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

Abstract

This invention relates to the field of unmanned aerial vehicle (UAV) flight control technology, and provides a UAV trajectory tracking control method and system based on adaptive dynamic programming. The method includes: based on the expected value of the position loop, the estimated value of external disturbances, and the position tracking error, after obtaining the control input of the position subsystem through the control law of the position subsystem, the position coordinate vector and its first derivative are calculated using the position subsystem to update the position tracking error; and the estimated value of external disturbances is updated using a 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 calculating the Euler angle vector and its first derivative through the attitude subsystem, an adaptive dynamic programming method is used to design the optimal feedback controller for the nominal error system and update the optimal feedback control input. This ensures that the UAV system has strong robustness and optimal tracking performance within a finite time.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) flight control technology, and particularly relates to a UAV trajectory tracking control method and system based on adaptive dynamic programming. Background Technology

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

[0003] Currently, quadcopter drones are widely used in many fields due to their small size, simple operation, and flexible flight, such as aerial reconnaissance, earthquake relief, power line inspection, and drone performances. However, due to the complex low-altitude flight environment, the design of quadcopter drone flight control systems faces many challenges. First, quadcopter drones inevitably face various external disturbances during flight, such as wind interference, which places higher demands on their robustness. Second, in the drone control system, the signal transmission of various sensors will inevitably generate input delays, and due to physical limitations, actuators may exhibit certain saturation characteristics. The existence of time delays and saturation will prevent control signals from being transmitted to the actuators in a timely manner, leading to a reduction in controller efficiency. Furthermore, in order to better reduce control energy consumption and improve the drone's endurance, it is necessary to introduce the concept of optimal control into the flight controller design, ensuring fast and stable control while meeting specific performance requirements, thereby improving controller efficiency.

[0004] Existing control schemes for quadrotor UAV systems, such as patents CN116225043 A ("A Quadrotor UAV Predetermined Performance Control Method Based on Interference Observer"), CN111679684A ("A Quadrotor UAV Backstepping Control Method with Input Delay"), CN116954240A ("A Quadrotor UAV Position and Attitude Tracking Control Method Against Saturation and Interference"), and CN117908576A ("A Saturation-Resistant Finite-Time Fault-Tolerant Control Method for Quadrotor UAV"), mostly only consider the quadrotor UAV flight control problem under partial constraints such as external interference, input delay, and input saturation, or the finite-time fault-tolerant flight control problem under saturated input conditions. However, there is no research on the finite-time robust optimal trajectory tracking control problem under the constraints of external interference, input delay, and input saturation. Moreover, the current quadrotor UAV control systems have poor robustness. Summary of the Invention

[0005] To address the technical problems mentioned above, this invention provides a UAV trajectory tracking control method and system based on adaptive dynamic programming. The tracking control problem of the original system is transformed into a stabilization problem of a nominal error system. An adaptive dynamic programming method with a single-evaluation neural network structure is used to design an optimal feedback controller for the error system, ensuring that the UAV system has strong robustness and optimal tracking performance within a finite time.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

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

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

[0009] Based on the expected value of the position loop, the estimated value of external disturbance, and the position tracking error, the control input of the position subsystem is obtained through the control law of the position subsystem. The position coordinate vector and its first derivative are calculated by the position subsystem to update the position tracking error. Based on the first derivative of the position coordinate vector, the estimated value of external disturbance is updated through the disturbance observer.

[0010] Based on the Euler angle vector reference signal, the feedforward control input is calculated. Combined with the optimal feedback control input, the attitude subsystem control input is calculated. After calculating the Euler angle vector and its first derivative through the attitude subsystem, the optimal feedback controller is designed for the nominal error system using an adaptive dynamic programming method with a single evaluation neural network structure, and the optimal feedback control input is updated.

[0011] Furthermore, the attitude subsystem is represented as:

[0012]

[0013] Where f(·) is the state function vector of the quadrotor UAV system, g() is the control gain function of the quadrotor UAV system, and Θ is the Euler angle vector. Let Θ = x³ be the first derivative of the Euler angle vector. Sat(·) is a saturation function, u Θτ =u Θ (t-τ), u Θ This is the control input for the attitude subsystem.

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

[0015]

[0016] Where δ is a defined variable, Q and R are positive definite symmetric matrices, and J * Let F represent the optimal performance index function. δ For the error system dynamics, G δ =-g(Θ), where g(Θ) is the control gain function of the quadcopter UAV system.

