Trajectory tracking method and device based on feedback data of unmanned aerial vehicle system

Abstract

The invention discloses a trajectory tracking method and device based on feedback data of an unmanned aerial vehicle system. The method comprises the following steps: constructing an internal model system according to an evolution matrix of an external signal; connecting an unknown unmanned aerial vehicle system, a sensor and an internal model system, directly applying control input to the unknown unmanned aerial vehicle system, taking a tracking error between output of the sensor and a trajectory expected to be tracked as input of the internal model system, and collecting an internal model state; constructing a control gain matrix by using control input, sensor output, an internal model state and a filtering matrix; and constructing an output controller by using the control gain matrix to realize system output control. According to the invention, the output adjustment controller for feedback output adjustment is constructed based on the control input-sensor output of the unknown unmanned aerial vehicle system collected offline, so that the output adjustment of the unknown unmanned aerial vehicle system is realized without identifying a physical process in advance; and zero-deviation tracking of the unmanned system in the trajectory tracking process is ensured.

Description

Technical Field

[0001] The present application relates to the field of drone control technology, and in particular to a trajectory tracking method and device based on feedback data from a drone system. Background Art

[0002] At a time when artificial intelligence is leading technological innovation, unmanned systems represented by unmanned vehicles, robots, drones, and unmanned boats have become the key application areas of artificial intelligence technology. In many applications of unmanned systems such as drone 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 collectively referred to as an output regulation problem, and the desired trajectory and specific interference signal are often collectively referred to as external signals.

[0003] The output regulation of the target unmanned system is generally implemented based on the model. Therefore, when controlling the output of the unmanned system, it is necessary to establish a mathematical model of the controlled object (for example, using a mechanism or identification model). However, as the scale and complexity of unmanned systems 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, in turn, the tracking accuracy of the position of the unmanned system during trajectory tracking.

[0004] Therefore the prior art still needs to be improved and enhanced. Summary of the invention

[0005] The technical problem to be solved by the present application is to provide a trajectory tracking method and device based on feedback data of a drone system in view of the deficiencies in the prior art.

[0006] In order to solve the above technical problems, the first aspect of the present application provides a trajectory tracking method based on feedback data of an unmanned aerial vehicle system, wherein the trajectory tracking method based on feedback data of an unmanned aerial vehicle system specifically includes: 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 of sensor outputs of each control input, and a filter matrix, and output a regulation controller based on the control gain matrix, wherein the filter matrix is ​​constructed based on an evolution matrix of an external signal; The unknown UAV system, the sensor, the internal model system and the output regulation controller are connected to each other, and the output regulation of the unknown UAV system is performed through the output regulation controller.

[0007] The trajectory tracking method based on feedback data of the UAV system, wherein 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.

[0008] The trajectory tracking method based on feedback data of the UAV system, wherein 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, Represents an observable index.

[0009] The trajectory tracking method based on feedback data of the unmanned aerial vehicle system, wherein the control gain matrix is ​​determined based on the control input sequence, all collected internal model states, the tracking error of the sensor output of each control input, and the filter matrix, specifically including: 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 of the sensor output of each control input, 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, Indicates the number of control inputs; Wherein, the linear matrix inequality group is: , in, represents the control gain parameter, express The inverse matrix of 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.

[0010] The trajectory tracking method based on feedback data of the UAV system, wherein 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, represents the observable index, 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.

[0011] In the trajectory tracking method based on feedback data of a drone system, the output regulation of the unknown drone system by the output regulation controller specifically includes: collecting sensor output of the unknown UAV system through sensors; Using 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; 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.

[0012] A second aspect of the present application provides a trajectory tracking system based on output feedback data of a drone system, wherein the trajectory tracking system based on output feedback data of a drone system comprises an unknown drone system, a sensor, an internal model system, and an output regulation controller constructed based on the output feedback data of the drone system, wherein the unknown drone system, the sensor, the internal model system, and the output regulation controller are connected; The sensor is used to collect sensor output of the unknown UAV system; The internal model system is used to take the tracking error between the sensor output and the corresponding desired trajectory as input, and output the internal model state corresponding to the tracking error; 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.

