An unmanned aerial vehicle wind-resistant control method based on improved LQR-LADRC

Abstract

The application relates to an unmanned aerial vehicle wind resistance control method based on an improved LQR-LADRC, which comprises the following steps: constructing an unmanned aerial vehicle attitude dynamics model containing a disturbance term; constructing an attitude controller based on the improved LQR-LADRC based on the unmanned aerial vehicle attitude dynamics model; calculating a system control amount capable of driving the unmanned aerial vehicle attitude to adjust to a desired state based on the attitude controller; and performing unmanned aerial vehicle wind resistance control based on the system control amount. The application can improve the response speed of the unmanned aerial vehicle attitude, improve the interference suppression capability of the unmanned aerial vehicle, and enable the unmanned aerial vehicle to maintain stability in a complex wind disturbance environment.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for unmanned aerial vehicles (UAVs), and in particular to a wind-resistant control method for UAVs based on an improved LQR-LADRC. Background Technology

[0002] With the development trends of larger-scale, more extensive offshore wind turbine equipment and deeper-sea development, problems such as high operation and maintenance costs and poor accessibility are becoming increasingly prominent, making traditional manual inspection methods insufficient to meet the needs. With the development of multi-rotor drone technology, multi-rotor drones are being widely used in wind power inspection. Currently, researchers are studying non-stop inspection solutions for offshore wind turbines.

[0003] Wind farms possess abundant wind energy resources, but wind conditions are complex and variable. When drones perform inspection tasks while the wind turbines are running, they are affected by complex wind disturbances, leading to reduced stability. Current drone control algorithms still have certain limitations in applications under complex wind disturbance environments. For example, while PID control has advantages such as simple structure and independence from system models, its control performance is not ideal for quadcopter drone systems characterized by nonlinearity, strong coupling, underactuation, and external wind disturbances.

[0004] Therefore, improving the interference suppression capability of drones and enabling them to maintain stability in complex wind and disturbance environments remains a technical problem that urgently needs to be solved. Summary of the Invention

[0005] The purpose of this invention is to provide a wind-resistant control method for unmanned aerial vehicles (UAVs) based on an improved LQR-LADRC. This method can improve the response speed of the UAV's attitude and maintain stability in complex wind-affected environments by leveraging the UAV's interference suppression capabilities.

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

[0007] A wind-resistant control method for unmanned aerial vehicles (UAVs) based on an improved LQR-LADRC includes:

[0008] Construct an attitude dynamics model of the UAV containing perturbation terms;

[0009] Based on the aforementioned UAV attitude dynamics model, an attitude controller based on an improved LQR-LADRC is constructed. The improved LQR-LADRC attitude controller includes: calculating the desired angular velocity based on the attitude angle error, designing the optimal control law based on the attitude angle error and angular velocity error, and introducing a second-order extended state observer.

[0010] Based on the improved LQR-LADRC attitude controller, the system control quantity that can drive the UAV attitude to adjust to the desired state is calculated;

[0011] The system control variables are used to control the wind resistance of the UAV.

[0012] Optionally, the UAV attitude dynamics model is:

[0013]

[0014] in, θ and ψ represent the roll angle, pitch angle, and yaw angle of the UAV, respectively. x I y I z These are the moments of inertia of the machine along the x, y, and z axes, respectively. Represents the roll angle and angular acceleration. Represents pitch angle and angular acceleration. Indicates yaw angle and angular acceleration. f θ f ψ These represent the total disturbances, including internal and external disturbances, on the roll, pitch, and yaw channels, respectively.

[0015] Optionally, based on the improved LQR-LADRC attitude controller, the system control quantities that can drive the UAV attitude adjustment to the desired state are calculated, including:

[0016] The desired angular velocity is obtained by using P control on the attitude angle error, and the angular velocity error is introduced. The optimal control law is designed with the attitude angle error and angular velocity error as state variables, so that the attitude angle error and angular velocity error converge to 0 quickly.

[0017] A second-order extended state observer is introduced to estimate the total disturbance and perform feedforward compensation, and combined with the optimal control law, the system control quantity is obtained.

[0018] Optionally, the optimal control rate includes: the optimal control rate of the roll angle channel, the optimal control rate of the pitch angle channel, and the optimal control rate of the yaw angle channel.