[0017] Furthermore, the feedforward control input is:

[0018]

[0019] Where g is the gravitational acceleration, f(·) is the state function vector of the quadrotor UAV system, and Θ d χ is the Euler angle vector reference signal, K3 is the auxiliary system variable, and K3 is the gain matrix to be designed.

[0020] A second aspect of the present invention provides an unmanned aerial vehicle (UAV) trajectory tracking control system based on adaptive dynamic programming, comprising:

[0021] The data acquisition module is specifically configured to acquire the Euler angle vector reference signal and the expected value of the position loop of the quadcopter UAV system.

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

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

[0024] Furthermore, the attitude subsystem is represented as:

[0025]

[0026] Where f(·) is the state function vector of the quadrotor UAV system, g() is the control gain function of the quadrotor UAV system, and Θ is the Euler angle vector. Let Θ = x³ be the first derivative of the Euler angle vector. Sat(·) is a saturation function, u Θτ =u Θ (t-τ), uΘ This is the control input for the attitude subsystem.

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

[0028]

[0029] Where δ is a defined variable, Q and R are positive definite symmetric matrices, and J * Let F represent the optimal performance index function. δ For the error system dynamics, G δ =-g(Θ), where g(Θ) is the control gain function of the quadcopter UAV system.

[0030] Furthermore, the feedforward control input is:

[0031]

[0032] Where g is the gravitational acceleration, f(·) is the state function vector of the quadrotor UAV system, and Θ d χ is the Euler angle vector reference signal, K3 is the auxiliary system variable, and K3 is the gain matrix to be designed.

[0033] A 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 above-described adaptive dynamic programming-based unmanned aerial vehicle trajectory tracking control method.

[0034] A 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 the processor executes the program to implement the steps in the above-described adaptive dynamic programming-based unmanned aerial vehicle trajectory tracking control method.

[0035] Compared with the prior art, the beneficial effects of the present invention are:

[0036] This invention transforms the tracking control problem of the original system into a stabilization problem of a nominal error system. It adopts an adaptive dynamic programming method with a single-evaluation neural network structure to design an optimal feedback controller for the error system, ensuring that the UAV system has strong robustness and optimal tracking performance within a finite time.

[0037] This invention considers the adverse effects of external interference, input time delay, and input saturation on quadrotor unmanned aerial vehicles (UAVs), and designs a finite-time-domain robust optimal tracking control strategy for low-altitude flight control of quadrotor UAVs. Attached Figure Description

[0038] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0039] Figure 1 This is a flowchart of the 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 a quadcopter UAV according to Embodiment 1 of the present invention;

[0041] Figure 3 This is an attitude angle tracking diagram of a quadcopter UAV according to Embodiment 1 of the present invention;

[0042] Figure 4 The evaluation network weights W in Embodiment 1 of the present invention c Convergence process diagram;

[0043] Figure 5 This is a control input diagram of the attitude subsystem according to Embodiment 1 of the present invention;

[0044] Figure 6 This is a schematic diagram of the structure of a computer device according to Embodiment 4 of the present invention. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0046] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0047] Example 1

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

[0049] To better meet the actual needs of low-altitude flight of quadrotor UAVs, this embodiment presents a UAV trajectory tracking control method based on adaptive dynamic programming. This method comprehensively considers the effects of external disturbances, input time delay, and input saturation constraints. Within the backstepping framework, a finite-time robust optimal trajectory controller is designed using an adaptive dynamic programming algorithm. First, external disturbances are estimated using disturbance observer technology. Then, the Padé approximation method and auxiliary system method are employed to address the input time delay and input saturation issues in the attitude loop. Subsequently, within the backstepping framework, an adaptive dynamic programming algorithm with a single evaluation network structure is combined to design a finite-time robust optimal tracking control strategy for optimal trajectory tracking control of the quadrotor UAV. Finally, numerical simulations are conducted using 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 the constraints of external disturbances, input time delay, and input saturation, establish the position loop and attitude loop dynamic models of the quadcopter UAV system respectively.

[0052] In step 1, considering the constraints of external interference, input time delay, and input saturation on the quadcopter UAV system, its dynamic model is as follows:

[0053]

[0054]

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

[0056]

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

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

[0059] In step 2, for ease of description, P and... If x1 and x2 are replaced, then equation (1) can be rewritten as:

[0060]

[0061] Next, the position tracking error is defined as z1 = x1 - x r z2 = x2 - x 2r , where x r Let x be the expected value of the position ring. 2r This is the virtual control signal for the position loop.