[0013] The trajectory tracking system based on the output feedback data of the UAV system, wherein the construction process of the output regulation controller specifically includes: 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; A control gain matrix is ​​determined based on the control input sequence, all collected internal model states, the tracking error of the sensor output of each control input and a filter matrix, and a controller is adjusted based on the control gain matrix output, wherein the filter matrix is ​​constructed based on an evolution matrix of an external signal.

[0014] A third aspect of the present application provides a computer-readable storage medium, which stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps in any of the above-described trajectory tracking methods based on feedback data from a drone system.

[0015] A fourth aspect of the present application provides a terminal device, comprising: a processor and a memory; The memory stores a computer-readable program for implementing the trajectory tracking method based on feedback data of the drone system as described above; The processor executes the computer-readable program to perform output control on the drone.

[0016] Beneficial effects: 1. This application collects the control input-sensor output of an unknown UAV system offline, and constructs an output regulation controller for feedback output regulation based on the internal model state determined by the sensor output. This achieves output regulation of the unknown UAV system without prior identification of the physical process, improves the accuracy of the output regulation of the unmanned system, and thus improves the tracking accuracy of the unmanned system during trajectory tracking.

[0017] 2. The process of constructing an output regulation controller through offline collection and the process of performing output regulation through the output regulation controller in this application both realize output regulation of an unknown UAV system only through the output data of the unknown UAV system.

[0018] 3. When constructing an output regulation controller for an unknown UAV system, this application only needs to solve a low-complexity linear matrix inequality group offline. There is no need to solve the optimization problem online, which reduces the computational complexity of the output regulation control. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 A flowchart of a trajectory tracking method based on feedback data from an unmanned aerial vehicle system provided in an embodiment of the present application.

[0021] Figure 2 Schematic diagram of the offline data collection principle.

[0022] Figure 3 This is a schematic diagram of the principle architecture of the trajectory tracking system based on the feedback data of the UAV system.

[0023] Figure 4 Figure 2. Sensor outputs from the first dimension to the fourth dimension of the quadrotor drone model and the trajectory diagram of the desired tracking.

[0024] Figure 5 Trajectory diagram of the system tracking error from the first dimension to the fourth dimension of the quadrotor drone model.

[0025] Figure 6 This is a diagram showing the structural principle architecture of the drone provided in the embodiments of the present application. DETAILED DESCRIPTION

[0026] The embodiment of the present application provides a trajectory tracking method and device based on feedback data of a drone system. In order to make the purpose, technical solution and effect of the present application clearer and more specific, the present application is further described in detail with reference to the accompanying drawings and 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.

[0027] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "one", "said", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present application refers to the presence of the 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 groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be an intermediate element. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The term "and / or" used herein includes all or any unit and all combinations of one or more associated listed items.

[0028] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art to which this application belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with those in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined as here.

[0029] It should be understood that the sequence numbers and sizes of the steps in this embodiment do not mean the order of execution. The execution order of each process is determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.

[0030] After research, it is found that at a time when artificial intelligence leads technological innovation, unmanned systems represented by unmanned vehicles, robots, drones, and unmanned boats have become the key application areas of artificial intelligence technology. In many applications of unmanned systems such as drone 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 collectively referred to as an output regulation problem, and the desired trajectory and specific interference signal are often collectively referred to as external signals.

[0031] The output regulation of the target unmanned system is generally implemented based on the model. Therefore, when controlling the output of the unmanned system, it is necessary to establish a mathematical model of the controlled object (for example, using a mechanism or identification model). However, as the scale and complexity of unmanned systems 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, in turn, the tracking accuracy of the position of the unmanned system during trajectory tracking.

[0032] In order to solve the above problems, some studies have used data-driven methods to directly learn the control rate from data. Then, using data-driven methods to solve the output regulation problem faces the following challenges: 1. Considering the coupled noise in the collected data, there are often countless systems matching the collected data; 2. Since the output regulation problem requires the precise solution of the output regulation equation, under the data-driven setting, solving the output regulation equation becomes a problem of matching the finite equation solution with the infinite system model, which makes the output regulation equation impossible to solve.

