expected state is the preset PRVQ response speed;

[0015] Determining a time-varying performance function of the PRVQ according to the performance boundary and a convergence trajectory of a preset expected state;

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

[0017] The stability of the PRVQ is controlled according to the control law expression.

[0018] Optionally, determining an error vector according to the current state and a preset expected state, and setting a 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, a state change trajectory that guides the current state of the PRVQ to the desired state is determined, and the state change trajectory is determined to be a sliding surface.

[0021] Optionally, constructing an obstacle Lyapunov function according to the error vector and the dynamic geometry framework comprises:

[0022] Based on the boundary of the dynamic geometric framework, an adjustment operator and an obstacle operator for boundary constraint are introduced; the adjustment operator and the obstacle operator respectively constrain the performance of the sliding surface from different angles;

[0023] Constructing a general formula of an obstacle Lyapunov function according to the adjustment operator and the obstacle operator;

[0024] The type of the barrier Lyapunov function formula is selected according to the current state, and the barrier Lyapunov function is constructed in combination with the constraint boundary of the sliding surface.

[0025] Optionally, according to the dynamic characteristics of the PRVQ and the convergence speed of a preset expected state, determining the performance boundary of the PRVQ includes:

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

[0027] According to the preset control requirements, the convergence speed of the PRVQ desired state is set;

[0028] In combination with the dynamic characteristics and convergence speed of the PRVQ, the constraints satisfied by the PRVQ during the adjustment process are determined, and the performance boundary of the PRVQ is obtained based on the constraints.

[0029] Optionally, determining the time-varying performance function of PRVQ according to the performance boundary and the convergence trajectory of the expected state includes:

[0030] Plan the convergence trajectory of the PRVQ state trajectory based on the convergence speed and performance boundary of the expected state;

[0031] The performance change of PRVQ during the adjustment process is evaluated by combining the convergence trajectory and performance boundary to obtain the time-varying performance function.

[0032] Optionally, performing stability control on the PRVQ according to the control law expression includes:

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

[0034] Determine the control input of the current PRVQ according to the real-time state and the control law expression;

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

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

[0037] An acquisition module is used to acquire the current state and preset expected state of the target parachute telescopic variant UAV PRVQ;

[0038] A determination module, used to determine an error vector according to the current state and a preset desired state, and set a sliding surface based on the error vector; based on the performance constraint of the sliding surface, map the sliding surface to a dynamic geometric framework; construct an obstacle Lyapunov function according to the error vector and the dynamic geometric framework, and determine a derivative expression of the obstacle Lyapunov function; determine a performance boundary of the PRVQ according to the dynamic characteristics of the PRVQ and the convergence speed of the preset desired state; the convergence speed of the preset desired state is a preset PRVQ response speed; determine a time-varying performance function of the PRVQ according to the performance boundary and the convergence trajectory of the preset desired state; determine a control law expression of the PRVQ through the Lyapunov stability theory according to the derivative expression of the obstacle Lyapunov function, the performance boundary and the time-varying performance function;

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

[0040] In addition, a computer-readable storage medium is provided, which stores a computer program, and when the computer program is executed by a processor, the performance constraint control method of the above-mentioned variant drone is implemented.

[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 implements the performance constraint control method of the above-mentioned variant UAV when executing the program.

[0042] The performance constraint control method of a variant UAV provided by the present invention has the following beneficial effects:

[0043] First, by obtaining the current state and the preset expected state of the umbrella-type telescopic variant drone, the necessary input information is provided for the subsequent control strategy, ensuring targeted adjustments to the actual situation of the drone; secondly, by calculating the error vector between the current state and the expected state, the direction and size of the PRVQ adjustment can be clarified, and the sliding surface is set to achieve fast and stable error convergence and improve the robustness of the system; mapping the sliding surface to the dynamic geometric framework can more intuitively understand and analyze the control process of the drone, which helps to optimize the control strategy while considering performance constraints, ensuring that the drone does not exceed its physical or performance limitations during the control process, thereby improving the safety and stability of the flight; constructing the obstacle Lyapunov function is to evaluate the stability of the system, and express it through its derivative The changing trend of the system state is observed by the formula, which is conducive to accurately evaluating the current state of PRVQ; then, by considering the dynamic characteristics of PRVQ and the convergence speed of the desired state, a reasonable performance boundary can be set to ensure that the UAV can maintain its physical and performance characteristics during the control process, and avoid overload or instability of the UAV during the control process; then, by setting a reasonable time-varying performance function, it is ensured that the UAV can maintain stable performance output during the control process, which is helpful to improve the control accuracy and stability of the UAV; finally, by combining the Lyapunov stability theory, this control law expression can ensure that the UAV maintains stability during the control process, while meeting various constraints and performance requirements, improving its ability to adapt to complex environments, and helping to improve the safety and control performance of the UAV. In this way, the constraints and control of the variables of the variant UAV system are completed through various steps, and the stability and safety of the variant control during the flight of PRVQ are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiment of the present invention and its design scheme, the following briefly introduces the drawings required for this embodiment. The drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 The present invention is a flowchart of a performance constraint control method for a variant UAV provided according to an exemplary embodiment.

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

[0047] Figure 3A 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 changes in performance boundaries in the dynamic geometric framework.

[0048] Figure 4 A block diagram of a performance constraint control device for a variant UAV provided by the present invention according to an exemplary embodiment. DETAILED DESCRIPTION

[0049] In order to enable those skilled in the art to better understand the technical solution of the present invention and implement it, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the scope of protection of the present invention.

[0050] The present invention converts system state constraints into geometric constraints based on a dynamic geometric framework, designs a new tangent barrier Lyapunov function (Barrier Lyapunov function, BLF) general formula, and combines it with predefined time control to control the Parachute Retractable Variable Quadrotor (PRVQ) model designed by the present invention.

[0051] The technical solutions provided by various embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0052] First, the present invention provides a performance constraint control method for a variant UAV, specifically, Figure 1 As shown, the following steps are included:

[0053] S101. Obtain the current state and preset expected state of the target parachute-type retractable variant UAV PRVQ.

[0054] In this step, the sensor system integrated on the PRVQ (such as GPS module, attitude sensor, altimeter, etc.) can be used to collect the current status information of the drone in real time, such as position, speed, attitude angle (pitch angle, roll angle, yaw angle), etc. These sensor data are read and analyzed by a data processing unit (such as a microcontroller or computer).

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

[0056] S102: Determine an error vector according to the current state and a preset expected state, and set a sliding surface based on the error vector.

[0057] Among them, sliding mode control is a nonlinear control strategy, the core idea of ​​which is to control the system performance by designing an appropriate sliding surface so that the system state reaches and remains on the sliding surface within a finite time. In sliding mode control, the design of the sliding surface is the key, which directly determines the stability and performance of the system.

[0058] In this step, the current state of the PRVQ can be compared with the preset desired state, and the error value of each state variable can be calculated to form an error vector; the error vector represents the deviation between the current state of the PRVQ and the desired state. Based on the error vector, the state change trajectory that guides the current state of the PRVQ to the desired state is determined, and the state change trajectory is determined to be the sliding surface. The sliding surface is usually a hyperplane associated with the error vector, which is used to guide the state trajectory of the PRVQ to slide along the plane and finally 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: Mapping the sliding surface into a dynamic geometric framework based on the performance constraints of the sliding surface.

[0060] The dynamic geometry framework is a method that transforms constraint problems into geometric relationships and understands and handles constraint problems by observing and analyzing changes in geometric shapes.