[0062] To compensate for unknown external disturbances, the control law of the position subsystem is designed as follows:

[0063]

[0064] Wherein, K1 is the positive definite matrix to be designed; D p The estimated value can be obtained by designing an interference observer of the following form:

[0065]

[0066] Where Γ is the auxiliary vector and K2 is the gain matrix to be designed.

[0067] Next, Θ and Replacing them with x3 and x4 yields the attitude subsystem:

[0068]

[0069] Among them, u Θτ =u Θ (t-τ).

[0070] To effectively address the saturation problem in the control input of the attitude subsystem, the following auxiliary system is employed:

[0071]

[0072] Where χ is the auxiliary system variable, K3 is the gain matrix to be designed, H(t) is the intermediate variable to be designed, and ΔuΘτ This is the actual input for the auxiliary system.

[0073] Define a new variable δ = x⁴ - x d -χ, and by taking its derivative, we get:

[0074]

[0075] Where, x d This represents a reference signal.

[0076] To address the input time delay problem in quadcopter UAV systems, the Padé approximation technique is used, as follows:

[0077]

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

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

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

[0081]

[0082] In this invention, the attitude subsystem control input u Θ It consists of two parts, namely:

[0083] u Θ =u f +u o (12)

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

[0085]

[0086] Where, Θ d The Euler angle vector reference signal is used. Let be the first derivative of the reference signal.

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

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

[0089]

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

[0091] First, define a finite-time performance index function of the following form:

[0092]

[0093] Among them, t f Represents the terminal time. and It 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 Hamiltonian equation is:

[0095]

[0096] in,

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

[0098]

[0099] in, The optimal form of the performance index function is represented by R, where R is a real symmetric positive definite matrix, i.e., R = R T >0.

[0100] Substituting the above equation into the Hamiltonian equation and setting the equation to zero, we obtain the HJB equation in the following form:

[0101]

[0102] Among them, J * J represents the optimal performance index function * (δ), This represents the first derivative of the optimal performance index function with respect to time t, i.e. Find the first 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, while for the ideal optimal feedback control input... There exists a function ρ(δ) > 0 with respect to the error variable, satisfying... The equation holds true; at the same time, there exists a bounded positive definite matrix Λ(δ) such that the equation holds true. This holds true, where J1(δ) is a 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, The weight vector to be obtained. Let N be the time-varying activation function of the neural network, N be the number of hidden nodes in the neural network, and ε be the activation function of the neural network. c This is for estimating the error.

[0107] Furthermore, the performance metric function at the terminal moment is:

[0108]

[0109] Taking the partial derivative of equation (20) with respect to δ and t, we get:

[0110]

[0111]

[0112] Assume ε c and W c and first derivative and It satisfies the boundedness condition, that is, it has a positive constant. and Make

[0113] Next and Substituting into the HJB equation and the optimal feedback control input From this, we can obtain:

[0114]

[0115]

[0116] in, And there are positive numbers. Make ε H Represents the bounded residual value and

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

[0118]

[0119]

[0120] Among them, e h This represents the corresponding estimation error. Furthermore, the estimation error at the terminal time is:

[0121]

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

[0123]

[0124] in, α1, α2>0 represent the learning rate; condition 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 quadcopter UAV system under external disturbances, input time delays, and input saturation constraints (Equations (1) and (2)), a robust position tracking controller as shown in Equation (5) and a finite-time domain robust optimal attitude tracking controller as shown in Equation (17) are designed. Simultaneously, an disturbance observer as shown in Equation (6) is designed to estimate external disturbances, and an adaptive update law as shown in Equation (28) is designed to estimate the optimal weights. By selecting appropriate parameters, it can be ensured that the state tracking error, disturbance estimation error, and neural network weight estimation error within the entire closed-loop system are consistently and eventually bounded.

[0127] Choose the following Lyapunov function:

[0128]

[0129] Where V δ =1 / 2δ T Taking the first derivative of δ with respect to V, we can obtain...

[0130]

[0131] in,

[0132] when When, satisfy Assuming a continuous excitation condition of δ, then there exists a positive constant. Make Select At this point, choosing the appropriate parameters will make λ min (K3) > 1, and the following inequality holds:

[0133] in, This can guarantee

[0134] when When considering Under the given conditions, choose appropriate parameters to make λ min (K3) > 1, and the following inequality holds: This can guarantee

[0135] According to Lyapunov's extended theorem, for and In both cases, it can be guaranteed that the error variables in the closed-loop system are consistent and eventually bounded.

