CN113219824A - Dynamic system control method based on variational modal decomposition - Google Patents
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Abstract
A dynamic system control method based on variational modal decomposition solves the problems of poor control precision and poor dynamic response effect of the existing dynamic system control method, and belongs to the technical field of dynamic system control. The invention comprises the following steps: s1, establishing a kinematic equation of the dynamic system; s2, establishing a dynamic equation of the dynamic system; s3, determining the parameters of the dynamic system preliminary PID controller according to a kinematic equation and a kinetic equation, and acquiring an error signal to give an initial value for a sliding window; s3, extracting error signals of a set number of data points at the current time and before the current time by using a sliding window, acquiring integral signals and differential signals of errors, and performing variation mode decomposition processing on the error signals, the integral signals of the errors and the differential signals of the errors respectively to obtain corresponding basic mode signals of K different frequency bands for each signal; and S4, controlling the dynamic system by adopting different control forces for the basic mode signals of different frequency bands contained in the error signal.
Description
Technical Field
The invention relates to a dynamic system control method based on variational modal decomposition, and belongs to the technical field of control of dynamic systems.
Background
The application of dynamic systems such as aircrafts, seagoing vessels, land mobile machines and the like in industrial production, home service, life entertainment and military wars is increasingly wide, and particularly, the development of land, sea and air unmanned mobile platforms greatly liberates and improves the productivity. The good controller guarantees continuous and stable work of the dynamic system, not only can ensure the reliability of the system, save cost and improve economic benefit, but also can control the dynamic system to complete more complex tasks. Compared with land and water mobile platforms, the spacecraft has more flexible motion space, has wider production and living requirements in the future, and is an important development direction in a dynamic system. Unmanned aerial vehicle is the industrial product that develops the most maturity among the space vehicle, and rotor unmanned aerial vehicle is as unmanned aerial vehicle's typical representative, because its simple structure, low cost, control is simple and easy, easy maintenance is developing gradually into a comparatively general unmanned aerial vehicle platform. However, in some special situations, such as in a limited narrow working space, the working range of a dynamic system such as a rotary-wing drone can be severely affected. The good controller can compensate the environmental restriction that narrow working space brought to a certain extent, ensures rotor unmanned aerial vehicle and other dynamic system's control accuracy and adaptability, can carry out more complicated task in severer complicated operational environment.
The PID controller is the most widely applied controller on the current dynamic system, has a simple structure, is easy to realize, but has relatively low control precision and poor system robustness. The robust control has certain application in a nonlinear system and guarantees the robustness of the system to a certain extent, but the theory is more complex and the design of the controller is relatively conservative. Active Disturbance Rejection Control (ADRC) is derived from PID algorithms and modern control theory, and is a relatively popular control algorithm in recent years. The ADRC carries out estimation compensation on the total disturbance of the system, the dynamic response of the system is ensured to be good, the anti-disturbance performance of the system is considered, however, the control parameters are not easy to adjust, and the extended state observer is easily influenced by the delay of the system to cause the divergence of the system. With the development of deep neural networks, deep reinforcement learning algorithms have become popular in the control field. After the control problem is subjected to reinforcement learning modeling, good control performance can be obtained through a large amount of training data, but the training time cost and the economic cost are too high along with the increase of the complexity of the problem.
Disclosure of Invention
Aiming at the problem that the control precision and the dynamic response effect of the existing dynamic system control method are poor, the invention provides a dynamic system control method based on variational modal decomposition.
The invention discloses a dynamic control method based on variational modal decomposition, which comprises the following steps:
s1, establishing a kinematic equation of the dynamic system;
s2, establishing a dynamic equation of the dynamic system;
s3, determining the parameters of the dynamic system preliminary PID controller according to a kinematic equation and a kinetic equation, and acquiring an error signal to give an initial value for a sliding window;
s3, extracting error signals of a set number of data points at the current time and before the current time by using a sliding window, acquiring integral signals and differential signals of errors, respectively carrying out variation mode decomposition processing on the error signals, the integral signals of the errors and the differential signals of the errors, and respectively obtaining corresponding basic mode signals of K different frequency bands for each signal:
e (t) represents an error signal, integral signal where e (t) dt represents an error,
differential signal, u, representing errorek(t) denotes e (t) the corresponding fundamental mode signal, uik(t) represents the fundamental modal signal corresponding to ^ e (t) dtdk(t) watchDisplay device
Corresponding fundamental mode signal, K denotes the index of the eigenmode function component, K is 1, …, K, epsilon1、ε2And ε3Denotes the pairs e (t), (je (t) dt) and
carrying out variation modal decomposition on the corresponding residual error quantity;
s4, calculating a control force ctrl, and controlling the dynamic state according to ctrl:
k represents the number of fundamental waves of different frequency bands obtained after signal decomposition, pk、qkAnd rkRespectively represent e (t), [ integral ] e (t) dt and
control coefficients for the respective frequencies.
