A Trajectory Tracking Method and Device Based on Feedback Data of a Drone System

Abstract

The present application discloses a trajectory tracking method and device based on feedback data of an unmanned aerial vehicle system. The method includes constructing an internal model system according to the evolution matrix of an external signal; connecting an unknown unmanned aerial vehicle system, a sensor and the internal model system, directly applying a control input to the unknown unmanned aerial vehicle system, using the tracking error between the sensor output and the trajectory to be tracked as the input of the internal model system, and collecting the internal model state; constructing a control gain matrix by using the control input, the sensor output, the internal model state and a filtering matrix; and constructing an output controller by using the control gain matrix to achieve system output control. The present application constructs an output regulation controller for feedback output regulation based on the control input-sensor output of the unknown unmanned aerial vehicle system collected offline, and realizes the output regulation of the unknown unmanned aerial vehicle system without prior identification of the physical process, ensuring zero-deviation tracking of the unmanned system during the trajectory tracking process.

Description

Technical Field

[0001] This application relates to the technical field of unmanned aerial vehicle control, and particularly to a trajectory tracking method and device based on feedback data of an unmanned aerial vehicle system. Background Art

[0002] At present, with the lead of artificial intelligence in scientific and technological innovation, unmanned systems represented by autonomous vehicles, robots, unmanned aerial vehicles, unmanned boats, etc. have become key application fields of artificial intelligence technology. In many applications of unmanned systems such as unmanned aerial vehicle flight control and robotic arm operation, tracking a desired trajectory signal or suppressing a specific type of interference is a common requirement. This problem is generally referred to as the output regulation problem, and the desired tracking trajectory and specific interference signal are collectively referred to as external signals.

[0003] The output regulation of the target unmanned system is generally achieved based on a model. Therefore, when performing output control on the unmanned system, it is necessary to establish a mathematical model of the controlled object (for example, using methods such as mechanism or identification models). However, as the scale and complexity of the unmanned system continue to increase, it is often difficult to obtain an accurate model of the controlled object, which will affect the construction of the output regulation controller, and further affect the tracking accuracy of the position of the unmanned system during the trajectory tracking process.

[0004] Therefore, the existing technology still needs to be improved. Summary of the Invention

[0005] The technical problem to be solved by this application is to provide a trajectory tracking method and device based on feedback data of an unmanned aerial vehicle system in view of the deficiencies of the existing technology.

[0006] To solve the above technical problem, the first aspect of this application provides a trajectory tracking method based on feedback data of an unmanned aerial vehicle system. Specifically, the trajectory tracking method based on feedback data of the unmanned aerial vehicle system includes:

[0007] Connect an unknown unmanned aerial vehicle system, a sensor, and an internal model system, apply a continuously excited control input sequence to the unknown unmanned aerial vehicle system for a period of time, and collect the sensor output of the sensor, where the internal model system is constructed based on the evolution matrix of the external signal;

[0008] Use the tracking error between the sensor output and its corresponding desired trajectory as the input of the internal model system, and collect the internal model state formed by the internal model system based on the tracking error;

[0009] Determine the control gain matrix based on the control input sequence, all the collected internal model states, the tracking error of the sensor output of each control input, and the filtering matrix, and output an adjustment controller based on the control gain matrix, where the filtering matrix is constructed based on the evolution matrix of the external signal;

[0010] Connect the unknown UAV system, the sensor, the internal model system, and the output adjustment controller, and perform output adjustment on the unknown UAV system through the output adjustment controller.

[0011] The trajectory tracking method based on the feedback data of the UAV system, where the internal model system is expressed as:

[0012] ,

[0013] ,

[0014] ,

[0015] where, represents the internal model state at time represents the tracking error at time represents the internal model matrix pair, represents the identity matrix of dimension , is a zero matrix of dimension , represents the coefficients of the minimal polynomial of the evolution matrix , is a positive integer.

[0016] The trajectory tracking method based on the feedback data of the UAV system, where the control input sequence includes randomly generated control inputs, and the control input sequence satisfies order persistent excitation, where, , represents the dimension of the control input, represents the dimension of the sensor output, represents the observable index.