[0019] Optionally, designing the optimal control law includes:

[0020] The optimal control law for the roll angle channel is designed, including:

[0021] The error between the desired roll angle and the actual roll angle is controlled by P to obtain the desired roll angular velocity; the desired roll angular velocity... for:

[0022]

[0023] in, The desired attitude angle is output by the UAV position controller. The proportional coefficient controlled by P;

[0024] Based on each output of the position controller If the value is constant, then the expected rate of change of the roll angle is taken as 0, that is:

[0025]

[0026] Take the roll angle error and roll angular velocity error as state variables. and

[0027]

[0028] in, It is the roll angle and angular velocity;

[0029] Take control quantity for:

[0030]

[0031] Based on the expected roll angular velocity Expected roll angle change rate of 0, roll angle error and roll angular velocity error, control quantity The state-space equation for obtaining the roll angle channel is:

[0032]

[0033] The roll angle channel system matrix is ​​as follows:

[0034]

[0035] The control matrix is:

[0036]

[0037] The weighted matrix of the state variables is as follows:

[0038]

[0039] The weighted matrix of the control variables is as follows:

[0040]

[0041] By solving the Riccati equation, the matrix is ​​calculated.

[0042]

[0043] Based on matrix Calculate the roll channel feedback gain matrix

[0044]

[0045] The optimal control rate for obtaining the roll angle channel is:

[0046]

[0047] in, The optimal control law for the roll angle channel. and These are the elements of the gain matrix.

[0048] Optionally, the optimal control law for the pitch angle channel includes:

[0049] The controller parameters for the pitch angle channel are designed in the same way as those for the roll angle channel; the optimal control rate for the pitch angle channel is:

[0050]

[0051] Among them, u 0θ This represents the optimal control law for the pitch angle channel.

[0052] Optionally, the optimal control law for the yaw angle channel includes:

[0053] The design process for the optimal control rate of the yaw angle channel is the same as that for the optimal control rate of the roll angle channel; the optimal control rate of the yaw angle channel is:

[0054]

[0055] Among them, u 0ψ The optimal control law for the pitch angle channel. For the yaw channel feedback gain matrix, x 1ψ =ψ d -ψ is the yaw angle error state variable. The yaw angle and angular velocity error are the state variables. Angular velocity represents the yaw angle.

[0056] Optionally, the second-order extended state observer includes: a roll angle second-order extended state observer, a pitch angle second-order extended state observer, and a yaw angle second-order extended state observer;

[0057] The second-order extended state observer for the roll angle is:

[0058]

[0059] in, respectively for roll angle Roll angle angular velocity Total disturbance in the roll channel The estimated value, Their respective rates of change Bandwidth for roll and pitch angle observers;

[0060] The second-order extended state observer for pitch angle is:

[0061]

[0062] Among them, z 1θ z 2θ z 3θ These are the pitch angle θ and the pitch angle angular velocity, respectively. Total disturbance f in pitch channel θ The estimated value, Their respective rates of change Bandwidth for roll and pitch angle observers;

[0063] The second-order extended state observer for the yaw angle is:

[0064]

[0065] Among them, z 1ψ z 2ψ z 3ψ These are the yaw angle ψ and the yaw angle angular velocity, respectively. Total disturbance f in the yaw channel ψ The estimated value, Their respective rates of change, w oψ This represents the bandwidth of the yaw angle observer.

[0066] Optionally, the system control quantity is:

[0067]

[0068] in, u represents the control quantity of the roll channel system. θ u represents the control variable of the pitch channel system. ψ This indicates the control parameters for the yaw channel system. b represents the roll channel disturbance compensation factor. θ b represents the pitch channel disturbance compensation factor. ψ This represents the yaw channel disturbance compensation factor.

[0069] The beneficial effects of this invention are as follows:

[0070] The improved LQR of this invention can respond to roll angle errors more quickly, thereby reducing the steady-state error of the system under disturbances; the introduction of LESO can improve the system's interference suppression capability and further reduce the steady-state error; compared with existing controllers, the improved LQR-LADRC controller of this invention has a smaller maximum steady-state error value and better interference suppression effect. Attached Figure Description

[0071] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0072] Figure 1 This is a schematic diagram of a wind-resistant control method for unmanned aerial vehicles based on an improved LQR-LADRC according to an embodiment of the present invention;

[0073] Figure 2 This is a schematic diagram of the structure of the improved LQR-LADRC controller according to an embodiment of the present invention;

[0074] Figure 3 The response diagrams of four controllers—improved LQR-LADRC, LQR-LADRC, improved LQR, and LQR—under the disturbance-free conditions of this invention are shown in the embodiments of the present invention.