[0136] Step 5: Substitute the control algorithm into the simulation model for testing to verify that the control algorithm can be used for the control of a quadcopter UAV system and has good performance. For example... Figure 1 As shown, it specifically includes:

[0137] Step 501: Obtain the reference signal at the current time t, including the Euler angle vector reference signal Θ. d And position ring expected value x r ;

[0138] Step 502: Based on the expected value x of the location loop r Estimates of external interference experienced by drones The position tracking errors z1 and z2 are used to obtain the position subsystem control input u through the control law of the position subsystem (Equation (5)). P ; based on the position subsystem control input u P Through the position subsystem (Equation (4)), the position coordinate vector P (i.e., x1) and its first derivative are obtained. (i.e., x2), and then update the position tracking errors z1 and z2; based on the first derivative of the position coordinate vector (i.e., x2), through the interference observer (Equation (6)), update the estimate of the external interference suffered by the UAV.

[0139] Step 503: Based on the Euler angle vector reference signal Θ d The feedforward control input u is calculated using formula (13). f Based on feedforward control input u f Combined with optimal feedback control input u o The attitude subsystem control input u is calculated. Θ Based on attitude subsystem control input u Θ The Euler angle vector Θ (i.e., x3) and its first derivative are calculated using the attitude subsystem (Formula (7)). (i.e., x4); based on the Euler angle vector Θ (i.e., x3) and its first derivative. (i.e., x4), the variable δ is calculated using formula (11); based on the variable δ, the optimal feedback control input u is updated using formula (17). o (Right now, );

[0140] Step 504, return to step 502.

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

[0142] The following simulation analysis demonstrates the method of this invention. The optimal feedback control input is solved by writing a Matlab program, and the control algorithm is verified to be applicable to the finite-time-domain robust optimal trajectory tracking system of a quadcopter UAV under external disturbances, input time delay, and input saturation constraints, and it has good tracking performance.

[0143] First, the quadcopter UAV model used in the simulation has parameters m = 1.2 (kg) and g = 9.8 (m / s²). 2 Secondly, the design parameters for the controller and disturbance observer are selected as follows: K1 = diag{5; 7.5; 10}, K2 = diag{10; 15; 20}, K3 = diag{15; 10; 5}, the input delay time τ = 0.1sin(t) + 0.2s, and the upper and lower bounds of the input saturation are respectively... The corresponding optimal feedback controller parameters are set as follows: α1 = 0.5, α2 = 1, Q = diag{10; 5; 5}, R = diag{5; 10; 15}. p =[0.5sin(0.5t), 0.1sin(0.5t), 0.1sin(0.5t)] T (N) represents the selected disturbance force, P r =[0.5sin(0.5t), 0.5sin(0.6t), 0.1t] T (m), ψ r =π12(deg) represents the selected desired position and yaw angle signals. Furthermore, the excitation function is selected as follows:

[0144]

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

[0146] Figure 2 and Figure 3 The images show the position and attitude angular trajectory tracking processes of a quadcopter UAV, respectively. It can be seen that the control method proposed in this invention has a faster convergence speed and higher tracking accuracy. Figure 4 The diagram shows the convergence of the weights in the neural network, indicating that the weights gradually converge to a stable value. Figure 5 As shown in the simulation results, the input torques are all within a reasonable range, serving as the control inputs for the attitude subsystem. These simulation results demonstrate that the finite-time robust optimal trajectory tracking control strategy proposed in this invention can be effectively applied to the flight control problem of quadrotor UAVs under external disturbances, input time delays, and input saturation constraints.

[0147] Example 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 specifically configured to acquire the Euler angle vector reference signal and the expected value of the position loop of the quadcopter UAV system.

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

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

[0152] The attitude subsystem is represented as follows:

[0153]

[0154] Where f(·) is the state function vector of the quadrotor UAV system, g() is the control gain function of the quadrotor UAV system, and Θ is the Euler angle vector. Let Θ = x³ be the first derivative of the Euler angle vector. Sat(·) is a saturation function, u Θτ =u Θ (t-τ), u Θ This is the 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] Where δ is a defined variable, Q and R are positive definite symmetric matrices, and J * Let F represent the optimal performance index function. δ For the error system dynamics, G δ =-g(Θ), where g(Θ) is the control gain function of the quadcopter UAV system.

