Path following control method based on quasi-linear parameter change model and related device

Abstract

The embodiment of the invention discloses a path following control method based on a quasi-linear parameter change model and a related device. A quasi-linear parameter system model in a linear or semi-linear form is constructed according to an unmanned aerial vehicle kinematics nonlinear system model so as to capture nonlinear characteristics of the system, and the nonlinear model can be accurately approached. The speed-based linearization is a strategy for accurately representing system nonlinear dynamics in a linear form, and the precision of a system model is greatly improved compared with that of a linear system. As a linear form is reserved, the speed of the algorithm is greatly improved compared with that of a control algorithm of a nonlinear system. A qLPV system is adopted for modeling, the limitation that a linear system is low in modeling precision and a nonlinear predictive control algorithm is low in speed is overcome, the dynamic characteristics of a nonlinear system can be accurately described, meanwhile, a linear form or a semi-linear form is reserved, solving can be conducted through a fast algorithm solver, a fast control algorithm is designed, and real-time control is achieved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of control of unmanned driving equipment, and specifically to a path following control method and related devices based on a quasi-linear parameter change model. Background Art

[0002] Model predictive control (MPC) has become one of the most effective techniques among all control-oriented path-following methods. It transforms the control problem into an online optimization problem, computing a sequence of future control values at each sampling time by solving an optimal control problem. A key advantage is its ability to handle constraints on inputs and states while ensuring high performance. Its main drawback is that computational and memory resources increase rapidly with longer time horizons. With the significant increase in computing power, several efficient software frameworks for nonlinear MPC have emerged, such as acados, GRAMPC, and OpEn.

[0003] However, UAV systems have much faster dynamic speeds, which means shorter sampling periods are required. For example, the controllers of fixed-wing aircraft operate in milliseconds. Therefore, it is urgent to design a method that is computationally efficient but still can capture the nonlinear dynamics of the system and controller design. Summary of the Invention

[0004] The embodiments of the present application provide a path following control method and related devices based on a quasi-linear parameter change model. By constructing a quasi-linear parameter system model with a linear or semi-linear form according to the nonlinear system model of the UAV kinematics to capture the nonlinear characteristics of the system, the accuracy of the system model is greatly improved compared to the linear system.

[0005] A first aspect of an embodiment of the present application provides a path following control method based on a quasi-linear parameter change model, the method comprising:

[0006] Constructing a kinematic model of the unmanned driving device, wherein the kinematic model is used to describe the motion state of the unmanned driving device in space and time;

[0007] Establishing a quasi-linear parameter change model of the unmanned driving device based on the motion state described by the kinematic model;

[0008] Expanding the quasi-linear parameter change model to obtain a quasi-linear parameter change expansion equation;

[0009] Generating a cost function for path following according to the quasi-linear parameter variation expansion equation, and adding constraints to the cost function; the constraints are obstacle-related constraints;

[0010] The cost function with the added constraint condition is solved to obtain control input data of the unmanned driving device, and the control input data is used to perform path following control on the unmanned driving device, so that the unmanned driving device can achieve autonomous obstacle avoidance and the path following control algorithm has stability assurance.

[0011] A second aspect of an embodiment of the present application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the method of the first aspect when executing the computer program.

[0012] A third aspect of an embodiment of the present application provides a computer storage medium, in which instructions are stored. When the instructions are executed on a computer, the computer executes the method of the first aspect.

[0013] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0014] By constructing a quasi-linear parameter system model in linear or semi-linear form based on the nonlinear kinematic system model of the drone, the nonlinear characteristics of the system are captured and accurately approximated. Velocity-based linearization is a strategy that accurately represents the nonlinear dynamics of the system in linear form, significantly improving the accuracy of the system model compared to linear systems. Because the linear form is retained, the algorithm is significantly faster than control algorithms for nonlinear systems.

