Unmanned aerial vehicle trajectory tracking control method and system based on adaptive dynamic programming

Abstract

The invention relates to the technical field of unmanned aerial vehicle flight control, and provides an unmanned aerial vehicle trajectory tracking control method and system based on self-adaptive dynamic programming, and the method comprises the steps: obtaining a position subsystem control input through a control law of a position subsystem based on a position loop expected value, an external interference estimated value and a position tracking error, and carrying out the control of the position subsystem; a position coordinate vector and a first-order derivative thereof are calculated by adopting a position subsystem so as to update a position tracking error, and an external interference estimated value is updated through an interference observer; based on an Euler angle vector reference signal, feedforward control input is obtained through calculation, attitude subsystem control input is obtained through calculation in combination with optimal feedback control input, an Euler angle vector and a first derivative of the Euler angle vector are calculated through an attitude subsystem, an optimal feedback controller is designed for a nominal error system by adopting a self-adaptive dynamic programming method, and the optimal feedback controller is used for controlling the nominal error system. And updating the optimal feedback control input. And the unmanned aerial vehicle system is ensured to have the optimal tracking performance while having relatively high robustness in finite time.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicle flight control, and in particular relates to a unmanned aerial vehicle trajectory tracking control method and system based on adaptive dynamic programming. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] At present, quad-rotor drones have been widely used in many fields due to their small size, simple operation and flexible flight, such as aerial reconnaissance, earthquake relief, line inspection and drone performances. However, due to the complex low-altitude flight environment, the design of the flight control system of quad-rotor drones will face many difficult problems. First, quad-rotor drones are inevitably affected by many external disturbances represented by wind interference during flight, which puts higher requirements on their robustness. Secondly, in the drone control system, the signal transmission of various sensors will inevitably produce input delays, and due to the limitations of the physical structure, the actuator may show certain saturation characteristics. The existence of time lag and saturation will cause the control signal to be unable to be transmitted to the actuator in time, resulting in a decrease in the controller's efficiency. In addition, in order to better reduce the control energy consumption and improve the endurance of the drone, it is necessary to introduce the idea of ​​optimal control into the design of the flight controller, while ensuring fast and stable control, it can also meet specific performance index requirements, thereby improving the controller's efficiency.

[0004] In the existing control scheme design for quadrotor UAV systems, such as publication number CN116225043 A, patent "A method for controlling the predetermined performance of a quadrotor UAV based on a disturbance observer"; publication number CN111679684A, patent "A method for controlling the backstepping control of a quadrotor UAV with input time lag"; publication number CN116954240A, patent "A method for tracking the position and attitude of a quadrotor UAV with resistance to saturation and interference" and publication number CN117908576A, patent "A method for controlling the finite-time fault-tolerant control of a quadrotor UAV with resistance to saturation" and other literatures, most of them only consider the flight control problem of the quadrotor UAV under some of the constraints such as external disturbance, input time lag and input saturation, or consider the finite-time fault-tolerant flight control problem under the saturated input situation. However, there is no research on the problem of robust optimal trajectory tracking control in the finite time domain under the constraints of external disturbance, input time lag and input saturation. Moreover, the robustness of the current quadrotor UAV control system is poor. Summary of the invention

[0005] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a UAV trajectory tracking control method and system based on adaptive dynamic programming, which converts the tracking control problem of the original system into a stabilization problem of a nominal error system, and adopts an adaptive dynamic programming method with a single evaluation neural network structure to design an optimal feedback controller for the error system, thereby ensuring that the UAV system has strong robustness within a limited time and also has optimal tracking performance.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] A first aspect of the present invention provides a UAV trajectory tracking control method based on adaptive dynamic programming, which comprises:

[0008] Obtain the Euler angle vector reference signal and position loop expected value of the quadrotor drone system;

[0009] Based on the expected value of the position loop, the estimated value of the external disturbance and the position tracking error, after obtaining the control input of the position subsystem through the control law of the position subsystem, the position subsystem is used to calculate the position coordinate vector and its first-order derivative to update the position tracking error, and based on the first-order derivative of the position coordinate vector, the estimated value of the external disturbance is updated through the disturbance observer;

[0010] Based on the Euler angle vector reference signal, the feedforward control input is calculated, and combined with the optimal feedback control input, the attitude subsystem control input is calculated. After the Euler angle vector and its first-order derivative are calculated by the attitude subsystem, the adaptive dynamic programming method with a single evaluation neural network structure is used to design the optimal feedback controller for the nominal error system and update the optimal feedback control input.