[0158] The feedforward control input is:

[0159]

[0160] Where g is the gravitational acceleration, f(·) is the state function vector of the quadrotor UAV system, and Θ d χ is the Euler angle vector reference signal, K3 is the auxiliary system variable, and K3 is the gain matrix to be designed.

[0161] It should be noted that each module in this embodiment corresponds one-to-one with each step in Embodiment 1, and their specific implementation processes are the same, so they will not be repeated here.

[0162] Example 3

[0163] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the UAV trajectory tracking control method based on adaptive dynamic programming as described in Embodiment 1 above.

[0164] Example 4

[0165] This embodiment provides a computer device, such as... Figure 6 As shown, the system 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. The processor, communication interface, and computer-readable storage medium can be connected via a bus or other means. The communication interface is used to receive and transmit data, and when the processor executes the program, it implements the steps of the UAV trajectory tracking control method based on adaptive dynamic programming as described in Embodiment 1 above.

[0166] Any references to memory, storage, database, or other media used in this application and 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. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0167] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1A device that provides the functions specified in one or more boxes.

[0168] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0169] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0170] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A UAV trajectory tracking control method based on adaptive dynamic programming, characterized in that, include: Obtain the Euler angle vector reference signal and the expected value of the position loop of the quadrotor UAV system; Based on the expected value of the position loop, the estimated value of external disturbance, and the position tracking error, the control input of the position subsystem is obtained through the control law of the position subsystem. The position coordinate vector and its first derivative are calculated by the position subsystem to update the position tracking error. Based on the first derivative of the position coordinate vector, the estimated value of 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 calculating the Euler angle vector and its first derivative through the attitude subsystem, the adaptive dynamic programming method of the 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. The attitude subsystem is represented as follows: in, Let be the state function vector of the quadcopter unmanned aerial vehicle system. For the control gain function of the quadcopter unmanned aerial vehicle system, Let them be Euler angle vectors. The first derivative of the Euler angle vector. = , = , It is a saturation function. , For attitude subsystem control input; The adaptive dynamic programming method obtains the optimal feedback control input by solving the HJB equation, which is: in, To define variables, Q and R It is a positive definite symmetric matrix. This represents the optimal performance index function. For error system dynamics, , For the control gain function of the quadcopter unmanned aerial vehicle system; The feedforward control input is: in, It is the acceleration due to gravity. Let be the state function vector of the quadcopter unmanned aerial vehicle system. The Euler angle vector reference signal is used. As auxiliary system variables, The gain matrix to be designed, Let be the first derivative of the reference signal.

2. A UAV trajectory tracking control system based on adaptive dynamic programming, characterized in that, include: The data acquisition module is specifically configured to acquire the Euler angle vector reference signal and the expected value of the position loop of the quadcopter UAV system. The position control module is configured to: obtain the position subsystem control input based on the position loop expected value, external disturbance estimate, and position tracking error through the control law of the position subsystem; calculate the position coordinate vector and its first derivative using the position subsystem to update the position tracking error; and update the external disturbance estimate based on the first derivative of the position coordinate vector through the disturbance observer. The attitude control module is configured to: calculate the feedforward control input based on the Euler angle vector reference signal, and calculate the attitude subsystem control input by combining the optimal feedback control input; calculate the Euler angle vector and its first derivative through the attitude subsystem, and then use an adaptive dynamic programming method with a single evaluation neural network structure to design the optimal feedback controller for the nominal error system and update the optimal feedback control input. The attitude subsystem is represented as follows: in, Let be the state function vector of the quadcopter unmanned aerial vehicle system. For the control gain function of the quadcopter unmanned aerial vehicle system, Let them be Euler angle vectors. The first derivative of the Euler angle vector. = , = , It is a saturation function. , For attitude subsystem control input; The adaptive dynamic programming method obtains the optimal feedback control input by solving the HJB equation, which is: in, To define variables, Q and R It is a positive definite symmetric matrix. This represents the optimal performance index function. For error system dynamics, , For the control gain function of the quadcopter unmanned aerial vehicle system; The feedforward control input is: in, It is the acceleration due to gravity. Let be the state function vector of the quadcopter unmanned aerial vehicle system. The Euler angle vector reference signal is used. As auxiliary system variables, The gain matrix to be designed, Let be the first derivative of the reference signal.

3. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the UAV trajectory tracking control method based on adaptive dynamic programming as described in claim 1.

4. A computer device comprising a computer-readable storage medium, a processor, and a computer program stored on the computer-readable storage medium and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the UAV trajectory tracking control method based on adaptive dynamic programming as described in claim 1.

Images