[0015] The use of qLPV system modeling overcomes the limitations of low accuracy of linear system modeling and slow speed of nonlinear predictive control algorithms. It can accurately describe the dynamic characteristics of nonlinear systems while retaining the linear form or semi-linear form. It can be solved by a fast algorithm solver, and a fast control algorithm can be designed to achieve real-time control.

[0016] Simply by adding obstacle-related constraints to the constraint condition part, autonomous obstacle avoidance can be achieved, while ensuring the stability of the path following control algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of a path following control method based on a quasi-linear parameter change model in an embodiment of the present application;

[0018] Figure 2 This is another flow chart of the path following control method based on the quasi-linear parameter change model in an embodiment of the present application;

[0019] Figure 3 An exemplary path following control effect of a UAV achieved by applying the path following control method based on the quasi-linear parameter change model according to an embodiment of the present application;

[0020] Figure 4 An exemplary path-following-related variable for a UAV used in the path-following control method based on a quasi-linear parameter change model according to an embodiment of the present application;

[0021] Figure 5 This is a structural diagram of a computer device in an embodiment of the present application. DETAILED DESCRIPTION

[0022] The embodiments of the present application provide a path following control method and related devices based on a quasi-linear parameter change model. By constructing a quasi-linear parameter system model with a linear or semi-linear form according to the nonlinear system model of the UAV kinematics to capture the nonlinear characteristics of the system, the accuracy of the system model is greatly improved compared to the linear system.

[0023] Over the past few decades, the demand for drones (UAVs), from both military and civilian applications, has surged significantly. The need to increase the level of automation in UAV systems has become increasingly crucial. Path-following control remains one of the most critical applications for enhancing UAV flight autonomy. Currently, there are two main approaches to the path-following problem: first, geometric path following, which involves steering the aircraft in yaw and pitch according to the evolution of the path in pitch and yaw. This approach focuses on designing the shape of the desired path and ensuring accurate tracking while minimizing tracking error. Second, feedback control techniques are typically used to adjust the system's motion and trajectory to match the desired path. This approach utilizes control theory to design a controller that generates appropriate control inputs to guide the system toward the desired path. This approach may require the use of sensors, feedback control, and other techniques to achieve the desired path-following performance. The primary advantage of feedback control techniques is their simplicity and ease of implementation, but they may not provide optimal or robust performance in the presence of disturbances or model uncertainty.

[0024] In robotics applications, such as drones, unmanned underwater vehicles, unmanned surface vehicles, and unmanned ground vehicles, many experts have proposed various control theory techniques to address path following. Key approaches include proportional-integral-derivative (PID) control, linear quadratic regulator (LQR), sliding mode control, model predictive control (MPC), backstepping control, and adaptive control. However, these approaches also face challenges. For one thing, most existing path-tracking control laws are nonlinear, which poses challenges in ensuring stability and performance for accurate path following under varying path and environmental conditions. Furthermore, linear control laws are insufficient for characterizing nonlinear systems.

[0025] Furthermore, model predictive control (MPC) has become one of the most effective techniques among all control-oriented path-following methods. It transforms the control problem into an online optimization problem, where at each sampling time, a sequence of future control values is calculated by solving an optimal control problem. One of its key advantages is its ability to handle constraints on inputs and states while ensuring high performance. Its main disadvantage is that computational and memory resources increase rapidly as the time horizon increases. With the significant improvement in computer computing power, several efficient software frameworks for nonlinear model predictive control have emerged, such as acados, GRAMPC, OpEn, and other software tools. However, UAV systems have faster dynamic speeds, which means shorter sampling periods are required. For example, the controller of a fixed-wing aircraft runs in milliseconds. Therefore, there is an urgent need to design a method that is computationally efficient but still captures the nonlinear dynamics of the system and controller design.