[0011] Further, the posture subsystem is expressed as:

[0012]

[0013] Among them, f(·) is the state function vector of the quadrotor drone system, g() is the control gain function of the quadrotor drone system, Θ is the Euler angle vector, is the first derivative of the Euler angle vector, Θ = x 3 , Sat(·) is the saturation function, u Θτ =u Θ (t-τ),u Θ Control input for the attitude subsystem.

[0014] Furthermore, the adaptive dynamic programming method obtains the optimal feedback control input by solving the HJB equation, and the HJB equation is:

[0015]

[0016] Among them, δ is a definition variable, Q and R are positive definite symmetric matrices, and J * represents the optimal performance index function, F δ is the error system dynamics, G δ =-g(Θ), g(Θ) is the control gain function of the quadrotor UAV system.

[0017] Furthermore, the feedforward control input is:

[0018]

[0019] Where g is the acceleration of gravity, f(·) is the state function vector of the quadrotor drone system, Θ d is the Euler angle vector reference signal, χ is the auxiliary system variable, K 3 is the gain matrix to be designed.

[0020] A second aspect of the present invention provides a UAV trajectory tracking control system based on adaptive dynamic programming, which comprises:

[0021] The data acquisition module is configured to: obtain the Euler angle vector reference signal and position loop expected value of the quadrotor drone system;

[0022] The position control module is configured to: obtain the position subsystem control input through the control law of the position subsystem based on the position loop expected value, the external disturbance estimated value and the position tracking error, and then use the position subsystem to calculate the position coordinate vector and its first-order derivative to update the position tracking error, and update the external disturbance estimated value through the disturbance observer based on the first-order derivative of the position coordinate vector;

[0023] The attitude control module is configured as follows: based on the Euler angle vector reference signal, a feedforward control input is calculated, and after combining the optimal feedback control input, the attitude subsystem control input is calculated, and after the Euler angle vector and its first-order derivative are calculated by the attitude subsystem, an adaptive dynamic programming method with a single evaluation neural network structure is used to design an optimal feedback controller for the nominal error system and update the optimal feedback control input.

[0024] Further, the posture subsystem is expressed as:

[0025]

[0026] Among them, f(·) is the state function vector of the quadrotor drone system, g() is the control gain function of the quadrotor drone system, Θ is the Euler angle vector, is the first derivative of the Euler angle vector, Θ = x 3 , Sat(·) is the saturation function, uΘτ =u Θ (t-τ),u Θ Control input for the attitude subsystem.

[0027] Furthermore, the adaptive dynamic programming method obtains the optimal feedback control input by solving the HJB equation, and the HJB equation is:

[0028]

[0029] Among them, δ is a definition variable, Q and R are positive definite symmetric matrices, and J * represents the optimal performance index function, F δ is the error system dynamics, G δ =-g(Θ), g(Θ) is the control gain function of the quadrotor UAV system.

[0030] Furthermore, the feedforward control input is:

[0031]

[0032] Where g is the acceleration of gravity, f(·) is the state function vector of the quadrotor drone system, Θ d is the Euler angle vector reference signal, χ is the auxiliary system variable, K 3 is the gain matrix to be designed.

[0033] The third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the UAV trajectory tracking control method based on adaptive dynamic programming as described above.

[0034] The fourth aspect of the present invention provides a computer device, including a computer-readable storage medium, a processor, and a computer program stored on the computer-readable storage medium and executable on the processor, wherein when the processor executes the program, the steps in the UAV trajectory tracking control method based on adaptive dynamic programming as described above are implemented.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The present invention converts the tracking control problem of the original system into a stabilization problem of a nominal error system, and adopts an adaptive dynamic programming method of a single evaluation neural network structure to design an optimal feedback controller for the error system, thereby ensuring that the UAV system has strong robustness within a limited time and also has optimal tracking performance.