[0033] In order to solve the above problems, in an embodiment of the present application, an unknown unmanned aerial vehicle system, a sensor and an internal model system are connected, a control input sequence of continuous excitation is applied to the unknown unmanned aerial vehicle system for a period of time, and the sensor output of the sensor is collected; the tracking error between the sensor output and the corresponding expected 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 collected internal model states, the tracking error of the sensor output of each control input and the filter matrix, and an output adjustment controller is output based on the control gain matrix; the unknown unmanned aerial vehicle system, the sensor, the internal model system and the output adjustment controller are connected, and the output adjustment controller is used to adjust the output of the unknown unmanned aerial vehicle system. The embodiment of the present application constructs an output adjustment controller for feedback output adjustment by collecting the control input-sensor output of the unknown unmanned aerial vehicle system offline and based on the internal model state determined by the sensor output, so as to realize the output adjustment of the unknown unmanned aerial vehicle system without the need for pre-identification of the physical process, and improve the accuracy of the output adjustment of the unmanned aerial vehicle system, thereby improving the tracking accuracy of the position of the unmanned aerial vehicle system during the trajectory tracking process. At the same time, the process of constructing an output regulation controller through offline collection and the process of output regulation through the output regulation controller in this application both use only the output data of the unknown UAV system to achieve 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 there is no need to solve the optimization problem online. This solves the problem that the output regulation equation cannot be solved on the one hand, and reduces the amount of calculation of the output regulation control on the other hand.

[0034] The application content is further explained below through the description of embodiments in conjunction with the accompanying drawings.

[0035] This embodiment provides a trajectory tracking method based on feedback data from a drone system, such as Figure 1 As shown, the method includes: S10, connecting the unknown UAV system, the sensor and the internal model system, applying a control input sequence of continuous excitation to the unknown UAV system for a period of time, and collecting the sensor output of the sensor, wherein the internal model system is constructed based on the evolution matrix of the external signal.

[0036] Specifically, the unknown UAV system can be a quad-rotor UAV, etc., and the unknown refers to that the mathematical model of the UAV system is unknown. Therefore, the dynamic equation of the unknown UAV system can be expressed as: , in, Expressed as The state value of the UAV system is unknown at any moment; express The control input of the UAV system is unknown at all times; express External signals from the drone system are unknown at all times; express The desired tracking trajectory signal of the unknown UAV system at the time; express The sensor output of the UAV system is unknown at any given moment; express Tracking error of the unknown UAV system at the time; matrix yes unknown real matrix of dimension; matrix yes unknown real matrix of dimension; matrix yes unknown real matrix of dimension; matrix yes unknown real matrix of dimension; matrix yes Unknown real matrix of dimension; evolution matrix of external signal yes dimensional known real matrix, and the evolution matrix of the external signal The real part of all characteristic roots is greater than or equal to 1; the matrix Observable, and the observable index is , The dimension representing the state value of the unknown UAV system, Represents the dimensionality of the control inputs to the unknown UAV system; represents the dimension of the external signal of the unknown UAV system, represents the dimension of the trajectory signal of the desired tracking of the unknown UAV system, represents the dimension of the sensor output collected by the sensor of the unknown UAV system, and the dimension of the tracking error of the unknown UAV system is also .

[0037] The internal model system is used for internal model compensation, wherein the internal model system is constructed based on the evolution matrix of the external signal, and the construction process of the internal model system can be: First, after obtaining the evolution matrix of the external signal Then, first determine the evolution matrix The coefficients of the minimal polynomial , then based on Build IMM , where the module matrix is It is expressed as: , , in, The dimension is The identity matrix of The dimension is The identity matrix of The dimension is The all-zero matrix, Represents the evolution matrix The coefficients of the minimal polynomial satisfy , represents the order of the minimal polynomial of the evolution matrix, which is a positive integer.

[0038] Secondly, using the internal model matrix The construction dimension is Internal model status ,in, represents the dimension of the internal model state, represents the dimensionality of the sensor outputs collected by the sensors of the unknown UAV system, The order of the minimum polynomial of the evolution matrix is ​​represented, and an internal model system is generated according to the internal model state. The internal model system can be expressed as: , in, express The internal model state at the moment, express Tracking error at that moment.