[0026] In order to solve the above technical problems, the embodiment of the present application proposes a path following control method for unmanned driving equipment with anti-disturbance and stability guarantee, which belongs to the control field of unmanned driving equipment. Among them, the unmanned driving equipment can be a drone (such as a fixed-wing unmanned aerial vehicle), an unmanned vehicle and other equipment with autonomous movement capabilities and capable of moving based on a pre-planned path. For example, in drone control, simple path point flight will lead to discontinuity in path curvature. In order to overcome this problem, the embodiment of the present application uses a path planner that combines arc length and cubic spline to provide online path planning based on a set of path points. The path planner generates a position reference signal, and the path following controller generates a position control command to ensure accurate tracking of the position reference signal.

[0027] According to the nonlinear kinematic model of the UAV, the embodiment of the present application adopts a speed-based linearization method to obtain a quasi-linear parameter variation (qLPV, quasi-linear parameter variation) model, and transforms the path following control problem into a quasi-linear model predictive control based on qLPV. The quadratic programming problem is solved within milliseconds by a fast solver to obtain the control parameters of the path following controller, thereby realizing the path following control of the UAV. Compared with the technical problems existing in the existing technology, the advantage of the embodiment of the present application is that the quasi-linear parameter variation (qLPV) model is used to capture the nonlinearity of the nonlinear model. Compared with the traditional linear model predictive control technology, the quasi-linear parameter variation model predictive control (qLMPC) method provides a better modeling accuracy. In addition, it solves the uncertainty and interference in the system by considering the time-varying nature of the system parameters, thereby having a larger stability margin and performance robustness.

[0028] The following describes the path following control method based on the quasi-linear parameter change model in the embodiment of the present application:

[0029] See also Figure 1 In one embodiment of the present application, a path following control method based on a quasi-linear parameter change model includes:

[0030] 101. Construct a kinematic model of an unmanned driving device, wherein the kinematic model is used to describe the motion state of the unmanned driving device in space and time;

[0031] The method of this embodiment can be applied to a computer device, which can serve as a control device for an unmanned driving device to plan a path for the unmanned driving device and perform path-following control on the unmanned driving device.

[0032] Specifically, when constructing a kinematic model for an unmanned vehicle, the kinematic model can be constructed based on one or more of the following: the unmanned vehicle's positional state information, aerodynamic heading angle, flight track angle, wind speed, slope angle, incremental load factor signal, and airspeed. The unmanned vehicle can be a drone, an unmanned vehicle, or other device with autonomous motion capabilities that can move along a pre-planned path. In patrol inspection scenarios, drones can be used for known and potential technology / product application areas and their application methods in industries such as initial bridge inspections, streetlight pole inspections, and external pipeline inspections.

[0033] For example, for a drone, in its nonlinear system model, its kinematic model is used to describe the motion of a moving body in space and time. Its motion can be described by a few parameters, such as position, velocity, and direction, without considering any forces and torques that cause the motion. For example, the kinematic model of a three-dimensional fixed-wing drone can be described as:

[0034]

[0035] in is the position state, χ is the aerodynamic heading angle, γ is the flight path angle, V W is the wind speed. The control inputs are φ (slope angle), n z (incremental load factor signal) and airspeed V a Airspeed V a and ground speed v g The relationship between them is:

[0036] v g cosχcosγ=v a cosψcosγ a +v w cosψ w cosγ w ;

[0037] v gsinχcosγ=v a sinψcosγ+v w sinψ w cosγ w ;

[0038] -v g sinγ=-v a sinγ a +v w sinγ w ;

[0039] Among them, v g is the ground speed, v w is the wind speed, χ is the course angle, γ a Defined as γ a =θ-α.∈ w and γ w is the wind direction of the heading angle and track angle.

[0040] Furthermore, this embodiment obtains a velocity-based quasi-linear parameter-varying model (qLPV) based on the kinematic model and integrates it into the quasi-predictive control framework. Among them, the quasi-linear parameter-varying system is a special type of nonlinear dynamic system, which is characterized by approximating or describing nonlinear behavior through parameter-dependent linear structures. For example Figure 2 In the path following control system framework shown, in the high-level loop, the path planner generates a position reference signal, and the path following controller is responsible for generating position control commands to the low-level controller to ensure that the unmanned vehicle accurately tracks the path generated in the path planner module.