[0037] The present invention designs a finite-time domain robust optimal tracking control strategy for low-altitude flight control of a quadrotor UAV taking into account the adverse effects of external interference, input lag and input saturation of the quadrotor UAV. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0039] Figure 1 is a flow chart of a UAV trajectory tracking control method based on adaptive dynamic programming according to Embodiment 1 of the present invention;

[0040] Figure 2 This is a position trajectory tracking diagram of the quad-rotor drone according to the first embodiment of the present invention;

[0041] Figure 3 This is an attitude angle tracking diagram of a quad-rotor drone according to Embodiment 1 of the present invention;

[0042] Figure 4 is the evaluation network weight W of the first embodiment of the present invention c Convergence process diagram;

[0043] Figure 5 is a control input diagram of the attitude subsystem of the first embodiment of the present invention;

[0044] Figure 6 It is a structural diagram of a computer device according to a fourth embodiment of the present invention. DETAILED DESCRIPTION

[0045] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0046] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0047] Embodiment 1

[0048] This embodiment provides a UAV trajectory tracking control method based on adaptive dynamic programming.

[0049] In order to design a drone trajectory tracking control method that better meets the actual needs of low-altitude flight of quadcopters, the drone trajectory tracking control method based on adaptive dynamic programming provided in this embodiment comprehensively considers the influence of external interference, input lag and input saturation constraints, and designs a finite-time robust optimal trajectory controller based on an adaptive dynamic programming algorithm under the backstepping framework; first, the external interference is estimated by using the interference observer technology; then, the Padé approximation method and the auxiliary system method are used to deal with the input lag and input saturation problems in the attitude loop; subsequently, a finite-time robust optimal tracking control strategy is designed under the backstepping framework in combination with an adaptive dynamic programming algorithm with a single evaluation network structure, and the quadcopter is subjected to optimal trajectory tracking control; finally, numerical simulation is carried out based on the Matlab platform to verify the effectiveness of the designed finite-time robust optimal flight control scheme.

[0050] The UAV trajectory tracking control method based on adaptive dynamic programming provided in this embodiment is implemented using the following steps:

[0051] Step 1: Considering that the model is subject to external interference, input time lag and input saturation constraints, the position loop and attitude loop dynamics models of the quadrotor UAV system are established respectively.

[0052] In step 1, considering that the quadrotor UAV system is subject to external interference, input delay and input saturation constraints, its dynamic model is as follows:

[0053]

[0054]

[0055] Where E = [0, 0, 1] T , m is the mass of the drone, g is the acceleration due to gravity, P = [x, y, z] T and Θ=[φ,θ,ψ] T are the position coordinate vector and the Euler angle vector, respectively, where x, y, and z represent the position of the quadrotor drone in the earth coordinate system, and φ, θ, and ψ represent the roll angle, pitch angle, and yaw angle of the quadrotor drone in the earth coordinate system, respectively. is the state function vector of the quadrotor UAV system, g(Θ) is the control gain function of the quadrotor UAV system, and D P is the external interference to the drone, u P is the control input of the position subsystem, Sat(u Θ (t-τ)) is the saturated control input of the attitude subsystem with time-varying delay, τ represents the time-varying delay signal, and the saturation function Sat(·) is as follows:

[0056]

[0057] in, and are the upper and lower bounds of the control input respectively.

[0058] Step 2: The Padé approximation technique and auxiliary system method are used to deal with the input delay and input saturation problems respectively, and the external disturbance is estimated by the disturbance observer technology under the backstepping framework.

[0059] In step 2, for ease of description, P and Replace with x 1 and x 2 , then (1) can be rewritten as:

[0060]

[0061] Next, define the position tracking error as z 1 =x 1 -x r , z 2 =x 2 -x 2r , where x r is the expected value of the position loop, x 2r It is the virtual control signal of the position loop.

[0062] In order to compensate for unknown external disturbances, the control law of the position subsystem is designed as:

[0063]

[0064] Among them, K 1 is the positive definite matrix to be designed; D p The estimated value of can be obtained by designing a disturbance observer of the following form:

[0065]

[0066] Among them, Γ is the auxiliary vector, K 2 is the gain matrix to be designed.

[0067] Next, we transform Θ and Replace with x 3 and x 4 , we can get the attitude subsystem:

[0068]

[0069] Among them, u Θτ =u Θ (t-τ).