[0075] Figure 4 The diagram shows the response of four controllers—improved LQR-LADRC, LQR-LADRC, improved LQR, and LQR—under sinusoidal disturbances according to embodiments of the present invention. Detailed Implementation

[0076] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0077] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0078] like Figure 1 As shown, this embodiment proposes a wind-resistant control method for unmanned aerial vehicles (UAVs) based on an improved LQR-LADRC, including:

[0079] Construct an attitude dynamics model of the UAV containing perturbation terms;

[0080] Based on the aforementioned UAV attitude dynamics model, an attitude controller based on an improved LQR-LADRC is constructed. The improved LQR-LADRC attitude controller includes: calculating the desired angular velocity based on the attitude angle error, designing the optimal control law based on the attitude angle error and angular velocity error, and introducing a second-order extended state observer. Figure 2 As shown;

[0081] Based on the improved LQR-LADRC attitude controller, the system control quantity that can drive the UAV attitude to adjust to the desired state is calculated;

[0082] The system control variables are used to control the wind resistance of the UAV.

[0083] Specifically, in this embodiment, constructing the UAV attitude dynamics model containing perturbation terms includes:

[0084] First, construct the UAV attitude dynamics model:

[0085]

[0086] Strongly coupled terms and other internal and external disturbances are categorized into a total disturbance. f θ f ψ The expression is as follows:

[0087]

[0088] Take control quantity u θ u ψ They are respectively:

[0089]

[0090] Decoupling equation (1), the UAV attitude dynamics model is rewritten as:

[0091]

[0092] In the formula, θ and ψ represent the roll angle, pitch angle, and yaw angle of the UAV, respectively; I x I y I z These are the moments of inertia of the machine body along the x, y, and z axes, respectively. Represents the roll angle and angular acceleration. Represents pitch angle and angular acceleration. Indicates yaw angle and angular acceleration. f θ f ψThese represent the total disturbances, including internal and external disturbances, on the roll, pitch, and yaw channels, respectively; l is the lever arm length. τ θ τ ψ These are the rolling moment, pitching moment, and yaw moment, respectively. k θ k ψ This is the drag coefficient; d θ d ψ This is the disturbance term caused by external factors.

[0093] Specifically, in this embodiment, based on the UAV attitude dynamics model, an attitude controller based on the improved LQR-LADRC is constructed; based on the attitude controller, a system control quantity that can drive the UAV attitude to adjust to the desired state is calculated;

[0094] The desired angular velocity is obtained by using P-control on the attitude angle error, and the angular velocity error is introduced. Using the attitude angle error and angular velocity error as state variables, the optimal control law is designed to make the attitude angle error and angular velocity error converge quickly to 0. The optimal control law for the roll angle is designed as follows:

[0095] The error between the desired roll angle and the actual roll angle is controlled by P to obtain the desired angular velocity. for:

[0096]

[0097] In the formula, The proportional coefficient controlled by P. The desired attitude angle is output by the UAV position controller; the following simplification is performed: Since the position controller outputs... If the value is constant, then the expected rate of change of the roll angle can be taken as 0, that is:

[0098]

[0099] Take the roll angle error and roll angular velocity error as state variables. and

[0100]

[0101] Take control quantity for:

[0102]

[0103] From equations (5), (6), (7), and (8), the state-space equation of the roll angle channel can be rewritten as:

[0104]

[0105] The roll angle channel system matrix is ​​as follows:

[0106]

[0107] The control matrix is:

[0108]

[0109] The weighted matrix of the state variables is as follows:

[0110]

[0111] The weighted matrix of the control variables is as follows:

[0112]

[0113] By solving the Riccati equation, the matrix is ​​calculated.

[0114]

[0115] Based on matrix Calculate the roll channel feedback gain matrix

[0116]

[0117] The optimal control rate for obtaining the roll angle channel is:

[0118]

[0119] in, The optimal control law for the roll angle channel. and These are the elements of the gain matrix.

[0120] The quadcopter UAV exhibits similar dynamic characteristics in both the roll and pitch channels. Therefore, a universal design is implemented for the controller parameters of the roll and pitch angles. The optimal control law for the pitch channel is as follows:

[0121]

[0122] The optimal control law design process for the yaw angle channel is the same as that for the roll angle, and the optimal control law is:

[0123]

[0124] Among them, u 0ψ The optimal control law for the pitch angle channel. For the yaw channel feedback gain matrix, x1ψ =ψ d -ψ is the yaw angle error state variable. The yaw angle and angular velocity error are the state variables. Angular velocity represents the yaw angle.