[0041] 102. Establishing a quasi-linear parameter change model of the unmanned driving device based on the motion state described by the kinematic model;

[0042] 103. Expand the quasi-linear parameter change model to obtain a quasi-linear parameter change expansion equation;

[0043] In this embodiment, after obtaining the kinematic model of the unmanned vehicle, a quasi-linear parameter variation model of the unmanned vehicle can be established based on the motion state described by the kinematic model. Specifically, the computer device can establish a nonlinear system model of the unmanned vehicle based on the system state, control inputs and outputs, and a scheduling parameter vector and a continuous function of the scheduling parameter vector described by the kinematic model. This nonlinear system model can then be rewritten into a quasi-linear parameter variation model based on velocity linearization.

[0044] Furthermore, when expanding the quasi-linear parameter change model, the state quantity of the quasi-linear parameter change model can be determined according to the kinematic model of the unmanned driving equipment, and the quasi-linear parameter change model can be discretized according to the state quantity to generate a discrete quasi-linear parameter change model, an error vector is generated according to the discrete quasi-linear parameter change model, and the quasi-linear parameter change model is expanded based on the error vector to obtain the quasi-linear parameter change expansion equation.

[0045] Since the quasi-linear parameter change system model needs to be updated according to the scheduling parameters or state changes at each sampling time, this is conducive to real-time updating of the impact of external disturbances on the system. Therefore, the predictive control designed based on the quasi-linear parameter change system is robust to external interference and has good control effect.

[0046] For example, the linear parameter-varying (LPV) model has a state-space representation, and its model expression can be shown as follows:

[0047]

[0048] y(t)=C(ρ(t))x(t)+D(ρ(t))u(t);

[0049] Where x(t) represents the system state, u(t) represents the control input, and y(t) represents the control output. n , u∈R m , y∈R l n, m and l represent the dimensions of the system state, control input and output respectively. The matrices A(.), B(.), C(.) and D(.) are continuous functions of the scheduling parameter vector ρ(t) and assume that where n ρ is the number of scheduling parameters, is a given compact set.

[0050] The above LPV system is linear in x and u and depends on a time-varying parameter ρ. In the qLPV model, the parameter vector ρ is a function of the system state and / or input, enabling accurate representation of nonlinear systems.

[0051] Continuing with the example described in step 101, consider the nonlinear system model of the drone position as follows:

[0052]

[0053] y=h(x,u).

[0054] Based on the above nonlinear system model rewritten into the qLPV model based on velocity linearization, we can obtain:

[0055]

[0056] in is the Jacobian of f(x,u) with respect to x, is the Jacobian of f, and is the Jacobian of h(x,u) with respect to u.

[0057] Combined with the kinematic equation of the UAV, the state quantity of its qLPV model can be written as:

[0058]

[0059] Then, the qLPV model can be discretized according to the state quantity to obtain a discrete qLPV system model, which can be specifically expressed as follows:

[0060]

[0061] It should be noted that both the system and the input matrix depend on ρ, where ρ = [χγV a φn z ] T .

[0062] Discretizing the above model yields:

[0063]

[0064] The model uses the input rate as the control input. Therefore, a model predictive control (MPC) algorithm with an integral action can be used to solve the above equation based on the input increment. Model predictive control (MPC) is a model-based optimization control algorithm. Its core concept is to predict future behavior based on the current state and system model, predict the system state changes within a period of time; optimize the control sequence, and select an optimal control sequence within the prediction range through mathematical optimization to minimize a certain performance indicator (such as energy consumption or tracking error); and perform rolling execution, executing only the first control action and repeating the above process after updating the state.

[0065] In order to achieve effective path following in three dimensions, this embodiment constructs a suitable cost function to minimize the following error. Dynamically enhancing the cost function with the following error is an effective method to solve the underlying control problem because it allows the tracking problem to be converted into a set point problem, thereby simplifying the solution process, while still allowing the system state to ensure good performance and smooth flight.