[0070] In order to effectively handle the saturation problem of the control input of the attitude subsystem, the following auxiliary system is adopted:

[0071]

[0072] Among them, χ is the auxiliary system variable, K 3 is the gain matrix to be designed, H(t) is the intermediate variable to be designed, Δu Θτ is the actual input of the auxiliary system.

[0073] Define a new variable δ = x 4 -x d -χ, and taking its derivative we get:

[0074]

[0075] Among them, x d Represents the reference signal.

[0076] In order to solve the input delay problem in the quadrotor drone system, the Padé approximation technique is used as follows:

[0077]

[0078] Where l{·} is the Laplace transform and s is the Laplace variable.

[0079] We can further obtain 4l{u Θ (t)}=2l{H(t)}+τsl{H(t)}, and then using the inverse Laplace transform, we can get Where λ = 2 / τ.

[0080] And u Θτ It can be expressed as u Θτ =H(t)-u Θ (t), therefore, formula (9) can be rewritten as:

[0081]

[0082] The attitude subsystem control input u in the present invention Θ It consists of two parts, namely:

[0083] u Θ =u f +u o (12)

[0084] Among them, u f is the feedforward control input, u o For the optimal feedback control input, the feedforward control input uf is designed as follows:

[0085]

[0086] Among them, Θ d is the Euler angle vector reference signal, is the first-order derivative of the reference signal.

[0087] Step 3: Design a finite-time optimal robust trajectory tracking controller based on a single-evaluation network adaptive dynamic programming algorithm.

[0088] In step 3, the optimal feedback controller u is designed for the following nominal error system o :

[0089]

[0090] in, is the error system dynamics, G δ =-g(Θ).

[0091] First, define the finite time domain performance index function as follows:

[0092]

[0093] Among them, t f represents the terminal moment, and is a positive definite symmetric matrix, J(δ(t f ), t f )=Ξ(δ(t f ), t f )≥0 is a terminal constraint.

[0094] The corresponding form of the Hamiltonian equation is:

[0095]

[0096] in,

[0097] Combined with the conditions for the existence of the optimal solution The ideal optimal feedback control input can be obtained:

[0098]

[0099] in, represents the optimal form of the performance index function, R is a real symmetric positive definite matrix, that is, R = R T >0.

[0100] Substituting the above equation into the Hamiltonian equation and setting the equation equal to zero, we get the following HJB equation:

[0101]

[0102] Among them, J * Represents the optimal performance index function J * (δ), It represents the first-order derivative of the optimal performance index function with respect to time t, that is, It represents the first-order derivative of the optimal performance index function with respect to the variable δ.

[0103] The HJB equations are then solved using an adaptive dynamic programming algorithm to obtain the optimal feedback control input The approximate optimal solution of There exists a function of the error variable ρ(δ)>0, satisfying holds; at the same time, there exists a bounded positive definite matrix Λ(δ) such that the equation Established, among which J 1 (δ) is the Lyapunov function with respect to the error variable, and

[0104] First, a single-evaluation network is constructed to approximate J * (δ), we have:

[0105]

[0106] in, is the weight vector to be obtained, is the time-varying neural network activation function, N is the number of hidden nodes in the neural network, ε c is the estimation error.

[0107] In addition, the performance indicator function at the terminal moment is:

[0108]

[0109] Taking partial derivatives of equation (20) with respect to δ and t, we can obtain:

[0110]

[0111]

[0112] Assume ε c and W c And the first-order derivative and Satisfies the bounded condition, that is, there is a positive constant and Make

[0113] Next will and Substitute into the HJB equation and the optimal feedback control input In, we can get:

[0114]

[0115]

[0116] in, And there exists a positive constant Make ε H represents a bounded residual value and

[0117] assumed W c The estimated value of and H(δ,W c ) can be expressed as:

[0118]

[0119]

[0120] Among them, e h is the corresponding estimation error. In addition, the estimation error at the terminal moment is:

[0121]

[0122] In order to better train the Define the objective function And minimize e H The adaptive update law designed for the target is as follows:

[0123]

[0124] in, α 1 ,α 2 >0 indicates the learning rate; conditional function The specific form is as follows:

[0125] Step 4: Analyze the stability conditions of the closed-loop system using the Lyapunov stability method.