[0039] After constructing the internal model system, Figure 2 As shown, the unknown UAV system, the sensor and the internal model system are connected in sequence to construct an offline data collection system, and offline data collection is performed through the offline data collection system, wherein during offline data collection, the sensor is connected to the internal model system, and the unknown UAV system is not connected to the output regulation controller. Specifically, when performing 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 a sensor output sequence corresponding to the control input sequence. 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, represents the observable index, which is a matrix pair in the dynamic model When it is considerable, it makes and The rank of the target matrix reaches the unmanned system monomer Status Dimension The smallest integer of , where the target matrix is: , in, represents the target matrix, Indicates transpose.

[0040] S20, 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 formed by the internal model system based on the tracking error.

[0041] Specifically, the sensor output sequence corresponds to the control input sequence one by one, that is, for each sensor output in the sensor output sequence, it is the sensor output collected by the sensor when one of the control inputs in the control input sequence is applied to the unknown UAV system. The tracking error corresponding to the sensor output refers to the difference between the sensor output and the desired tracking trajectory signal, that is, after the sensor output is collected by the sensor, the difference between the sensor output and the corresponding desired tracking trajectory signal is calculated to obtain the tracking error. However, the tracking error is used as the input of the internal model system to collect the internal model state of the internal model system.

[0042] It should be noted that, since a control input sequence is applied to the unknown UAV system for a period of time, each time a control input is applied to the unknown UAV system, the sensor will collect the sensor output after the control input is applied, and calculate the tracking error between the sensor output and the corresponding desired tracking trajectory signal, and collect the internal model state formed by the internal model system based on the tracking error. In other words, the internal model state collection process will also last for a period of time, and multiple internal model states and calculated tracking errors will be collected, that is, a control input sequence of continuous excitation is applied to the unknown UAV system for a period of time, and all internal model states and all tracking errors in the process of applying the control input sequence are collected.

[0043] S30, determining a control gain matrix based on the control input sequence, all collected internal model states, the tracking error of the sensor output of each control input, and a filter matrix, and outputting an adjustment controller based on the control gain matrix.

[0044] Specifically, the control gain matrix is ​​used to construct an output regulation controller, through which the output of the unknown UAV system is controlled to achieve exponential convergence of the unknown UAV system when the external disturbance and the external signal are 0, and to achieve zero bias tracking of the sensor output for the external signal when the external disturbance and the external signal are not 0.

[0045] The output regulation controller may be: 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, represents the observable index, 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.

[0046] Furthermore, the control gain matrix is ​​determined by solving a linear matrix inequality group to determine the control gain parameters and the control input sequence, wherein the linear matrix inequality group is a linear inequality equation group composed of a matrix, and the inequality equation group includes control gain parameters, a tracking error augmented data matrix, a tracking error evolution augmented data matrix and a filter matrix, and the tracking error augmented data matrix and the tracking error evolution augmented data matrix are calculated based on the control input sequence, all collected internal model states and the tracking error of the sensor output of each control input.

[0047] Based on this, the control gain matrix is ​​determined based on the control input sequence, all collected internal model states, the tracking error of the sensor output of each control input, and the filter matrix, specifically including: 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 of the sensor output of each control input, 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 .

[0048] Specifically, the input data matrix is ​​determined based on the control input sequence and the observable index, wherein the input data matrix can be expressed as: , in, represents the input data matrix, represents the observable index, Indicates the number of control inputs.

[0049] The tracking error augmented data matrix and the tracking error evolution augmented data matrix can be expressed as: , , in, 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, express The tracking error at that moment, Represents an observable index.

[0050] Furthermore, the filter matrix is ​​constructed based on the evolution matrix of the external signal, wherein the construction process of the filter matrix can be: First, determine the evolution matrix of different real characteristic roots and different pairs of complex eigenvalues; Secondly, according to the evolution matrix of different real characteristic roots and Construct the filter matrix using different complex eigenvalue pairs , where the filter matrix It can be expressed as: , For the range from 0 to Each of have: , , , , Among them, for a positive integer , express The factorial of express The sine function of express The cosine function of represents the real characteristic root, represents the multiplicity of real characteristic roots, Indicates Complex characteristic roots ， Indicates The conjugate complex characteristic root of complex characteristic roots, is the real part, is the imaginary number sign, is the imaginary part, represents the complex characteristic root pair The multiplicity of and Both represent intermediate variables.