[0066] One way to derive the error dynamics is to expand the qLPV model to the following equation:

[0067]

[0068] where e = [x r -x p y r -y p z r -z p ψ r -ψ m γ r -γ m ] T is the error vector.

[0069] Therefore, by constructing a quasi-linear parameter system model in linear or semi-linear form based on the nonlinear kinematic system model of the drone, the nonlinear characteristics of the system can be captured and accurately approximated. Velocity-based linearization is a strategy that accurately represents the nonlinear dynamics of the system in linear form, significantly improving the accuracy of the system model compared to linear systems. Because the linear form is retained, the algorithm is significantly faster than control algorithms for nonlinear systems.

[0070] 104. Generate a cost function for path following according to the quasi-linear parameter variation expansion equation, and add constraints to the cost function;

[0071] After expanding the quasi-linear parameter variation model to obtain an expanded quasi-linear parameter variation equation, the computer device may further generate a path-following cost function based on the expanded quasi-linear parameter variation equation and add constraints to the cost function. Specifically, constraints may be applied to the control input rate, system state, and output in the cost function based on the product of a preset matrix and the decision variable and the constraint vector to generate a quadratic programming equation. This quadratic programming equation is then solved to obtain a prediction equation for the control input.

[0072] Among them, this constraint condition is a constraint related to obstacles, so the cost function with this constraint condition is added with the parameter of obstacle, so that the path following control algorithm can calculate the control parameters according to the existence of obstacles.

[0073] For example, following the above example, after expanding the qLPV model to obtain the above equation, this embodiment can create a quadratic cost function with linear constraints based on it, which can be effectively solved as a quadratic program using a fast solver. Therefore, the cost function can be written as:

[0074]

[0075] where Q∈R n×nis a symmetric positive semidefinite matrix. The matrix T is an additional positive semidefinite matrix. The smoothness of flight is further enhanced by imposing additional constraints on the control input rate, which ensures that the control input does not change abruptly.

[0076] The commonly used representation of the constraints in the control input is as follows:

[0077]

[0078] Therefore, constraints on the control input rate, state, and output can also be incorporated into the optimization problem for effective management. The specific expression for adding constraints can be shown as follows:

[0079]

[0080] where A∈R q×p is a matrix multiplied by the decision variable U k , b∈R q is the constraint vector.

[0081] Then, constraints can be imposed on the control input rate, system state, and output in the cost function based on the product of the above matrix and the decision variable and the constraint vector to generate a quadratic programming equation. The quadratic programming problem can be expressed as:

[0082]

[0083] Afterwards, the command quadprog in the MATLAB optimization toolbox can be used to solve the above quadratic programming equation, and the prediction equation can be output as:

[0084]

[0085] Among them, Y k ∈R Nl is the vector of predicted output,

[0086] Therefore, by expanding and rewriting the qLPV model to generate quadratic programming equations and prediction equations, a convenient means can be provided for solving the qLPV model, which helps to efficiently solve the control input of path following control and improve the efficiency of path following control.

[0087] 105. Solve the cost function with the constraint condition added to obtain control input data of the unmanned driving device, where the control input data is used to perform path following control on the unmanned driving device;

[0088] After adding constraints to the cost function, the constrained cost function can be solved to obtain control input data for the unmanned vehicle. This control input data can be used to perform path-following control of the unmanned vehicle. Specifically, the computer device can substitute the above-mentioned prediction equation into the cost function to obtain a cost function solution equation, and rewrite the cost function solution equation to obtain a cost function rewrite equation corresponding to the cost function. This cost function rewrite equation can then be solved to obtain the control input data for the unmanned vehicle.