[0126] In step 4, for the quadrotor UAV system with external disturbance, input time lag and input saturation constraints (Formula (1) and Formula (2)), a robust position tracking controller as shown in Formula (5) and a finite time domain robust optimal attitude tracking controller as shown in Formula (17) are designed; at the same time, a disturbance observer as shown in Formula (6) is designed to estimate the external disturbance, and an adaptive update law as shown in Formula (28) is designed to estimate the optimal weight. By selecting appropriate parameters, it can be ensured that the state tracking error, disturbance estimation error and neural network weight estimation error in the entire closed-loop system are consistent and ultimately bounded.

[0127] Choose the following Lyapunov function:

[0128]

[0129] Where V δ =1 / 2δ T δ, taking the first-order derivative of V, we get

[0130]

[0131] in,

[0132] when When, meet Assuming the δ constant excitation condition, there exists a positive constant Make Select At this time, choose appropriate parameters so that λ min (K 3 )>1, and the following inequality holds:

[0133] in, It can guarantee

[0134] when When considering Conditions, choose appropriate parameters so that λ min (K 3 )>1, and the following inequality holds: It can guarantee

[0135] According to Lyapunov's expansion theorem, for and In both cases, the error variable in the closed-loop system is guaranteed to be consistently and ultimately bounded.

[0136] Step 5: Substitute the control algorithm into the simulation model for testing to verify that the control algorithm can be used to control the quadrotor UAV system and has good results. Figure 1 As shown, specifically including:

[0137] Step 501: Obtain the reference signal at the current time t, including the Euler angle vector reference signal θ d and the expected value x of the position loop r ;

[0138] Step 502: Based on the expected value x of the position loop r and the estimated value of external interference to the drone Position tracking error z 1 and z 2 , through the control law of the position subsystem (Formula (5)), we get the control input u of the position subsystem P ; Based on the position subsystem control input u P , through the position subsystem (Formula (4)), we get the position coordinate vector P (i.e., x 1 ) and its first-order derivative (i.e., x 2 ), and then update the position tracking error z 1 and z 2 ; Based on the first derivative of the position coordinate vector (i.e., x 2 ), and update the estimated value of external interference to the UAV through the interference observer (Formula (6))

[0139] Step 503: Based on the Euler angle vector reference signal θ d , through formula (13), the feedforward control input u is calculated f ; Based on the feedforward control input u f , combined with the optimal feedback control input u o , calculate the attitude subsystem control input u Θ ; Based on the attitude subsystem control input u Θ , through the attitude subsystem (Formula (7)), calculate the Euler angle vector Θ (i.e., x 3 ) and its first-order derivative (i.e., x 4 ); based on the Euler angle vector Θ (i.e., x 3 ) and its first-order derivative (i.e., x 4 ), and the variable δ is calculated by formula (11); based on the variable δ, the optimal feedback control input u is updated by formula (17) o (Right now, );

[0140] Step 504, return to step 502.

[0141] In step 5, the relevant simulation platform is built with the help of Matlab / Simulink, and the control algorithm is substituted into the quadrotor UAV model for simulation testing to verify that it can be used for the finite time domain control of the quadrotor UAV system under external interference, input time lag and input saturation constraints, and has good stability and robustness.

[0142] The method of the present invention is simulated and analyzed below. The optimal feedback control input is solved by writing a Matlab program, and it is verified that the control algorithm can be used in the finite time domain robust optimal trajectory tracking system of a quadrotor drone under external disturbance, input time lag and input saturation constraints, and has a good tracking effect.

[0143] First, the model parameters of the quadcopter used in the simulation are m=1.2 (kg), g=9.8 (m / s 2 ). Secondly, the design parameters of the controller and disturbance observer are selected as: K1 = diag{5; 7.5; 10}, K 2 =diag{10;15;20},K 3 =diag{15;10;5}, input delay time τ=0.1sin(t)+0.2s, input saturation upper and lower bounds are The corresponding optimal feedback controller parameters are set as follows: α 1 =0.5,α 2 =1, Q=diag{10;5;5}, R=diag{5;10;15}. D p =[0.5sin(0.5t), 0.1sin(0.5t), 0.1sin(0.5t)] T (N) is the selected disturbance force, P r =[0.5sin(0.5t), 0.5sin(0.6t), 0.1t] T (m), ψ r =π12(deg) is the desired position signal and yaw angle signal. In addition, the excitation function is selected as:

[0144]

[0145] in, t f =15s. Finally, through Simulink simulation analysis, the results are as follows Figure 2-Figure 5 shown.