[0017] The trajectory tracking method based on the feedback data of the UAV system, where the determination of the control gain matrix based on the control input sequence, all the collected internal model states, the tracking error of the sensor output of each control input, and the filtering matrix specifically includes:

[0018] Construct an input data matrix based on the control input sequence;

[0019] Construct the augmented tracking error data matrix and the augmented tracking error evolution data matrix of the unknown UAV system based on all the collected internal model states, the input data matrix, and the tracking errors of the sensor outputs of each control input;

[0020] Solve a set of linear matrix inequalities based on the augmented tracking error data matrix, the augmented tracking error evolution data matrix, and the filtering matrix to obtain the control gain parameters;

[0021] Determine the control gain matrix based on the control gain parameters and the input data matrix , denotes the input data matrix, denotes the observability index, denotes the number of control inputs;

[0022] wherein, the set of linear matrix inequalities is:

[0023] ,

[0024] wherein, denotes the control gain parameter, denotes the inverse matrix of, denotes the augmented tracking error data matrix, denotes the augmented tracking error evolution data matrix, denotes a zero matrix of dimension , denotes the filtering matrix.

[0025] The trajectory tracking method based on the feedback data of the UAV system, wherein the output regulation controller is specifically:

[0026] When :

[0027] Each element in is randomly selected from , collect and record ;

[0028] When :

[0029] ,

[0030] wherein,

[0031] ,

[0032] ,

[0033] ,

[0034] ,

[0035] wherein, represents the auxiliary variable of the dimension, and represents the sensor output of the unknown UAV system at time represents the internal model state of the internal model system at time represents the control input at time represents the observability index, represents the identity matrix of dimension , , and both represent the parameter matrix, represents the identity matrix of dimension , represents the all-zero matrix of represents the all-zero matrix of dimension , represents the all-zero matrix of dimension , represents the all-zero matrix of represents the all-zero matrix of represents the all-zero matrix of represents the all-zero matrix of

[0036] For the trajectory tracking method based on the feedback data of the UAV system, wherein the output regulation of the unknown UAV system by the output regulation controller specifically includes:

[0037] Collecting the sensor output of the unknown UAV system through a sensor;

[0038] Taking the tracking error corresponding to the sensor output as the input of the internal model system and collecting the internal model state of the internal model system;

[0039] Sending the internal model state to the output regulation controller, generating a control input by the output regulation controller based on the sensor output and the internal model state, and applying the control input to the unknown UAV system to perform output regulation on the unknown UAV system.

[0040] The second aspect of the present application provides a trajectory tracking system based on the output feedback data of an unmanned aerial vehicle (UAV) system. The trajectory tracking system based on the output feedback data of the UAV system includes an unknown UAV system, a sensor, an internal model system, and an output regulation controller constructed based on the output feedback data of the UAV system. The unknown UAV system, the sensor, the internal model system, and the output regulation controller are connected;

[0041] The sensor is used to collect the sensor output of the unknown UAV system;

[0042] The internal model system is used to take the tracking error between the sensor output and its corresponding desired trajectory as the input, and output the internal model state corresponding to the tracking error;

[0043] The output regulation controller is used to generate a control input based on the sensor output and the internal model state, and apply the control input to the unknown UAV system to perform output regulation on the unknown UAV system.

[0044] For the above-mentioned trajectory tracking system based on the output feedback data of the UAV system, the construction process of the output regulation controller specifically includes:

[0045] Connect the unknown UAV system, the sensor, and the internal model system, apply a control input sequence with continuous excitation to the unknown UAV system for a period of time, and collect the sensor output of the sensor. The internal model system is constructed based on the evolution matrix of the external signal;

[0046] Take the tracking error between the sensor output and its corresponding desired trajectory as the input of the internal model system, and collect the internal model state formed by the internal model system based on the tracking error;

[0047] Determine the control gain matrix based on the control input sequence, all the collected internal model states, the tracking error of the sensor output of each control input, and the filtering matrix, and output the regulation controller based on the control gain matrix. The filtering matrix is constructed based on the evolution matrix of the external signal.

[0048] The third aspect of the present application provides a computer-readable storage medium storing one or more programs, which can be executed by one or more processors to implement the steps in any of the above-mentioned trajectory tracking methods based on the feedback data of the UAV system.

[0049] The fourth aspect of the present application provides a terminal device, which includes: a processor and a memory;

[0050] The computer-readable program for implementing the trajectory tracking method based on the feedback data of the drone system as described above is stored in the memory;

[0051] The processor executes the computer-readable program to perform output control on the drone.

[0052] Beneficial effects:

[0053] 1. In this application, the control input-sensor output of the unknown drone system collected offline is used, and an output adjustment controller for feedback output adjustment is constructed based on the internal model state determined by the sensor output. Without the need for prior identification of the physical process, the output adjustment of the unknown drone system is achieved, improving the accuracy of the output adjustment of the unmanned system, and thus the tracking accuracy during the trajectory tracking of the unmanned system can be improved.

[0054] 2. In this application, both the process of constructing the output adjustment controller through offline collection and the process of performing output adjustment through the output adjustment controller are achieved only through the output data of the unknown drone system, realizing the output adjustment of the unknown drone system.