[0089] Among them, when solving the cost function rewriting equation, a constraint matrix and a constraint vector can be constructed to represent the constraint conditions of the control input and output based on the constraint matrix and the constraint vector, and the constraint matrix and the constraint vector are substituted into the cost function rewriting equation, and the cost function is solved based on the quadratic programming solver to obtain the control input data of the unmanned driving equipment.

[0090] Therefore, for example, for drones, this embodiment only needs to add obstacle-related constraints to the constraint condition part to directly achieve drone obstacle avoidance. At the same time, the path following control system also has stability assurance.

[0091] Continuing with the above example, the cost function can be written as:

[0092]

[0093] where R k Represents the reference position value and should not be confused with the symbol R, which is the weight matrix. Substituting the above prediction equation into the cost function of the above formula, the cost function solution equation is obtained, which is expressed as follows:

[0094]

[0095] Then the cost function solution equation can be rewritten as:

[0096]

[0097] The next step is to incorporate constraints into the optimization problem. Considering that the system has input and output constraints, we can construct a constraint matrix A and a constraint vector b to represent the required constraints and determine the necessary input, output, and state constraints. The constraint matrix A and constraint vector b can be constructed as:

[0098]

[0099] in Therefore, we can get:

[0100]

[0101] Therefore, the cost function of the path following problem can be effectively solved using a quadratic programming solver.

[0102] In this embodiment, the stability proof scheme of the quasi-model predictive control is to determine a terminal set, and in this terminal set, the quasi-model predictive control guides the state into the terminal state. Then, when the terminal conditions are met, a feedback controller that ensures stability can be designed.

[0103] Specifically, in practical applications, this embodiment can be performed on a computer equipped with an octa-core processor running at 3.6 GHz, and the quadprog solver in MATLAB Optimization Toolbox is used to solve the algorithm. w The range is 1m / s to 5m / s, and the direction is χ w =335 degrees of time-varying wind as the interference signal, the simulation results show that Figure 3 and Figure 4 .in, Figure 3 The path following control effect of the UAV is described; Figure 4 This figure depicts the variables associated with the drone's path following. The first five figures are state tracking diagrams, and the sixth represents the additional variables designed for the reference trajectory design. The red line represents the reference trajectory, and the black line represents the actual tracking trajectory. The simulation results demonstrate the effectiveness of this embodiment's path following control method.

[0104] Linear time-invariant system modeling can lead to model distortion and control failure; control algorithms for nonlinear systems are often slow, making real-time control impossible. However, in this embodiment, qLPV system modeling overcomes the limitations of low linear system modeling accuracy and slow nonlinear predictive control algorithms. It accurately describes the dynamic characteristics of nonlinear systems while preserving their linear or semi-linear form. This allows for fast algorithm solvers to be used, enabling the design of fast control algorithms and achieving real-time control.

[0105] As can be seen from the above, for example, for the path following control of a fixed-wing UAV system subject to wind disturbances, this embodiment proposes an effective modeling scheme based on a quasi-linear parameter variation system. This model accurately describes the real-time changes in nonlinear systems and the effects of wind disturbances. The designed predictive controller also effectively compensates for wind disturbances, effectively controlling the UAV's path following in real time. As a result, the UAV can automatically output a path to follow based on the planned points and achieve real-time path following, improving the performance of the control system and effectively completing space missions.

[0106] The computer device in the embodiment of the present application is described below. Figure 5 In one embodiment of the present application, a computer device includes:

[0107] The computer device 500 may include one or more central processing units (CPUs) 501 and a memory 505 , wherein the memory 505 stores one or more application programs or data.

[0108] Memory 505 may be volatile or persistent storage. The program stored in memory 505 may include one or more modules, each of which may include a series of instruction operations on the computer device. Furthermore, CPU 501 may be configured to communicate with memory 505 and execute the series of instruction operations in memory 505 on computer device 500.

[0109] The computer device 500 may also include one or more power supplies 502, one or more wired or wireless network interfaces 503, one or more input and output interfaces 504, and / or one or more operating systems, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.