[0146] Figure 2 and Figure 3They are respectively the position and attitude angle trajectory tracking processes of the quadrotor drone. It can be seen that the control method proposed in the present invention has a faster convergence speed and higher tracking accuracy. Figure 4 This is the convergence of the weights of the neural network. It can be seen that the weights gradually converge to a stable value. Figure 5 is the control input of the attitude subsystem, and it can be seen that the input torque is within a reasonable range. The above simulation results show that the finite time domain robust optimal trajectory tracking control strategy proposed in this invention can be effectively applied to the flight control problem of a quadrotor UAV under the constraints of external interference, input time lag and input saturation.

[0147] Embodiment 2

[0148] This embodiment provides a UAV trajectory tracking control system based on adaptive dynamic programming, which specifically includes:

[0149] The data acquisition module is configured to: obtain the Euler angle vector reference signal and position loop expected value of the quadrotor drone system;

[0150] The position control module is configured to: obtain the position subsystem control input through the control law of the position subsystem based on the position loop expected value, the external disturbance estimated value and the position tracking error, and then use the position subsystem to calculate the position coordinate vector and its first-order derivative to update the position tracking error, and update the external disturbance estimated value through the disturbance observer based on the first-order derivative of the position coordinate vector;

[0151] The attitude control module is configured as follows: based on the Euler angle vector reference signal, a feedforward control input is calculated, and after combining the optimal feedback control input, the attitude subsystem control input is calculated, and after the Euler angle vector and its first-order derivative are calculated by the attitude subsystem, an adaptive dynamic programming method with a single evaluation neural network structure is used to design an optimal feedback controller for the nominal error system and update the optimal feedback control input.

[0152] Among them, the attitude subsystem is expressed as:

[0153]

[0154] Among them, f(·) is the state function vector of the quadrotor drone system, g() is the control gain function of the quadrotor drone system, Θ is the Euler angle vector, is the first derivative of the Euler angle vector, Θ = x 3 , Sat(·) is the saturation function, u Θτ =u Θ (t-τ),u Θ Control input for the attitude subsystem.

[0155] The adaptive dynamic programming method obtains the optimal feedback control input by solving the HJB equation, which is:

[0156]

[0157] Among them, δ is a definition variable, Q and R are positive definite symmetric matrices, and J * represents the optimal performance index function, F δ is the error system dynamics, G δ =-g(Θ), g(Θ) is the control gain function of the quadrotor UAV system.

[0158] Among them, the feedforward control input is:

[0159]

[0160] Where g is the acceleration of gravity, f(·) is the state function vector of the quadrotor drone system, Θ d is the Euler angle vector reference signal, χ is the auxiliary system variable, K 3 is the gain matrix to be designed.

[0161] It should be noted here that each module in this embodiment corresponds to each step in Example 1 one by one, and the specific implementation process is the same, which will not be repeated here.

[0162] Embodiment 3

[0163] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps in the UAV trajectory tracking control method based on adaptive dynamic programming as described in the above-mentioned embodiment 1 are implemented.

[0164] Embodiment 4

[0165] This embodiment provides a computer device, such as Figure 6 As shown, it includes a display device, an input device, a computer-readable storage medium (volatile memory and non-volatile storage medium), a processor, a communication interface (i.e., a network interface), and a computer program stored on the computer-readable storage medium and executable on the processor, wherein the processor, the communication interface, and the computer-readable storage medium may be connected via a bus or other means. The communication interface is used to receive and send data, and when the processor executes the program, the steps in the UAV trajectory tracking control method based on adaptive dynamic programming as described in the first embodiment above are implemented.

[0166] Among them, any reference to memory, storage, database or other media provided by the present application and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0167] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (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 the 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.

[0168] 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.