[0055] 3. When constructing the output adjustment controller of the unknown drone system in this application, only a low-complexity linear matrix inequality group needs to be solved offline, and there is no need to solve optimization problems online, reducing the computational complexity of the output adjustment control. Description of the drawings

[0056] To more clearly illustrate the technical solutions in the embodiments of this application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of this application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0057] Figure 1 It is a flowchart of the trajectory tracking method based on the feedback data of the drone system provided by the embodiments of this application.

[0058] Figure 2 The principle architecture diagram of offline data collection.

[0059] Figure 3 It is the principle architecture diagram of the trajectory tracking system based on the feedback data of the drone system.

[0060] Figure 4 It is the sensor output from the first dimension to the fourth dimension of the quadrotor drone model and the trajectory diagram of the desired tracking.

[0061] Figure 5 It is the trajectory diagram of the system tracking error from the first dimension to the fourth dimension of the quadrotor drone model.

[0062] Figure 6 This is the structural principle architecture diagram of the unmanned aerial vehicle provided by the embodiment of the present application. Specific implementation manners

[0063] The embodiment of the present application provides a trajectory tracking method and device based on feedback data of an unmanned aerial vehicle system. To make the purpose, technical solution and effect of the present application clearer and more definite, the following further describes the present application in detail with reference to the accompanying drawings and by way of examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0064] Those skilled in the art of the present technology can understand that unless specifically stated otherwise, the singular forms "a", "an", "the" and "said" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of the present application means the presence of the described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or their groups. It should be understood that when we say that an element is "connected" or "coupled" to another element, it can be directly connected or coupled to other elements, or there may also be intermediate elements. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The phrase "and / or" used herein includes all or any unit and all combinations of one or more related listed items.

[0065] Those skilled in the art of the present technology can understand that unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as the general understanding of those of ordinary skill in the art to which the present application belongs. It should also be understood that terms such as those defined in a general dictionary should be understood to have a meaning consistent with the meaning in the context of the prior art, and will not be interpreted with an idealized or overly formal meaning unless specifically defined as here.

[0066] It should be understood that the sequence numbers and magnitudes of the steps in this embodiment do not mean the order of execution. The order of execution of each process is determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiment of the present application.

[0067] Through research, it is found that in the current situation where artificial intelligence leads scientific and technological innovation, unmanned systems represented by autonomous vehicles, robots, unmanned aerial vehicles, unmanned boats, etc. have become key application fields of artificial intelligence technology. In many applications of unmanned systems such as unmanned aerial vehicle flight control and robotic arm operation, tracking a desired trajectory signal or suppressing a specific type of interference is a common requirement. This type of problem is collectively referred to as the output regulation problem, and the desired tracking trajectory and specific interference signal are often collectively referred to as external signals.

[0068] The output regulation of the target unmanned system is generally achieved based on a model. Therefore, when performing output control on the unmanned system, it is necessary to establish a mathematical model of the controlled object (for example, using methods such as mechanism or identification models, etc.). However, as the scale and complexity of the unmanned system continue to grow, it is often difficult to obtain an accurate model of the controlled object, which will affect the construction of the output regulation controller and further affect the tracking accuracy of the position of the unmanned system during the trajectory tracking process.

[0069] To solve the above problems, there is research on a data-driven method that directly learns the control law from data. Then, using the data-driven method to solve the output regulation problem faces the following challenges:

[0070] 1. Considering the coupled noise in the collected data, there are often countless systems that match the collected data;

[0071] 2. Since the output regulation problem requires an accurate solution of the output regulation equation, in the data-driven setting, solving the output regulation equation becomes a problem of matching a finite equation solution with an infinite system model, resulting in the inability to solve the output regulation equation.

[0072] To solve the above problems, in the embodiments of the present application, an unknown unmanned aerial vehicle (UAV) system, a sensor, and an internal model system are connected. A continuous excitation control input sequence is applied to the unknown UAV system for a period of time, and the sensor output of the sensor is collected. The tracking error between the sensor output and its corresponding desired trajectory is used as the input of the internal model system, and the internal model state formed by the internal model system based on the tracking error is collected. A control gain matrix is determined based on the control input sequence, all the collected internal model states, the tracking error of the sensor output of each control input, and the filtering matrix, and an output regulation controller is output based on the control gain matrix. The unknown UAV system, the sensor, the internal model system, and the output regulation controller are connected, and the output of the unknown UAV system is regulated through the output regulation controller. In the embodiments of the present application, the control input-sensor output of the unknown UAV system collected offline and the internal model state determined based on the sensor output are used to construct an output regulation controller for feedback output regulation. Without prior identification of the physical process, the output regulation of the unknown UAV system is realized, the accuracy of the output regulation of the unmanned system is improved, and thus the tracking accuracy of the position of the unmanned system during the trajectory tracking process can be improved. At the same time, in the present application, both the process of constructing the output regulation controller by offline collection and the process of output regulation through the output regulation controller are only based on the output data of the unknown UAV system, realizing the output regulation of the unknown UAV system. Moreover, when constructing the output regulation controller of the unknown UAV system, only a low-complexity linear matrix inequality needs to be solved offline, and no optimization problem needs to be solved online. On the one hand, this solves the problem that the output regulation equation cannot be solved, and on the other hand, it reduces the computational amount of the output regulation control.