[0110] The CPU 501 can execute the aforementioned Figure 1 The operations performed by the computer device in the illustrated embodiment will not be described in detail here.

[0111] The present application also provides a computer storage medium, wherein one embodiment includes: the computer storage medium stores instructions, and when the instructions are executed on a computer, the computer executes the aforementioned Figure 1 The operations performed by the computer device in the illustrated embodiment.

[0112] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0113] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0114] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0115] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0116] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, read-only memory), random access memory (RAM, random access memory), disk or optical disk, and other media that can store program code.

Claims

1. A path following control method based on a quasi-linear parameter variation model, characterized in that: The method comprises: Constructing a kinematic model of the unmanned driving device, wherein the kinematic model is used to describe the motion state of the unmanned driving device in space and time; Establishing a quasi-linear parameter change model of the unmanned driving device based on the motion state described by the kinematic model; Expanding the quasi-linear parameter change model to obtain a quasi-linear parameter change expansion equation; Generating a cost function for path following according to the quasi-linear parameter variation expansion equation, and adding constraints to the cost function; the constraints are obstacle-related constraints; The cost function with the added constraint condition is solved to obtain control input data of the unmanned driving device, and the control input data is used to perform path following control on the unmanned driving device, so that the unmanned driving device can achieve autonomous obstacle avoidance and the path following control algorithm has stability assurance.

2. The method according to claim 1, characterized in that The kinematic model of the unmanned driving device is constructed, including: The kinematic model of the unmanned device is constructed based on one or more of the position state information, aerodynamic heading angle, flight track angle, wind speed, bank angle, incremental load factor signal and airspeed of the unmanned device.

3. The method according to claim 1, characterized in that The step of establishing a quasi-linear parameter change model of the unmanned driving device based on the motion state described by the kinematic model includes: Establishing a nonlinear system model of the unmanned driving device based on the system state, control input and output, scheduling parameter vector, and a continuous function of the scheduling parameter vector described by the kinematic model; The nonlinear system model is rewritten into the quasi-linear parameter variation model based on speed linearization.

4. The method according to claim 1, wherein The quasi-linear parameter change model is expanded to obtain a quasi-linear parameter change expansion equation, including: Determining a state quantity of the quasi-linear parameter change model according to the kinematic model of the unmanned driving device, and discretizing the quasi-linear parameter change model according to the state quantity to generate a discrete quasi-linear parameter change model; An error vector is generated according to the discrete quasi-linear parameter change model, and the quasi-linear parameter change model is expanded based on the error vector to obtain the quasi-linear parameter change expansion equation.

5. The method according to claim 1, wherein Adding constraints to the cost function includes: Applying constraints on the control input rate, system state, and output in the cost function according to the product of a preset matrix and the decision variable and a constraint vector to generate a quadratic programming equation; The quadratic programming equation is solved to obtain a prediction equation for the control input.

6. The method according to claim 5, characterized in that Solving the cost function with the constraint condition added thereto to obtain control input data for the unmanned driving device includes: Substituting the prediction equation into the cost function to obtain a cost function solution equation, and rewriting the cost function solution equation to obtain a cost function rewritten equation corresponding to the cost function; The cost function is rewritten into an equation and solved to obtain control input data of the unmanned driving device.

7. The method according to claim 6, characterized in that Solving the cost function rewriting equation to obtain control input data for the unmanned driving device includes: constructing a constraint matrix and a constraint vector to express constraint conditions for control input and output based on the constraint matrix and the constraint vector; Substituting the constraint matrix and the constraint vector into the cost function to rewrite the equation, and solving the cost function based on a quadratic programming solver to obtain control input data of the unmanned driving device.

8. The method according to any one of claims 1 to 7, characterized in that The unmanned driving equipment includes drones, unmanned vehicles and other equipment with autonomous movement capabilities.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 8 is implemented.

10. A computer storage medium, characterized in that The computer storage medium stores instructions, which, when executed on a computer, enable the computer to perform the method according to any one of claims 1 to 8.

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