Preferably, K ═ 3 denotes a low frequency signal, an intermediate frequency signal, and a high frequency signal, respectively.
Preferably, in S3, the sliding window has a length of 20.
Preferably, in S1, the dynamic system is a quad-rotor drone, and the kinematic equation thereof is:
wherein theta is a pitch angle, phi is a roll angle, psi is a yaw angle, and R represents a turning matrix transformed to a world coordinate system by the unmanned aerial vehicle body coordinate system.
Preferably, in S2, the kinematic equation of the quad-rotor drone is:
wherein the content of the first and second substances,
m is the mass of the unmanned aerial vehicle; i isx、Iy、IzRespectively the rotational inertia of three axes of the unmanned aerial vehicle; x, y and z are positions of three axes of the unmanned aerial vehicle in a world coordinate system respectively; d is the distance from the center of the propeller to the origin of the coordinate system of the body; u shape1、U2、U3、U4Control input quantities of 4 motors respectively; g is an acceleration constant; l ispIs a coefficient of lift, LdAs drag coefficient, ω1、ω2、ω3And ω4Respectively the rotation angular rates of the four propellers.
Preferably, the PID controller is:
The invention has the beneficial effects that: the invention provides a new idea of controlling feedback error signals after analysis by applying a non-recursive VMD (variable Mode Decomposition) algorithm to a controller design with a time sequence by using a sliding window algorithm. The invention decomposes the error signal through the VMD algorithm, processes the sub-signals with different frequencies contained in the error signal by adopting different control forces, solves the balance problem of system control precision and dynamic response when designing the dynamic system controller under strong coupling and nonlinear conditions, and has good control precision and quick dynamic response capability.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic diagram of a quad rotor control;
FIG. 3 is a quad-rotor model;
FIG. 4 is a schematic view of a sliding window;
FIG. 5 is a flow chart of the VMD algorithm;
FIG. 6 is a VMD control quad-rotor step response curve;
FIG. 7 is an X-axis position error and modal decomposition signal;
FIG. 8 is a Y-axis position error and modal decomposition signal;
FIG. 9 is a Z-axis position error and modal decomposition signal.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The invention is further described with reference to the following drawings and specific examples, which are not intended to be limiting.
As shown in fig. 1, the dynamic system control method based on variational modal decomposition according to this embodiment may be an unmanned aerial vehicle system, and specifically includes:
firstly, modeling the kinematics of an unmanned aerial vehicle system, establishing a kinematics equation of the unmanned aerial vehicle system, determining the geometric structure and scale information of a carrier, and establishing a conversion relation between the motion state of the carrier in a body coordinate system and the motion state of the carrier in a world coordinate system.
And step two, modeling the dynamics of the unmanned aerial vehicle system, establishing a dynamics equation of the unmanned aerial vehicle system, determining dynamics parameters such as carrier mass, rotational inertia, motor coefficients and the like, and determining a mapping relation between system control variables and state variables.
Designing a preliminary PID controller, determining parameters of the preliminary PID controller of the unmanned aerial vehicle system according to a kinematic equation and a kinetic equation, and acquiring an error signal to assign a preliminary value to a sliding window;
at the initial stage of unmanned aerial vehicle system operation, guarantee through the PID controller that can remain stable at unmanned aerial vehicle system, gather error signal and fill sliding window.
Step four, designing a sliding window and a VMD algorithm, extracting error signals of a set number of data points at the current moment and before the current moment by using the sliding window, acquiring integral signals and differential signals of errors, respectively carrying out variation modal decomposition processing on the error signals, the integral signals of the errors and the differential signals of the errors, and respectively obtaining basic modal signals of corresponding K different frequency bands for each signal:
e (t) represents an error signal, integral signal where e (t) dt represents an error,
differential signal, u, representing errorek(t) denotes e (t) the corresponding fundamental mode signal, uik(t) represents the fundamental modal signal corresponding to ^ e (t) dtdk(t) represents
Corresponding fundamental mode signal, K denotes the index of the eigenmode function component, K is 1, …, K, epsilon1、ε2And ε3Denotes the pairs e (t), (je (t) dt) and
carrying out variation modal decomposition on the corresponding residual error quantity;
the VMD algorithm of the embodiment carries out variational processing on the tracking error signal of the unmanned aerial vehicle, and the tracking error signal is decomposed into a plurality of basic modes, and different modes adopt different control forces.