[0061] In this step, the boundaries of the dynamic geometric framework are the geometric restrictions that the sliding surface should meet. These boundaries can be straight lines, curves or more complex geometric shapes, depending on the constraints and performance requirements of the system. The present invention here designs a dynamic BLF geometric framework based on PRVQ, which will be described in detail later. Mapping the sliding surface to the preset boundary in the dynamic geometric framework involves matching or converting the equation of the sliding surface with the equation of the preset boundary to ensure that the sliding surface meets all constraints. During the mapping process, it is necessary to adjust the parameters of the sliding surface and the preset boundary to ensure the matching degree between them and the performance of the system, including adjusting the slope, intercept, shape and position of the preset boundary of the sliding surface. Finally, the correctness and effectiveness of the mapping results can also be verified by simulation or experiment, including checking whether the system state can reach and remain on the sliding surface within a limited time, and whether the system meets all performance constraints.

[0062] In the above steps, the dynamic BLF geometry framework is a method to convert system state constraints into geometric constraints. Through this framework, complex BLF design problems can be transformed into intuitive geometric relationship problems, which greatly simplifies the BLF design process. This framework provides a flexible and efficient method for designing logarithmic, tangent and quadratic fraction BLF functions, and can adapt to the needs and constraints of different systems.

[0063] S104, constructing an obstacle Lyapunov function according to the error vector and the dynamic geometric framework, and determining a derivative expression of the obstacle Lyapunov function.

[0064] In this step, an obstacle Lyapunov function is constructed using the error vector and the dynamic geometry framework. This function is used to evaluate the distance between the PRVQ state trajectory and the sliding surface and the stability of the system. The selection and design of the obstacle Lyapunov function should meet the conditions of the Lyapunov stability theorem. The barrier Lyapunov function is differentiated to obtain its derivative expression. This derivative expression is used for subsequent control law design and stability analysis.

[0065] Specifically, based on the boundary of the dynamic geometric framework, an adjustment operator and an obstacle operator for boundary constraints are introduced; the adjustment operator and the obstacle operator 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 operator and the obstacle operator; 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, first, the system state of the parachute telescopic variant unmanned aerial vehicle (PRVQ) is clarified, including key variables such as position and attitude. Then, according to the mission requirements and flight safety, the constraint boundaries of the system state, that is, the upper and lower performance boundaries, are set. The present invention proposes a dynamic geometric framework to transform the constraint problem of the system state into a geometric relationship, thereby simplifying the design process of the BLF. In this framework, the constraint s and its upper and lower performance boundaries are set, and the constraint conditions of the system state are intuitively represented by geometric relationships. Then, based on the dynamic geometric framework, general formulas that conform to the logarithmic, tangent and quadratic fraction BLFs are derived. These general formulas have similar structural features, including adjustment operators and obstacle operators, which are used to adjust the constrained amount of the system and prevent the system state from exceeding the constraint boundary. According to the system state and constraint conditions, an appropriate BLF type (such as tangent type) is selected. Using the derived BLF general formula, combined with system parameters and constraint boundaries, a specific BLF function is constructed. After that, the constructed BLF function is differentiated to obtain its derivative expression, which will be used in the subsequent control law design to ensure that the system state always remains within the constraint boundary. In addition, the present invention also combines a predefined time control method, and achieves the control goal of the system state tending to the expected value within the predefined time by designing a performance function with a decay term, which further improves the robustness and performance of the control system.

[0067] S105 : Determine the performance boundary of the PRVQ according to the dynamic characteristics of the PRVQ and the convergence speed of the preset expected state.

[0068] The convergence speed of the preset expected state is a preset PRVQ response speed.

[0069] Specifically, the PRVQ may first be subjected to a dynamic analysis to determine the dynamic characteristics of the PRVQ. For example, the PRVQ's mechanical structure, mass distribution, power system, and other factors may be considered to analyze its dynamic characteristics. These characteristics include the mass, moment of inertia, aerodynamic parameters, and the like of the drone.

[0070] Secondly, according to the preset control requirements, the convergence speed of the PRVQ desired state is set. The convergence speed reflects the speed and efficiency of the PRVQ adjusting from the current state to the desired state.