[0073] The following further describes the content of the application by describing the embodiments in conjunction with the accompanying drawings.

[0074] This embodiment provides a trajectory tracking method based on the feedback data of the UAV system, as Figure 1 shown, the method includes:

[0075] S10. Connect an unknown UAV system, a sensor, and an internal model system, apply a continuous excitation control input sequence to the unknown UAV system for a period of time, and collect the sensor output of the sensor, where the internal model system is constructed based on the evolution matrix of the external signal.

[0076] Specifically, the unknown UAV system can be a quadrotor UAV, etc. Unknown means that the mathematical model of the UAV system is unknown. For this reason, the dynamic equation of the unknown UAV system can be expressed as:

[0077] ,

[0078] wherein, is expressed as the state value of the time-unknown UAV system; denotes the control input of the time-unknown UAV system; denotes the external signal of the time-unknown UAV system; denotes the trajectory signal that the time-unknown UAV system expects to track; denotes the sensor output of the time-unknown UAV system; denotes the tracking error of the time-unknown UAV system; Matrix is an unknown real matrix of dimension is an unknown real matrix of dimension is an unknown real matrix of dimension is an unknown real matrix of dimension is an unknown real matrix of dimension; the evolution matrix of the external signal is a known real matrix of dimension, and the evolution matrix of the external signal has the real part of all its eigenvalues greater than or equal to 1; the matrix pair is observable, and the observability index is , denotes the dimension of the state value of the time-unknown UAV system, denotes the dimension of the control input of the time-unknown UAV system; denotes the dimension of the external signal of the time-unknown UAV system, denotes the dimension of the trajectory signal that the time-unknown UAV system expects to track, denotes the dimension of the sensor output collected by the sensors of the time-unknown UAV system, and the dimension of the tracking error of the time-unknown UAV system is also .

[0079] The internal model system is used for internal model compensation. Among them, the internal model system is constructed based on the evolution matrix of the external signal. The construction process of the internal model system can be:

[0080] First, after obtaining the evolution matrix of the external signal, first determine the coefficients of the minimal polynomial of the evolution matrix , and then based on construct Inner-mode matrix pair , where the mode matrix pair is expressed as:

[0081] ,

[0082] ,

[0083] where represents the identity matrix of dimension , represents the identity matrix of dimension , is the all-zero matrix of dimension , represents the coefficients of the minimal polynomial of the evolution matrix , satisfying , represents the order of the minimal polynomial of the evolution matrix, which is a positive integer.

[0084] Secondly, use the inner-mode matrix pair to construct the inner-mode state of dimension , where represents the dimension of the inner-mode state, represents the dimension of the sensor output collected by the sensor of the unknown UAV system, represents the order of the minimal polynomial of the evolution matrix, and generate an inner-mode system according to the inner-mode state. The inner-mode system can be expressed as:

[0085] ,

[0086] where represents the inner-mode state at time, represents the tracking error at time.

[0087] After constructing the inner-mode system, as shown in Figure 2 , connect the unknown UAV system, the sensor, and the inner-mode system in sequence to construct an offline data collection system, and perform offline data collection through the offline data collection system. Among them, during offline data collection, the sensor is connected to the inner-mode system, and the unknown UAV system is not connected to the output regulation controller. Specifically, during offline data collection, a randomly generated control input sequence is directly applied to the unknown UAV system, and the sensor output formed by the unknown UAV system based on each control input in the control input sequence is collected through the sensor to obtain the sensor output sequence corresponding to the control input sequence. Among them, the control input sequence includes A randomly generated control input, and the control input sequence satisfies order persistent excitation, where = , represents the dimension of the control input, represents the dimension of the sensor output, represents the observability index, which is the matrix pair in the dynamic model when observable, such that for and the rank of the target matrix reaches the state dimension of the unmanned system monomer of the minimum integer, where the target matrix is:

[0088]

[0089] where represents the target matrix, represents the transpose.