[0051] Further, the linear matrix inequality group is: , in, represents the control gain parameter, express The inverse matrix of 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.

[0052] It should be noted that is represented as the variables to be solved in the linear matrix inequality system, for A positive definite real symmetric matrix of dimension for A matrix of dimension, represents the dimensionality of the sensor outputs collected by the sensors of the unknown UAV system, represents the dimension of the control input of the unknown UAV system, Dimension representing the state of the internal model.

[0053] S40, 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.

[0054] Specifically, after obtaining the output regulation controller, such as Figure 3 As shown, the unknown UAV system, the sensor, the internal model system and the output regulation controller are connected to form an output regulation system, and then the output regulation system is used to adjust the output of the unknown UAV system. That is, the sensor output of the unknown UAV system is collected by the sensor, and the internal model state formed by the tracking error of the sensor output is collected by the internal model system, and then the sensor output and the internal model state are sent to the output regulation controller, and the control input is generated by the output regulation controller, and the control input is fed back to the unknown UAV system to achieve output regulation of the unknown UAV system.

[0055] Exemplarily, the output regulation of the unknown UAV system by the output regulation controller specifically includes: collecting sensor output of the unknown UAV system through sensors; Using 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; 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.

[0056] Specifically, the sensor output is obtained by the sensor based on the state value of the unknown UAV system, wherein 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 sensor output collected by the sensor to the output regulation controller.

[0057] In summary, the present embodiment provides a trajectory tracking method based on feedback data of a UAV system, wherein the method constructs an output regulation controller through offline data collection, and then connects an unknown UAV system, a sensor, an internal model system and a controller to form a closed-loop system, and adjusts the output of the unknown UAV system through the output regulation controller to achieve exponential convergence of the unknown UAV system when the external disturbance and the external signal are 0, and achieve zero-bias tracking of the sensor output for the external signal when the external disturbance and the external signal are not 0. Specifically, the embodiment of the present application constructs an internal model system of an external signal according to the evolution matrix of the external signal, connects the unknown UAV system, the sensor and the internal model system, and then applies a continuous excitation input sequence to the unknown UAV system for a period of time, and collects the tracking error and internal model state of the unknown UAV system. 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 As shown, the output regulation controller is connected to the unknown UAV system. The controller collects the sensor output sent by the unknown UAV system and the internal model state sent by the internal model system at every moment, and generates control input based on the received sensor output and internal model state to control the unknown UAV system, thereby achieving output regulation.

[0058] At the same time, the embodiments of the present application only need to collect noisy input-output data offline to construct a data-based state feedback output regulation controller to achieve disturbance suppression and zero-deviation tracking of trajectory signals. There is no need to collect interference and external tracking signals, and there is no need for prior physical process identification. In this way, on the one hand, there will not be countless systems to match the collected data, and on the other hand, there is no need to solve the regulation method when adjusting the output, and the output regulation equation cannot be solved.

[0059] In order to further illustrate the effectiveness of the trajectory tracking method based on the feedback data of the drone system provided in the embodiment of the present application, the embodiment of the present application provides a specific example of using the trajectory tracking method based on the feedback data of the drone system provided in the present application to perform output adjustment on a quadrotor drone system.

[0060] Specifically, on a quad-rotor drone system, the system matrix of the quad-rotor drone system is: , , , , in, , ,for are constants, For Dimension The identity matrix of For Dimension The all-zero matrix, For Dimension The all-zero matrix, For Dimension All matrix.

[0061] Internal model matrix of quadrotor UAV system They are: , , Set the parameter T=100. The matrix has a pair of single characteristic root pairs, so construct the filter matrix .

[0062] Then, according to the trajectory tracking method based on the feedback data of the drone system provided in the embodiment of the present application, the output of the quad-rotor drone system is controlled, and the following is formed: Figure 4 and Figure 5 The effect diagram of running 150 steps is shown, where Figure 4 The four small figures in the figure are the sensor outputs from the first dimension to the fourth dimension and the trajectory diagrams of the expected tracking signal. Figure 5 The four small figures in the figure are the trajectory diagrams of the system tracking error from the first dimension to the fourth dimension. Figure 4 and Figure 5 It can be seen that the system is able to track the external signal without deviation, that is, output regulation is achieved, which shows the effectiveness of the trajectory tracking method based on the feedback data of the drone system provided in the embodiment of the present application.