[0071] Finally, combined with the dynamic characteristics and convergence speed of PRVQ, the constraints that PRVQ meets during the adjustment process are determined, and the performance boundary of PRVQ is obtained based on the constraints, which include restrictions on speed, acceleration, attitude angle change rate, etc.

[0072] S106: Determine a time-varying performance function of PRVQ according to the performance boundary and a convergence trajectory of a preset expected state.

[0073] In this step, the convergence trajectory of the PRVQ state trajectory can be planned according to the convergence speed and performance boundary 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 in combination with the convergence trajectory and the performance boundary to obtain a time-varying performance function. This function is used to evaluate the performance changes of the PRVQ during the adjustment process, including changes in states such as speed, acceleration, and attitude angle. The selection and design of the time-varying performance function should meet the task requirements and control requirements.

[0074] S107. According to the derivative expression of the obstacle Lyapunov function, the performance boundary and the time-varying performance function, the control law expression of the PRVQ is determined by Lyapunov stability theory.

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

[0076] S108. Perform stability control on the 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 control input of the current PRVQ according to the real-time state and the control law expression; and perform real-time stability control on the PRVQ based on the control input.

[0078] Specifically, the control input calculated by the control law expression is applied to the control system of the PRVQ. The control input can be a command in the form of motor speed, steering gear deflection angle, etc., which is used to adjust the position and attitude of the UAV. By continuously acquiring the current state information of the PRVQ and calculating the control input according to the control law expression, the PRVQ is controlled in real time for stability. During the control process, it is necessary to monitor the state changes of the UAV and adjust the control input as needed to cope with external disturbances and uncertain factors. At the same time, it is also necessary to evaluate and feedback the control effect to continuously optimize the control law and improve the control performance.

[0079] Based on the above method, the present invention also provides a formula derivation process of a performance constraint control method of a variant UAV, which is as follows:

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

[0081]

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

[0083]

[0084] Among them, 0<μ<1, adjustment time T pc for:

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

[0086] Assumption 2: The quadrotor drone body is a rigid structure, and the drone's geometric center coincides with its center of gravity.

[0087] 1.2 PRVQ mathematical model

[0088] In a small and complex environment, PRVQ changes the arm distance l by changing the length h of the telescopic rod below, so that it can adapt to different environments and flight requirements. Construct the PRVQ coordinate system framework, 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 various physical quantity labels such as Figure 2 As shown in the figure, when the arm distance of the PRVQ changes, its dynamic characteristics will change accordingly, especially in terms of attitude control. A mechanical telescopic rod is installed under the PRVQ designed by the present invention to accurately control the length of the quadcopter arm. The physical quantity of the telescopic rod m l Indicates equivalent mass; h is a variable length that is subject to physical limitations, for example: h min ≤h≤h max ,h min and h max Indicates the shortest and longest telescopic lengths of the rod. The change h of the telescopic rod is related to the change γ of the fan-shaped rotating connecting rod (the specific installation position is in Figure 2 The physical relationship of the highlighted ones is as follows:

[0089]

[0090]

[0091] The transmission relationship of PRVQ is: the telescopic rod drives the fan-shaped rotating link to rotate, so that the length of the drone arm changes. The telescopic rod and the fan-shaped rotating link are connected by a rigid connecting rod. When the telescopic rod is extended by h, the fan-shaped rotating link on the stretched PRVQ arm rotates downward by γ degrees, and the length of the PRVQ arm is shortened. Based on this, the mathematical model of PRVQ is:

[0092]

[0093] Where x, y, z represent the position coordinates of PRVQ in 3D space; φ, θ, ψ are defined as roll angle, pitch angle, and yaw angle, respectively; u φ ,u θ ,u ψ Represents attitude system control input; I xx (l),I yy (l),I zz(l) represents the UAV moment of inertia, which is directly affected by the change of the arm length; m, g represent the mass of PRVQ and the acceleration of gravity; l(t) is the time-varying arm length value, which represents the distance between the PRVQ center of mass and the center of the motor rotor; u f represents the total lift, which is divided into 6 control channels for PRVQ p = {x, y, z, φ, θ, ψ}; k p Indicates the air resistance coefficient; d p Indicates the external unknown disturbance to the system. Comprehensive three moments of inertia of the body:

[0094]

[0095] Where m eq is the PRVQ equivalent mass; m mo is the mass of the driving motor; m arm is the mass of the arm; l eq ,h eq is the length, width and height of the equivalent mass block; l arm is the equivalent arm length.