[0169] 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. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0170] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. The UAV trajectory tracking control method based on adaptive dynamic programming is characterized by: include: Obtain the Euler angle vector reference signal and position loop expected value of the quadrotor drone system; Based on the expected value of the position loop, the estimated value of the external disturbance and the position tracking error, after obtaining the control input of the position subsystem through the control law of the position subsystem, the position subsystem is used to calculate the position coordinate vector and its first-order derivative to update the position tracking error, and based on the first-order derivative of the position coordinate vector, the estimated value of the external disturbance is updated through the disturbance observer; Based on the Euler angle vector reference signal, the feedforward control input is calculated, and combined with the optimal feedback control input, the attitude subsystem control input is calculated. After the Euler angle vector and its first-order derivative are calculated by the attitude subsystem, the adaptive dynamic programming method with a single evaluation neural network structure is used to design the optimal feedback controller for the nominal error system and update the optimal feedback control input.

2. The UAV trajectory tracking control method based on adaptive dynamic programming according to claim 1, characterized in that: The attitude subsystem is expressed as: Among them, f(·) is the state function vector of the quadrotor drone system, g() is the control gain function of the quadrotor drone system, Θ is the Euler angle vector, is the first derivative of the Euler angle vector, Θ = x3, Sat(·) is the saturation function, u Θτ =u Θ (t-τ),u Θ Control input for the attitude subsystem.

3. The UAV trajectory tracking control method based on adaptive dynamic programming according to claim 1, characterized in that: The adaptive dynamic programming method obtains the optimal feedback control input by solving the HJB equation, which is: Among them, δ is a definition variable, Q and R are positive definite symmetric matrices, and J * represents the optimal performance index function, F δ is the error system dynamics, G δ =-g(Θ), g(Θ) is the control gain function of the quadrotor UAV system.

4. The UAV trajectory tracking control method based on adaptive dynamic programming according to claim 1, characterized in that: The feedforward control input is: Where g is the acceleration of gravity, f(·) is the state function vector of the quadrotor drone system, Θ d is the Euler angle vector reference signal, χ is the auxiliary system variable, and K3 is the gain matrix to be designed.

5. The UAV trajectory tracking control system based on adaptive dynamic programming is characterized by: include: The data acquisition module is configured to: obtain the Euler angle vector reference signal and position loop expected value of the quadrotor drone system; The position control module is configured to: obtain the position subsystem control input through the control law of the position subsystem based on the position loop expected value, the external disturbance estimated value and the position tracking error, and then use the position subsystem to calculate the position coordinate vector and its first-order derivative to update the position tracking error, and update the external disturbance estimated value through the disturbance observer based on the first-order derivative of the position coordinate vector; The attitude control module is configured as follows: based on the Euler angle vector reference signal, a feedforward control input is calculated, and after combining the optimal feedback control input, the attitude subsystem control input is calculated, and after the Euler angle vector and its first-order derivative are calculated by the attitude subsystem, an adaptive dynamic programming method with a single evaluation neural network structure is used to design an optimal feedback controller for the nominal error system and update the optimal feedback control input.

6. The UAV trajectory tracking control system based on adaptive dynamic programming as claimed in claim 5, characterized in that: The attitude subsystem is expressed as: Where f(·) is the state function vector of the quadrotor drone system, g(·) is the control gain function of the quadrotor drone system, Θ is the Euler angle vector, is the first derivative of the Euler angle vector, Θ = x3, Sat(·) is the saturation function, u Θτ =u Θ (t-τ),u Θ Control input for the attitude subsystem.

7. The UAV trajectory tracking control system based on adaptive dynamic programming as claimed in claim 5, characterized in that: The adaptive dynamic programming method obtains the optimal feedback control input by solving the HJB equation, which is: Among them, δ is a definition variable, Q and R are positive definite symmetric matrices, and J * represents the optimal performance index function, F δ is the error system dynamics, G δ =-g(Θ), g(Θ) is the control gain function of the quadrotor UAV system.

8. The UAV trajectory tracking control system based on adaptive dynamic programming as claimed in claim 5, characterized in that: The feedforward control input is: Where g is the acceleration of gravity, f(·) is the state function vector of the quadrotor drone system, Θ d is the Euler angle vector reference signal, χ is the auxiliary system variable, and K3 is the gain matrix to be designed.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in the UAV trajectory tracking control method based on adaptive dynamic programming as described in any one of claims 1 to 4 are implemented.

10. A computer device comprising a computer-readable storage medium, a processor, and a computer program stored in the computer-readable storage medium and executable on the processor, characterized in that: When the processor executes the program, the steps in the UAV trajectory tracking control method based on adaptive dynamic programming as described in any one of claims 1-4 are implemented.

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