In the embodiment, a proper sliding window is designed aiming at the time sequence of the controller and the non-recursion of the VMD, so that the VMD algorithm can process the time sequence signal. And gradually dividing the error signal to obtain an instantaneous value of a basic mode of the error signal, and calculating the magnitude of the control force after weighting the instantaneous value of the mode. The length of the sliding window is 10-100 data points, and when the length of the window is longer, the calculation complexity is higher, and the requirement on the calculation capacity is more severe. In order to ensure the stability and the control performance of the unmanned aerial vehicle system, the length of the adopted sliding window is 20 data points. As shown in fig. 4, during the extraction, signal VMD processing is performed on the current time point including the current previous 20 error signal data points, and the current time point of the fundamental mode signal after the decomposition is used as the calculation of the control force.
Step five, calculating control force ctrl, and controlling the unmanned aerial vehicle according to ctrl:
k represents the number of fundamental waves of different frequency bands obtained after signal decomposition, pk、qkAnd rkRespectively represent e (t), [ integral ] e (t) dt and
control coefficients for the respective frequencies.
The method designs control parameters with pertinence to the error basic mode signals, and the signals with different frequencies adopt different control coefficients, so that the final output curve of the system has good dynamic performance and is smooth, and good control precision is realized.
Unmanned aerial vehicle system is four rotor unmanned aerial vehicle in this implementation, and its kinematics equation is:
where theta is the pitch angle, as shown in figure 3, theta is positive when the longitudinal axis of the quadrotor is above the horizontal plane, and negative otherwise. Phi is a roll angle, and when the four rotors tilt to the right, the phi is positive, and vice versa, when looking forward from the tail of the airplane. Psi is yaw angle, viewed in plane at psi angle, positive if the projection onto the horizontal plane from the OX axis to the longitudinal axis of the quadrotors is rotated counterclockwise, and negative otherwise. And sequentially rotating the four-rotor wing body to the world coordinate system according to the sequence of psi, theta and phi to obtain a rotation matrix R for converting the unmanned aerial vehicle body coordinate system to the world coordinate system.
In this embodiment, the dynamical equations of the quad-rotor unmanned aerial vehicle include 3 translational equations and 3 rotational equations, and the logic mapping between the system input variables and the state variables is determined specifically as follows:
wherein the content of the first and second substances,
the conversion relation between the motor control input and the rotating speed is obtained, and m is the mass of the unmanned aerial vehicle; i isx、Iy、IzRespectively the rotational inertia of three axes of the unmanned aerial vehicle; x, y and z are positions of three axes of the unmanned aerial vehicle in a world coordinate system respectively; d is the distance from the center of the propeller to the origin of the coordinate system of the body; u shape1、U2、U3、U4Control input quantities of 4 motors respectively; g is an acceleration constant; l ispIs a coefficient of lift, LdAs drag coefficient, ω1、ω2、ω3And ω4Respectively the rotation angular rates of the four propellers.
In this embodiment, the PID controller is:
And adjusting parameters of a PID controller to keep the system stable, wherein the initial value of the sliding window needs to be filled with a tracking error signal in the initial stage of the quad-rotor unmanned aerial vehicle system. And acquiring a four-rotor position tracking error signal of the system starting according to the length L of the window, filling the sliding window, and continuously updating the window subsequently. During specific operation, the data in the sliding window is subjected to variation processing, the last data of the basic mode obtained by decomposition is used for calculating the current control force,
in the present embodiment, the VMD algorithm is a variational process for the four-rotor position tracking error signal, and a flowchart of the VMD algorithm is shown in fig. 5. The VMD algorithm decomposes an original signal into a specified number of Intrinsic Mode Functions (IMFs) by constructing and solving a constraint variation problem, and determines the center frequency and the bandwidth corresponding to a basic mode of a tracking error signal by iteratively searching an optimal solution of a variation model. Converting the constraint problem into an unconstrained problem to obtain formula (5), wherein f (t) is the original signal, uk(t) is a basis function, ω, obtained by decompositionkFor each basis function uk(t), α is a penalty factor, and λ (t) is the Lagrangian multiplier. The main steps of the VMD algorithm are as shown in FIG. 5, and for K fundamental modes, the functional is updated by formula (6) and formula (7) respectively
And functional ωkAfter updating is finished, double promotion is carried out on all omega which is more than or equal to 0 through a formula (8), when an iteration finishing condition formula (9) is met, n represents iteration times, and K IMF basis functions are output.