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

[0097]

[0098] Where: is the control input,

[0099]

[0100]

[0101]

[0102] According to the desired angle v required by the control amount 4d ,v 5d and the expected lift u f for:

[0103]

[0104] The present invention proposes the following from the perspective of geometry: Figure 3 (a) The dynamic BLF geometric framework, with constraints s and their upper and lower performance boundaries. and p (t)<0, satisfying This simplifies the design of BLF. By converting 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) The change of performance boundary in the dynamic geometric framework represented by and lower bound p The distance between (t) gradually shrinks with time. In the steady-state stage of the system, by tightening the performance boundary, the system response can be forced to be closer to the expectation and the control accuracy can be improved.

[0105] Common BLF forms include: Quadratic Fractional BLF, qfBLF, logarithmic BLF (lnBLF), tangent BLF (tanBLF). From the three BLF configurations currently studied as a starting point, it can be concluded that BLFs have similar structural characteristics. For the sake of convenience, the adjustment operator is proposed here: It is used to adjust the constrained amount of the system so that after the system is away from the constraint boundary and satisfies the constraint conditions and reaches a balanced state, V B The value of tends to zero. Barrier operator: When the system state or the constrained quantity is close to the constraint boundary, V B The value tends to infinity, thus preventing the system state from exceeding the constraint boundary. Fractional modulation function: is a fractional function composed of a regulating operator and a barrier operator, which is used to construct different types of BLFs. Then the BLF and its derivative can be simply expressed as:

[0106]

[0107] Combination Figure 3 , the geometric relationship in the figure and the corresponding and Classification is shown in Table 1:

[0108] Table 1 Classification of adjustment operators and barrier operators

[0109]

[0110] The present invention Figure 3 As a reference, a new tanBLF formula is designed, which can be used for asymmetric constraints. According to the classification table 1 and the structural characteristics of the three types of BLF, the following formula is obtained:

[0111]

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

[0113]

[0114] The derivative of formula (10) is:

[0115]

[0116] According to formula (11), since the present invention defines the adjustment operator Its derivative is There is s in the derivative, which can be taken as a common factor to facilitate the design of control law. Now analyze formula (9), V tanBLF There are two terms, first take the derivative of the first term:

[0117]

[0118]

[0119] Similarly, take the derivative of the second term:

[0120]

[0121]

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

[0123]

[0124] According to the characteristics of BLF general formula (9), the general formula of the derivative of the obstacle Lyapunov function is given:

[0125]

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

[0127]

[0128] In the formula Represent the expected value of the PRVQ state and its derivative, and define the sliding surface:

[0129]

[0130] Where: 0<η si <1, T si >0 indicates a predefined time, α si ,β si> 0. The barrier Lyapunov function is defined as:

[0131]

[0132] According to formula (17), we can find its derivative:

[0133]

[0134]

[0135]

[0136]

[0137]

[0138]

[0139]

[0140] The present invention is aimed at tanBLF and designs the performance boundary When t>T ci There should be The general performance function p(t) = (p0-p ∞ ) -λt +p ∞ The transition time is not set, or is implemented through a piecewise function, while the present invention designs a performance function with an attenuation term, which has a continuous characteristic.

[0141]

[0142] In the formula, k pi ,T ci ,λ i is the design parameter, p0=k pi T ci +p i∞ , T ci Represents the performance function from the initial value p0 to the final value p ∞ The transition time, w p Used to control the first attenuation, when t = T c When , the first attenuation coefficient is In order to achieve the design purpose, set w p =6. The performance boundary derivative is:

[0143]

[0144]

[0145] Then the designed control law is:

[0146]

[0147] Then the sliding mode reaching law is:

[0148]

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

[0150]

[0151] In the formula Take c di ≥D i , by Lemma 1 we can get s i At the predefined time T i tends to zero.