[0090] S20. Use the tracking error between the sensor output and its corresponding desired trajectory as the input of the internal model system, and collect the internal model state formed by the internal model system based on the tracking error.

[0091] Specifically, the sensor output sequence corresponds one-to-one with the control input sequence, that is, for each sensor output in the sensor output sequence, and it is the sensor output collected by the sensor when applying a control input in the control input sequence to the unknown unmanned aerial vehicle system. The tracking error corresponding to the sensor output refers to the difference value between the sensor output and the desired tracking trajectory signal. That is to say, after the sensor output is collected by the sensor, the difference between the sensor output and its corresponding desired tracking trajectory signal will be calculated to obtain the tracking error. However, the tracking error is used as the input of the internal model system, and the internal model state of the internal model system is collected.

[0092] It should be noted that since the control input sequence is applied to the unknown unmanned aerial vehicle system for a period of time, thus each time a control input is applied to the unknown unmanned aerial vehicle system, the sensor will collect the sensor output after applying the control input, and calculate the tracking error between the sensor output and its corresponding desired tracking trajectory signal, and collect the internal model state formed by the internal model system based on the tracking error. That is to say, the internal model state collection process will also last for a period of time, and multiple internal model states and the calculated tracking errors will be collected, that is, a control input sequence of persistent excitation is continuously applied to the unknown unmanned aerial vehicle system for a period of time, and all internal model states and all tracking errors during the application of the control input sequence are collected.

[0093] S30. Determine the control gain matrix based on the control input sequence, all the collected internal model states, the tracking error of the sensor output of each control input, and the filtering matrix, and output an adjustment controller based on the control gain matrix.

[0094] Specifically, the control gain matrix is used to construct an output adjustment controller, and the output control of the unknown UAV system is performed through the output adjustment controller to achieve exponential convergence when the external disturbance and the external signal of the unknown UAV system are 0, and to achieve zero-bias tracking of the sensor output with respect to the external signal when the external disturbance and the external signal are not 0.

[0095] The output adjustment controller can be:

[0096] When :

[0097] Each element in is randomly selected from ;

[0098] When :

[0099] ,

[0100] where

[0101] ,

[0102] ,

[0103] ,

[0104] ,

[0105] where represents the auxiliary variable of dimension represents the sensor output of the unknown UAV system at time represents the internal model state of the internal model system at represents the control input at represents the observable index, represents the identity matrix of dimension , and both represent parameter matrices, represents the identity matrix of dimension represents a zero matrix of all zeros represents a zero matrix of all zeros with dimension a zero matrix of all zeros represents a zero matrix of all zeros with dimension a zero matrix of all zeros represents a zero matrix of all zeros represents a zero matrix of all zeros represents a zero matrix of all zeros represents a zero matrix of all zeros

[0106] Furthermore, the control gain matrix is determined by the control gain parameters and the control input sequence obtained by solving a system of linear matrix inequalities. Among them, the system of linear matrix inequalities is a system of linear inequality equations composed of a matrix, and this inequality equation system includes control gain parameters, a tracking error augmented data matrix, a tracking error evolution augmented data matrix, and a filtering matrix. The tracking error augmented data matrix and the tracking error evolution augmented data matrix are calculated based on the control input sequence, all the collected internal model states, and the tracking error of the sensor output of each control input.

[0107] Based on this, determining the control gain matrix based on the control input sequence, all the collected internal model states, the tracking error of the sensor output of each control input, and the filtering matrix specifically includes:

[0108] Construct an input data matrix based on the control input sequence

[0109] Based on all the collected internal model states, the input data matrix, and the tracking error of the sensor output of each control input, construct the tracking error augmented data matrix and the tracking error evolution augmented data matrix of the unknown UAV system

[0110] Solve the system of linear matrix inequalities based on the tracking error augmented data matrix, the tracking error evolution augmented data matrix, and the filtering matrix to obtain the control gain parameters

[0111] Determine the control gain matrix based on the control gain parameters and the input data matrix

[0112] Specifically, the input data matrix is determined based on the control input sequence and the observable index. Among them, the input data matrix can be expressed as

[0113] ,

[0114] where represents the input data matrix ​Denote the observability index, Denote the number of control inputs.

[0115] The augmented tracking error data matrix and the augmented tracking error evolution data matrix can be respectively expressed as:

[0116] ,

[0117] ,

[0118] where, Denote the sensor output of the unknown UAV system at time Denote the internal model state of the internal model system at time Denote the control input at time Denote the tracking error at time Denote the observability index.