[0125] Furthermore, in S2, a second-order extended state observer (LESO) is introduced to estimate the total disturbance and perform feedforward compensation, and combined with the optimal control law, the system control quantity is obtained; wherein, the roll angle LESO is:

[0126]

[0127] in, respectively for roll angle Roll angle angular velocity Total disturbance in the roll channel The estimated value, Their respective rates of change Bandwidth for roll and pitch angle observers;

[0128] The pitch angle LESO is:

[0129]

[0130] Among them, z 1θ z 2θ z 3θ These are the pitch angle θ and the pitch angle angular velocity, respectively. Total disturbance f in pitch channel θ The estimated value, Their respective rates of change Bandwidth for roll and pitch angle observers;

[0131] Yaw angle LESO is:

[0132]

[0133] Among them, z 1ψ z 2ψ z 3ψ These are the yaw angle ψ and the yaw angle angular velocity, respectively. Total disturbance f in the yaw channel ψ The estimated value, Their respective rates of change, w oψ This represents the bandwidth of the yaw angle observer. b θ b ψ The possible values ​​are:

[0134]

[0135] Combining the optimal control law, the system control quantity is obtained as follows:

[0136]

[0137] in, u represents the control quantity of the roll channel system. θ u represents the control variable of the pitch channel system. ψ This indicates the control parameters for the yaw channel system. b represents the roll channel disturbance compensation factor. θ b represents the pitch channel disturbance compensation factor. ψ This represents the yaw channel disturbance compensation factor.

[0138] The following embodiment uses roll angle as an example to illustrate the design of four controllers: improved LQR-LADRC, LQR-LADRC, improved LQR, and LQR. Simulations comparing the four controllers are performed in the Matlab / Simulink environment. The optimal control law for the LQR-LADRC and LQR controllers is designed using roll angle error and roll angular velocity as state variables. Relevant parameters are shown in Tables 1 and 2.

[0139] Table 1. Parameters of the Quadrotor UAV Model

[0140]

[0141] Table 2 Controller Parameters

[0142]

[0143] like Figure 3 The diagram shows the response of each controller to a 15° roll angle step signal under no external disturbance. There is no significant difference in response to LESO without external disturbance. The improved LQR-LADRC and improved LQR converge to the desired value in approximately 0.84s with no overshoot. LQR reaches stability in approximately 0.55s with an overshoot of approximately 0.33%. LQR-LADRC reaches stability in approximately 0.81s with an overshoot of approximately 0.33%. While the improved LQR-LADRC and improved LQR have slightly slower settling times, they have a faster initial rise in response speed, meaning they can respond more quickly. For devices with high real-time requirements, such as quadcopter drones, the improved LQR-LADRC and improved LQR have advantages.

[0144] Figure 4 The diagram shows the response of each controller to a 15° roll angle step signal under a sinusoidal disturbance. The applied sinusoidal disturbance is... Table 3 shows the maximum steady-state error of each controller.

[0145] Table 3 Maximum steady-state error of each controller

[0146]

[0147] The improved LQR can respond to roll angle errors more quickly, thereby reducing the steady-state error of the system under disturbances; the introduction of LESO can improve the system's interference suppression capability and further reduce the steady-state error; compared with the other three controllers, the improved LQR-LADRC controller of this invention has the smallest maximum steady-state error value and has a better interference suppression effect.

[0148] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. An improved LQR-LADRC-based wind-resistant control method for a UAV, characterized in that, The application relates to an unmanned aerial vehicle (UAV) wind-resistant control method. The application relates to an unmanned aerial vehicle (UAV) wind-resistant control method. The application relates to an unmanned aerial vehicle (UAV) wind-resistant control method. The application relates to an unmanned aerial vehicle (UAV) wind-resistant control method. The application relates to an unmanned aerial vehicle (UAV) wind-resistant control method. The application relates to an unmanned aerial vehicle (UAV) wind-resistant control method. The application relates to an unmanned aerial vehicle (UAV) wind-resistant control method.

2. The improved LQR-LADRC based wind-resistant control method for UAV according to claim 1, wherein, The application relates to an unmanned aerial vehicle (UAV) wind-resistant control method. wherein φ, θ, ψ are the roll angle, pitch angle, yaw angle of the UAV respectively, I x , y , z are the moment of inertia of the body x, y, z three-axis respectively, denotes the roll angle angular acceleration, denotes the pitch angle angular acceleration, denotes the yaw angle angular acceleration, , , denote the total disturbance on the roll channel, pitch channel, yaw channel respectively, which contains the internal disturbance and external disturbance of the system.