[0152] When i = 0, there is Use Lyapunov function:

[0153]

[0154] The derivative is:

[0155]

[0156] According to Lemma 1, we can get 1i At the predefined time T si Converges to 0 internally.

[0157] In summary, the system is si +T i Reaching equilibrium within.

[0158] In order 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 paper, two control methods are selected for comparison, namely predefined time sliding mode control (PTSC) and BLF backstepping control (B-BLFC), and simulation experiments are carried out. 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 Numeric symbol Numeric <![CDATA[T sj ]]> [0.5,0.5,1.5] <![CDATA[β sj ]]> [0.5,0.5,0.5] <![CDATA[T sk ]]> [0.5,0.5,0.5] <![CDATA[β sk ]]> [5,3,3] <![CDATA[T j ]]> [1,1,2] <![CDATA[β j ]]> [3,3,3] <![CDATA[T k ]]> [1,1,1] <![CDATA[β k ]]> [3,3,3] <![CDATA[α sj ]]> [0.01,0.01,1] <![CDATA[η sj ]]> [0.5,0.5,0.4] <![CDATA[α sk ]]> [1,1,1] <![CDATA[η sk ]]> [0.5,0.3,0.3] <![CDATA[ α j ]]> [1,1,3] <![CDATA[η j ]]> [0.75,0.75,0.75] <![CDATA[α k ]]> [1,1,1] <![CDATA[η k ]]> [0.5,0.5,0.4]

[0161] Within 2s, h changes in the range of [0,0.055]m, corresponding to γ ​​changes in the range of [0,1.026]rad, and the arm distance l arm The corresponding decrease is [0.17,0.11]m, and the moment of inertia is also reduced accordingly. zzVariation range [0.029, 0.018], I xx ,I yy Variation range [0.014, 0.009].

[0162] In the initial stage, all three control methods have no overshoot. The TA-TBLF and PTSC control methods make the attitude error converge quickly, with a convergence time of about 1s, while the B-BLFC control converges slowly, with a convergence time of about 3s. In the arm distance switching process after 10s, TA-TBLF has higher control performance. The performance boundary is set to make the attitude error jump range smaller than the other two control methods, while the attitude error jump range of the PTSC control method is larger. If the PRVQ needs to switch the arm distance during the mission, the TA-TBLF control is more robust and can effectively cope with the impact of the arm distance change and maintain smooth attitude control.

[0163] Because of the coupling relationship between PRVQ attitude control and position control, the change of arm distance not only affects the attitude dynamic characteristics, but also affects the position characteristics. When the arm distance decreases, the rotational inertia of PRVQ decreases accordingly, the anti-disturbance ability of the UAV decreases, and the stability decreases. In the 10-20s stage, the position errors of the three control methods all fluctuate, and the fluctuation of PTSC is more obvious. The fluctuation range of the x and y direction errors of PTSC is affected by the turbulent wind field and deviates from the zero scale, while the TA-tBLF and B-BLFC controls set the performance boundary to constrain the system state, making the error more concentrated on the zero scale.

[0164] The core goal of control system design is to achieve system error convergence, and the sliding surface can be regarded as a synthesis of system errors, describing the dynamic deviation between the system state and the expected value. This paper constrains the sliding surface through BLF, so as to constrain the PRVQ system state while optimizing the dynamic response characteristics of the system.

[0165] At the beginning, because the deviation between the initial value and the expected value of the system is large, the three control methods all have large outputs, while the outputs of TA-tBLF and PTSC are smaller than those of B-BLFC, and they have faster convergence speeds. As the deviation decreases, the control laws of the three methods are relatively stable, but after the control law of TA-tBLF stabilizes, the control law jumps again around 0.5s, because the set time-varying performance function is at the transition time T c The rear boundary value is Monotonically decreasing and stable at and p ∞ , so the control law jumps and controls the dynamic system to transition to the next stage.