[0119] Furthermore, the filtering matrix is constructed based on the evolution matrix of the external signal, where the construction process of the filtering matrix can be:

[0120] First, determine the distinct real eigenvalues and distinct pairs of complex eigenvalues of the evolution matrix

[0121] Second, construct the filtering matrix distinct real eigenvalues and distinct pairs of complex eigenvalues of the evolution matrix where the filtering matrix can be expressed as:

[0122] ,

[0123] For each from 0 to it has:

[0124] ,

[0125] ,

[0126] ,

[0127] ,

[0128] where, for a positive integer , denotes the factorial of, denotes the sine function of, denotes the cosine function of, denotes real characteristic roots, denotes the multiplicity of real characteristic roots, denotes the th complex characteristic root ， denotes the th conjugate complex characteristic root of the complex characteristic root, is the real part, is the imaginary number marker, is the imaginary part, denotes a pair of complex characteristic roots the multiplicity of, and both denote intermediate variables.

[0129] Furthermore, the linear matrix inequality group is as follows:

[0130] ,

[0131] wherein, denotes the control gain parameter, denotes the inverse matrix of, denotes the augmented data matrix of the tracking error, denotes the augmented data matrix of the tracking error evolution, denotes a zero matrix of dimension , denotes the filtering matrix.

[0132] It should be noted that, denotes the variable to be solved in the linear matrix inequality group, is a positive definite real symmetric matrix of dimension, the matrix is a matrix of dimension, denotes the dimension of the sensor output collected by the sensor of the unknown UAV system, denotes the dimension of the control input of the unknown UAV system, denotes the dimension of the internal model state.

[0133] S40. Connect the unknown UAV system, the sensor, the internal model system, and the output regulation controller, and perform output regulation on the unknown UAV system through the output regulation controller.

[0134] Specifically, after obtaining the output regulation controller, asFigure 3 As shown, an unknown unmanned aerial vehicle (UAV) system, a sensor, the internal model system, and the output regulation controller are connected to form an output regulation system, and then the output of the unknown UAV system is regulated through the output regulation system. That is, the sensor output of the unknown UAV system is collected by the sensor, and the internal model state formed by taking the tracking error of the sensor output as the input is collected by the internal model system. Then, the sensor output and the internal model state are sent to the output regulation controller, and a control input is generated by the output regulation controller and fed back to the unknown UAV system to achieve the output regulation of the unknown UAV system.

[0135] Exemplarily, the output regulation of the unknown UAV system by the output regulation controller specifically includes:

[0136] Collecting the sensor output of the unknown UAV system by the sensor;

[0137] Taking the tracking error corresponding to the sensor output as the input of the internal model system and collecting the internal model state of the internal model system;

[0138] Sending the internal model state to the output regulation controller, generating a control input by the output regulation controller based on the sensor output and the internal model state, and applying the control input to the unknown UAV system to perform output regulation on the unknown UAV system.

[0139] Specifically, the sensor output is obtained by the sensor based on the state value of the unknown UAV system, where the state value is formed based on the control input and the external signal. In addition, the sensor is connected to the output regulation controller and sends the collected sensor output to the output regulation controller.

[0140] In summary, this embodiment provides a trajectory tracking method based on the feedback data of an unmanned aerial vehicle (UAV) system. The method constructs an output regulation controller through offline data collection, and then connects an unknown UAV system, sensors, an internal model system, and a controller to form a closed-loop system. The output regulation controller is used to perform output regulation on the unknown UAV system to achieve exponential convergence of the unknown UAV system when external disturbances and external signals are 0, and to achieve zero-bias tracking of the sensor output with respect to the external signal when external disturbances and external signals are not 0. Specifically, in the embodiment of the present application, an internal model system of the external signal is constructed according to the evolution matrix of the external signal, the unknown UAV system, sensors, and the internal model system are connected, and then a continuous excitation input sequence is applied to the unknown UAV system for a period of time, and the tracking error and internal model state of the unknown UAV system are collected. Using the control input, tracking error, and internal model state, the control gain matrix of the controller is obtained by solving the control method. The output regulation controller is constructed using the control gain matrix. Finally, as Figure 1 shown, the output regulation controller is connected to the unknown UAV system. At each moment, the controller side collects the sensor output sent by the unknown UAV system and the internal model state sent by the internal model system, and generates a control input based on the received sensor output and internal model state to control the unknown UAV system, thereby achieving output regulation.

[0141] At the same time, the embodiment of the present application only needs to collect offline the input-output data with noise to construct a data-based state feedback output regulation controller, realize the suppression of disturbances and zero-deviation tracking of the trajectory signal, without collecting interference and external tracking signals, and also without prior physical process identification. In this way, on the one hand, there will be no matching of countless systems with the collected data, and on the other hand, when performing output adjustment, there is no need to solve the regulation method, and there is no problem that the output regulation equation cannot be solved.