3. The improved LQR-LADRC based wind-resistant control method for UAV according to claim 2, wherein, The application relates to an unmanned aerial vehicle (UAV) wind-resistant control method.

4. The improved LQR-LADRC based wind-resistant control method for UAV according to claim 3, wherein, The application relates to an unmanned aerial vehicle (UAV) wind-resistant control method. The application relates to an unmanned aerial vehicle (UAV) wind-resistant control method. The error of the desired roll angle and the actual roll angle is controlled by P control to obtain a desired roll angular velocity; the desired roll angular velocity ω φd is: wherein φ d is a desired attitude angle, output by the UAV position controller, is a proportional coefficient for P control; φ based on the position controller output each time d is a constant value, then the desired roll angle rate is taken to be zero, i.e.: Taking roll angle error and roll angular velocity error as state variables and : wherein, is the roll angle velocity; Taking the control quantity u 0φ is: Based on the desired roll angular velocity ω φd , the desired roll angular change rate is 0, the roll angle error and the roll angular velocity error, the control amount u 0φ The state space equation of the roll angle channel is obtained as The application relates to an unmanned aerial vehicle (UAV) wind-resistant control method. The application relates to an unmanned aerial vehicle (UAV) wind-resistant control method. The application relates to an unmanned aerial vehicle (UAV) wind-resistant control method. The application relates to an unmanned aerial vehicle (UAV) wind-resistant control method. By solving the Riccati equation, the matrix : Based on a matrix Computing roll channel feedback gain matrix : The application relates to an unmanned aerial vehicle (UAV) wind-resistant control method. wherein, is the optimal control rate for the roll angle channel, and is the gain matrix element.

5. The improved LQR-LADRC based wind-resistant control method for UAV according to claim 4, wherein, The application relates to an unmanned aerial vehicle (UAV) wind-resistant control method. The application relates to an unmanned aerial vehicle (UAV) wind-resistant control method. wherein, is the optimal control rate for the pitch channel.

6. The improved LQR-LADRC based wind-resistant control method for UAV according to claim 4, wherein, The application relates to an unmanned aerial vehicle (UAV) wind-resistant control method. The application relates to an unmanned aerial vehicle (UAV) wind-resistant control method. wherein, is the optimal control rate for the pitch channel, is the yaw channel feedback gain matrix, is the yaw angle error state variable, is the yaw angle angular velocity error state variable, is the yaw angle angular velocity.

7. The improved LQR-LADRC based wind-resistant control method for UAV according to claim 1, wherein, The application relates to an unmanned aerial vehicle (UAV) wind-resistant control method. The application relates to an unmanned aerial vehicle (UAV) wind-resistant control method. wherein , , are respectively the estimated values of the roll angle , the roll angle angular velocity , the total roll channel disturbance , , , are respectively the rates of change thereof, is the roll angle and pitch angle observer bandwidth; The application relates to an unmanned aerial vehicle (UAV) wind-resistant control method. in, , , respectively for pitch angle Pitch angle and angular velocity Total disturbance in pitch channel The estimated value, , , Their respective rates of change Bandwidth for roll and pitch angle observers; The application relates to an unmanned aerial vehicle (UAV) wind-resistant control method. wherein , , are respectively the estimated values of the yaw angle , the yaw angle angular velocity , the total disturbance of the yaw channel , , , are respectively the rates of change thereof, is the yaw angle observer bandwidth.

8. The improved LQR-LADRC based anti-wind control method for UAV according to claim 1, wherein, The application relates to an unmanned aerial vehicle (UAV) wind-resistant control method. The application relates to an unmanned aerial vehicle (UAV) wind-resistant control method. The application relates to an unmanned aerial vehicle (UAV) wind-resistant control method. The application relates to an unmanned aerial vehicle (UAV) wind-resistant control method. The application relates to an unmanned aerial vehicle (UAV) wind-resistant control method. The application relates to an unmanned aerial vehicle (UAV) wind-resistant control method. The application relates to an unmanned aerial vehicle (UAV) wind-resistant control method. The application relates to an unmanned aerial vehicle (UAV) wind-resistant control method. The application relates to an unmanned aerial vehicle (UAV) wind-resistant control method. 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