[0166] B-BLFC designs the control law by directly constraining the system error. When the arm distance changes, the control law also changes suddenly. Compared with TA-tBLF, which constrains the sliding surface, the control law has a smaller mutation amplitude and changes more slowly.

[0167] By adopting the above method, firstly, by obtaining the current state of PRVQ and the preset expected state, the necessary input information is provided for the subsequent control strategy; secondly, by calculating the error vector between the current state and the expected state, the direction and size of the adjustment of PRVQ can be clarified, and the sliding surface is set to achieve fast and stable error convergence and improve the robustness of the system; the barrier Lyapunov function is constructed to evaluate the stability of the system, and the trend of the change of the system state is observed through its derivative expression, which is conducive to the accurate evaluation of the current state of PRVQ; then, by considering the dynamic characteristics of PRVQ and the convergence speed of the expected state, a reasonable performance boundary can be set to ensure that the control strategy is neither too radical to cause system instability nor too conservative to affect the response speed; then, 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 the Lyapunov stability theory, a control law that can both ensure the stability of the system and meet the performance requirements can be designed, which is helpful to achieve the precise control of PRVQ and improve its ability to adapt to complex environments. In this way, the key links of constructing and implementing the stability control strategy of PRVQ through various steps ensure that PRVQ can perform tasks accurately and stably.

[0168] Secondly, the present invention also provides a performance constraint control device for a variant UAV, such as Figure 4 As shown, including:

[0169] The acquisition module 401 is used to acquire the current state and the preset expected state of the target umbrella-type telescopic variant UAV PRVQ.

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

[0171] The control module 403 is used to perform stability control on the PRVQ according to the control law expression. The present invention also provides a computer readable storage medium, which stores a computer program, which can be used to execute the above Figure 1 The steps of a performance constraint control method for a variant UAV are provided.

[0172] By adopting the above device, firstly, the current state of PRVQ and the preset expected state are obtained, which provides the necessary input information for the subsequent control strategy; secondly, by calculating the error vector between the current state and the expected state, the direction and size of the adjustment of PRVQ can be clarified, and the sliding surface is set to achieve fast and stable error convergence and improve the robustness of the system; the barrier Lyapunov function is constructed to evaluate the stability of the system, and the change trend of the system state is observed through its derivative expression, which is conducive to the accurate evaluation of the current state of PRVQ; then, by considering the dynamic characteristics of PRVQ and the convergence speed of the expected state, a reasonable performance boundary can be set to ensure that the control strategy is neither too radical to cause system instability nor too conservative to affect the response speed; then, 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 the Lyapunov stability theory, a control law that can both ensure the stability of the system and meet the performance requirements can be designed, which is helpful to achieve the precise control of PRVQ and improve its ability to adapt to complex environments. In this way, the key links of constructing and implementing the stability control strategy of PRVQ through various steps ensure that PRVQ can perform tasks accurately and stably.

[0173] The present invention also provides a computer device. At the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory. Of course, it may also include hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 The steps of a performance constraint control method for a variant UAV are provided.

[0174] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented 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] The present invention is described with reference to flowcharts and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as a combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0176] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0177] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0178] It should be noted that the above specific implementation method can enable those skilled in the art to more fully understand the invention, but does not limit the invention in any way. Therefore, although the invention has been described in detail in this specification, those skilled in the art should understand that the invention can still be modified or replaced by equivalents; and all technical solutions and improvements that do not deviate from the spirit and scope of the invention are included in the protection scope of the patent for the invention. Any figure mark in the claims should not be regarded as limiting the claims involved.