[0063] Based on the above-mentioned trajectory tracking method based on feedback data of the UAV system, this embodiment provides a trajectory tracking system based on feedback data output by the UAV system, such as 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, and the unknown UAV system, the sensor, the internal model system, and the output regulation controller are connected; The sensor is used to collect sensor output of the unknown UAV system; The internal model system is used to take the tracking error between the sensor output and the corresponding desired trajectory as input, and output the internal model state corresponding to the tracking error; 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.

[0064] The trajectory tracking system based on the output feedback data of the UAV system, wherein the construction process of the output regulation controller specifically includes: 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; A control gain matrix is ​​determined based on the control input sequence, all collected internal model states, the tracking error of the sensor output of each control input and a filter matrix, and a controller is adjusted based on the control gain matrix output, wherein the filter matrix is ​​constructed based on an evolution matrix of an external signal.

[0065] Based on the above-mentioned trajectory tracking method based on feedback data from a drone system, this embodiment provides a computer-readable storage medium, which stores one or more programs. The one or more programs can be executed by one or more processors to implement the steps in the trajectory tracking method based on feedback data from a drone system as described in the above-mentioned embodiment.

[0066] Based on the above trajectory tracking method based on feedback data of the UAV system, the present application also provides a terminal device, such as Figure 6As shown, it includes at least one processor 20 and a memory 22, and may also include a communications interface 23 and a bus 21. The processor 20, the memory 22 and the communications interface 23 may communicate with each other through the bus 21. The communications interface 23 may transmit information. The processor 20 may call the logic instructions in the memory 22 to execute the method in the above embodiment.

[0067] In addition, the logic instructions in the memory 22 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product.

[0068] The memory 22 is a computer-readable storage medium that can be configured to store software programs, computer executable programs, such as 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, implementing the methods in the above embodiments.

[0069] The memory 22 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the drone, etc. In addition, the memory 22 may include a high-speed random access memory and may also include a non-volatile memory. For example, a variety of media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, may also be a transient storage medium.

[0070] In addition, the specific process of loading and executing the above-mentioned storage medium and the multiple instruction processors in the drone has been described in detail in the above-mentioned method and will not be described one by one here.

[0071] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions 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: The trajectory tracking method based on the feedback data of the UAV system specifically includes: 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 of sensor outputs of each control input, and a filter matrix, and output a regulation controller based on the control gain matrix, wherein the filter matrix is ​​constructed based on an evolution matrix of an external signal; The unknown UAV system, the sensor, the internal model system and the output regulation controller are connected to each other, and the output regulation of the unknown UAV system is performed through the output regulation controller.

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, Represents an observable index.

4. The trajectory tracking method based on feedback data of an unmanned aerial vehicle system according to claim 1, characterized in that: The control gain matrix is ​​determined based on the control input sequence, all collected internal model states, the tracking error of the sensor output of each control input, and the filter matrix, specifically including: 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 of the sensor output of each control input, 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, Indicates the number of control inputs; Wherein, the linear matrix inequality group is: ， in, represents the control gain parameter, express The inverse matrix of 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.

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, represents the observable index, 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 output of the unknown UAV system through sensors; Using 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; 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: The trajectory tracking system based on the output feedback data of the UAV system comprises 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, wherein the unknown UAV system, the sensor, the internal model system and the output regulation controller are connected; The sensor is used to collect sensor output of the unknown UAV system; The internal model system is used to take the tracking error between the sensor output and the corresponding desired trajectory as input, and output the internal model state corresponding to the tracking error; 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.

8. The trajectory tracking system based on the output feedback data of the UAV system according to claim 7 is characterized in that: The construction process of the output regulation controller specifically includes: 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; A control gain matrix is ​​determined based on the control input sequence, all collected internal model states, the tracking error of the sensor output of each control input and a filter matrix, and a controller is adjusted based on the control gain matrix output, wherein the filter matrix is ​​constructed based on an evolution matrix of an external signal.

9. 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.

10. 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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