[0142] To further illustrate the effectiveness of the trajectory tracking method based on the feedback data of the UAV system provided by the embodiment of the present application, the embodiment of the present application gives a specific example of output regulation using the trajectory tracking method based on the feedback data of the UAV system provided by the present application on a quadrotor UAV system.

[0143] Specifically, on the quadrotor UAV system, the system matrix of the quadrotor UAV system is:

[0144] ,

[0145] ,

[0146] ,

[0147] ,

[0148] Among them,

[0149] ,

[0150] , for are all constants, is the dimension identity matrix of, is the dimension all-zero matrix of, is the dimension all-zero matrix of, is the dimension all matrix.

[0151] The internal model matrix pairs of the quadrotor UAV system are respectively:

[0152] ,

[0153] ,

[0154] Set the parameter T = 100. Since the matrix has a pair of simple eigenvalue pairs, then construct the filter matrix

[0155] .

[0156] Then, according to the trajectory tracking method based on the feedback data of the UAV system provided in the embodiments of the present application, output control is performed on the quadrotor UAV system, and the effect diagrams of running 150 steps as shown in Figure 4 and Figure 5 are formed. Among them, Figure 4 the four small graphs in are respectively the trajectory graphs of the sensor outputs from the first dimension to the fourth dimension and the desired tracking signals, Figure 5 the four small graphs in are the trajectory graphs of the system tracking errors from the first dimension to the fourth dimension. From Figure 4 and Figure 5 it can be seen that the system can track the external signal without deviation, that is, output regulation is achieved, indicating the effectiveness of the trajectory tracking method based on the feedback data of the UAV system provided in the embodiments of the present application.

[0157] Based on the above trajectory tracking method based on the feedback data of the UAV system, this embodiment provides a trajectory tracking system based on the output feedback data of the UAV system, as shown in Figure 3As shown, the trajectory tracking system based on the output feedback data of the UAV system includes an unknown UAV system, a sensor, an internal model system, and an output regulation controller constructed based on the output feedback data of the UAV system. The unknown UAV system, the sensor, the internal model system, and the output regulation controller are connected;

[0158] The sensor is used to collect the sensor output of the unknown UAV system;

[0159] The internal model system is used to take the tracking error between the sensor output and its corresponding desired trajectory as the input, and output the internal model state corresponding to the tracking error;

[0160] The output regulation controller is used to generate a control input based on the sensor output and the internal model state, and apply the control input to the unknown UAV system to perform output regulation on the unknown UAV system.

[0161] A trajectory tracking system based on the output feedback data of the UAV system, wherein the construction process of the output regulation controller specifically includes:

[0162] Connect the unknown UAV system, the sensor, and the internal model system, apply a continuous excitation control input sequence to the unknown UAV system for a period of time, and collect the sensor output of the sensor, wherein the internal model system is constructed based on the evolution matrix of the external signal;

[0163] Take the tracking error between the sensor output and its corresponding desired trajectory as the input of the internal model system, and collect the internal model state formed by the internal model system based on the tracking error;

[0164] Determine the control gain matrix based on the control input sequence, all the collected internal model states, the tracking error of the sensor output of each control input, and the filtering matrix, and output the regulation controller based on the control gain matrix, wherein the filtering matrix is constructed based on the evolution matrix of the external signal.

[0165] Based on the above trajectory tracking method based on the UAV system feedback data, this embodiment provides a computer-readable storage medium. The computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps in the trajectory tracking method based on the UAV system feedback data as described in the above embodiment.

[0166] Based on the above trajectory tracking method based on the UAV system feedback data, this application also provides a terminal device, such as Figure 6As shown in the figure, it includes at least one processor 20 and a memory 22, and may also include a communications interface 23 and a bus 21. Among them, the processor 20, the memory 22, and the communications interface 23 can communicate with each other through the bus 21. The communications interface 23 can transmit information. The processor 20 can call the logical instructions in the memory 22 to execute the methods in the above embodiments.

[0167] In addition, when the logical instructions in the above-mentioned memory 22 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0168] The memory 22, as a computer-readable storage medium, can be set to store software programs and computer-executable programs, such as the program instructions or modules corresponding to the methods in the embodiments of the present disclosure. The processor 20 executes functional applications and data processing by running the software programs, instructions, or modules stored in the memory 22, that is, implements the methods in the above embodiments.

[0169] The memory 22 may include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the unmanned aerial vehicle, etc. In addition, the memory 22 may include a high-speed random access memory and may also include a non-volatile memory. For example, various media that can store program codes such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs can also be transient storage media.