Claims

1. A performance constraint control method for a variant UAV, characterized in that: The method comprises: Obtain the current state and preset expected state of the target parachute telescopic variant UAV PRVQ; Determining an error vector according to the current state and a preset desired state, and setting a sliding surface based on the error vector; Based on the performance constraints of the sliding surface, mapping the sliding surface into a dynamic geometric framework; Constructing an obstacle Lyapunov function according to the error vector and the dynamic geometric framework, and determining a derivative expression of the obstacle Lyapunov function; Determine the performance boundary of the PRVQ according to the dynamic characteristics of the PRVQ and the convergence speed of the preset expected state; the convergence speed of the preset expected state is the preset PRVQ response speed; Determining a time-varying performance function of the PRVQ according to the performance boundary and a convergence trajectory of a preset expected state; The control law expression of PRVQ is determined based on the derivative expression of the obstacle Lyapunov function, the performance boundary and the time-varying performance function; The stability of the PRVQ is controlled according to the control law expression.

2. The performance constraint control method of a variant UAV according to claim 1 is characterized in that: Determining an error vector according to the current state and a preset expected state, and setting a sliding surface based on the error vector includes: 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, a state change trajectory that guides the current state of the 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 of a variant UAV according to claim 1 is characterized in that: Constructing the obstacle Lyapunov function according to the error vector and the dynamic geometry framework includes: Based on the boundary of the dynamic geometric framework, an adjustment operator and an obstacle operator for boundary constraint are introduced; the adjustment operator and the obstacle operator respectively constrain the performance of the sliding surface from different angles; Constructing a general formula of an obstacle Lyapunov function according to the adjustment operator and the obstacle operator; The type of the barrier Lyapunov function formula is selected according to the current state, and the barrier Lyapunov function is constructed in combination with the constraint boundary of the sliding surface.

4. The performance constraint control method of a variant UAV according to claim 1 is characterized in that: According to the dynamic characteristics of PRVQ and the convergence speed of the preset expected state, the performance boundaries of PRVQ are determined as follows: Perform kinetic analysis on PRVQ to determine the kinetic characteristics of PRVQ; According to the preset control requirements, the convergence speed of the PRVQ desired state is set; In combination with the dynamic characteristics and convergence speed of the PRVQ, the constraints satisfied by the PRVQ during the adjustment process are determined, and the performance boundary of the PRVQ is obtained based on the constraints.

5. The performance constraint control method of a variant UAV according to claim 1 is characterized in that: According to the performance boundary and the convergence trajectory of the desired state, determining the time-varying performance function of PRVQ includes: Plan the convergence trajectory of the PRVQ state trajectory based on the convergence speed and performance boundary of the expected state; The performance change of PRVQ during the adjustment process is evaluated by combining the convergence trajectory and performance boundary to obtain the time-varying performance function.

6. The performance constraint control method of a variant UAV according to claim 1 is characterized in that: The stability control of PRVQ according to the control law expression includes: Get the real-time status of PRVQ; Determine the control input of the current PRVQ according to the real-time state and the control law expression; Real-time stability control of the PRVQ is performed based on the control input.

7. A performance constraint control device for a variant UAV, characterized in that: The device comprises: An acquisition module, used to acquire the current state and preset expected state of the target parachute telescopic variant UAV PRVQ; A determination module, used to determine an error vector according to the current state and a preset desired state, and set a sliding surface based on the error vector; based on the performance constraint of the sliding surface, map the sliding surface to a dynamic geometric framework; construct an obstacle Lyapunov function according to the error vector and the dynamic geometric framework, and determine a derivative expression of the obstacle Lyapunov function; determine a performance boundary of the PRVQ according to the dynamic characteristics of the PRVQ and the convergence speed of the preset desired state; the convergence speed of the preset desired state is a preset PRVQ response speed; determine a time-varying performance function of the PRVQ according to the performance boundary and the convergence trajectory of the preset desired state; determine a control law expression of the PRVQ through the Lyapunov stability theory according to the derivative expression of the obstacle Lyapunov function, the performance boundary and the time-varying performance function; A control module is used to perform stability control on the PRVQ according to the control law expression.

8. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

9. A computer device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method according to any one of claims 1 to 6 is implemented.

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