[0170] In addition, the specific processes of loading and executing multiple instructions by the above-mentioned storage medium and the instruction processor in the unmanned aerial vehicle have been described in detail in the above methods and will not be repeated here.

[0171] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A trajectory tracking method based on feedback data of an unmanned aerial vehicle system, characterized in that: include: Connecting an unknown UAV system, a sensor, and an internal model system, applying a control input sequence of continuous excitation to the unknown UAV system for a period of time, and collecting sensor outputs of the sensor, wherein the internal model system is constructed based on an evolution matrix of an external signal; Taking the tracking error between the sensor output and the expected trajectory corresponding to it as the input of the internal model system, and collecting the internal model state formed by the internal model system based on the tracking error; Determine a control gain matrix based on the control input sequence, all collected internal model states, tracking errors and filter matrices, and construct an output regulation controller based on the control gain matrix, wherein the filter matrix is ​​constructed based on an evolution matrix of an external signal; Connecting the unknown UAV system, the sensor, the internal model system and the output regulation controller, and performing output regulation on the unknown UAV system through the output regulation controller; The step of determining the control gain matrix based on the control input sequence, all collected internal model states, tracking errors and filter matrices specifically includes: Construct an input data matrix based on the control input sequence; Based on all the collected internal model states, the input data matrix and the tracking error, constructing a tracking error augmented data matrix and a tracking error evolution augmented data matrix of the unknown UAV system; Solving a linear matrix inequality group based on the tracking error augmented data matrix, the tracking error evolution augmented data matrix and the filter matrix to obtain a control gain parameter; Determine a control gain matrix based on the control gain parameters and the input data matrix , represents the input data matrix, represents the observable index, represents the number of control inputs, represents the control gain parameter, express The inverse matrix of .

2. The trajectory tracking method based on feedback data of an unmanned aerial vehicle system according to claim 1, characterized in that: The internal model system is expressed as: ， ， ， in, express The internal model state at the moment, express The tracking error at that moment, represents the internal model matrix pair, The dimension is The identity matrix of The dimension is The all-zero matrix, Represents the evolution matrix The coefficients of the minimal polynomial are Is a positive integer.

3. The trajectory tracking method based on feedback data of an unmanned aerial vehicle system according to claim 1, characterized in that: The control input sequence includes randomly generated control inputs, the control input sequence satisfies The continuous excitation of the order, , represents the dimension of the control input, Represents the dimension of the sensor output.

4. The trajectory tracking method based on feedback data of an unmanned aerial vehicle system according to claim 1, characterized in that: The linear matrix inequality set is: ， in, represents the tracking error augmented data matrix, represents the augmented data matrix of tracking error evolution, The dimension is The matrix of all zeros, represents the filter matrix, Represents the dimensionality of the external signal of the unknown UAV system.

5. The trajectory tracking method based on feedback data of an unmanned aerial vehicle system according to claim 1, characterized in that: The output regulation controller is specifically: when hour: Each element is randomly selected from Select, collect and record ； when hour: ， in, ， ， ， ， in, express Auxiliary variables of dimension, Indicates unknown drone system The sensor output at the moment, express The internal model state of the internal model system at time express The control input at the moment, The dimension is The identity matrix of , and They all represent parameter matrices, The dimension is The identity matrix of express The matrix of all zeros, The dimension is The matrix of all zeros, The dimension is The matrix of all zeros, express The matrix of all zeros, express The matrix of all zeros, express The matrix of all zeros, express An all-0 matrix.

6. The trajectory tracking method based on feedback data of an unmanned aerial vehicle system according to claim 1, characterized in that: The output regulation of the unknown UAV system by the output regulation controller specifically includes: collecting sensor outputs of the unknown UAV system; Taking the tracking error between the sensor output and the corresponding expected trajectory as the input of the internal model system, and collecting the internal model state of the internal model system; The internal model state is sent to the output regulation controller, and the output regulation controller generates a control input based on the sensor output and the internal model state, and the control input is applied to the unknown UAV system to perform output regulation on the unknown UAV system.

7. A trajectory tracking system based on feedback data output by an unmanned aerial vehicle system, characterized in that: Used to execute the trajectory tracking method based on UAV system feedback data as described in claim 1, the trajectory tracking system based on UAV system output feedback data includes an unknown UAV system, a sensor, an internal model system and an output regulation controller constructed based on the output feedback data of the UAV system.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps in the trajectory tracking method based on drone system feedback data as described in any one of claims 1-6.

9. A terminal device, characterized in that: include: Processor and memory; The memory stores a computer-readable program for implementing the trajectory tracking method based on feedback data of a drone system according to any one of claims 1 to 6; The processor executes the computer-readable program to perform output control on the drone.

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