In step five of the present embodiment, K is 3, which indicates a low frequency signal, an intermediate frequency signal, and a high frequency signal, respectively, and a low frequency signal, an intermediate frequency signal, and a high frequency signal in which errors are included in the control mode are designed as a control coefficient in the low frequency, a control coefficient in the intermediate frequency, and a control coefficient in the high frequency, respectively. The error signal, the error integral signal and the error differential signal are respectively decomposed into modal signals of 3 frequency bands, 3 control coefficients are respectively designed, and a higher frequency mode adopts a larger control coefficient. This is equivalent to further mining the internal information of the error signal, processing the signal components with different properties respectively, and ensuring the dynamic response capability of the signal and simultaneously enabling the output curve to be as smooth as possible. The final result is shown in fig. 6, in the four-rotor position control application, the designed VMD control algorithm shows good control accuracy and dynamic response effect, the steady-state error is small, the response is fast, the curve is stable, and the corresponding error signal variation results are shown in fig. 7, 8 and 9.
Although the invention herein has been described with reference to particular embodiments, it is to be understood that these embodiments are merely illustrative of the principles and applications of the present invention. It is therefore to be understood that numerous modifications may be made to the illustrative embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that features described in different dependent claims and herein may be combined in ways different from those described in the original claims. It is also to be understood that features described in connection with individual embodiments may be used in other described embodiments.
Claims (6)
1. A dynamic system control method based on variational modal decomposition is characterized by comprising the following steps:
s1, establishing a kinematic equation of the dynamic system;
s2, establishing a dynamic equation of the dynamic system;
s3, determining the parameters of the dynamic system preliminary PID controller according to a kinematic equation and a kinetic equation, and acquiring an error signal to give an initial value for a sliding window;
s3, extracting error signals of a set number of data points at the current time and before the current time by using a sliding window, acquiring integral signals and differential signals of errors, respectively carrying out variation mode decomposition processing on the error signals, the integral signals of the errors and the differential signals of the errors, and respectively obtaining corresponding basic mode signals of K different frequency bands for each signal:
e (t) represents an error signal, integral signal where e (t) dt represents an error,
differential signal, u, representing errorek(t) denotes e (t) the corresponding fundamental mode signal, uik(t) represents the fundamental modal signal corresponding to ^ e (t) dtdk(t) represents
Corresponding fundamental mode signal, K denotes the index of the eigenmode function component, K is 1, …, K, epsilon1、ε2And ε3Denotes the pairs e (t), (je (t) dt) and
carrying out variation modal decomposition on the corresponding residual error quantity;
s4, calculating a control force ctrl, and controlling the dynamic state according to ctrl:
k represents the number of fundamental waves of different frequency bands obtained after signal decomposition, pk、qkAnd rkRespectively represent e (t), [ integral ] e (t) dt and
control coefficients for the respective frequencies.
2. The dynamic system control method based on variational modal decomposition according to claim 1, wherein K-3 represents a low frequency signal, an intermediate frequency signal and a high frequency signal, respectively.
3. The dynamic system control method based on variational modal decomposition according to claim 2, wherein in S3, the sliding window length is 20.
4. The method according to claim 3, wherein in step S1, the dynamic system is a quad-rotor Unmanned Aerial Vehicle (UAV) and its kinematic equation is:
wherein theta is a pitch angle, phi is a roll angle, psi is a yaw angle, and R represents a turning matrix transformed to a world coordinate system by the unmanned aerial vehicle body coordinate system.
5. The dynamic system control method based on variational modal decomposition according to claim 4, wherein in S2, the kinetic equation of the quad-rotor drone is:
wherein the content of the first and second substances,
m is the mass of the unmanned aerial vehicle; i isx、Iy、IzRespectively the rotational inertia of three axes of the unmanned aerial vehicle; x, y and z are positions of three axes of the unmanned aerial vehicle in a world coordinate system respectively; d is the distance from the center of the propeller to the origin of the coordinate system of the body; u shape1、U2、U3、U4Control input quantities of 4 motors respectively; g is an acceleration constant; l ispIs a coefficient of lift, LdAs drag coefficient, ω1、ω2、ω3And ω4Respectively the rotation angular rates of the four propellers.
6. The dynamic system control method based on variational modal decomposition according to claim 5, wherein the PID controller is:
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160202670A1 (en) * | 2015-01-08 | 2016-07-14 | Northwestern University | System and method for sequential action control for nonlinear systems |
| CN108663202A (en) * | 2018-05-03 | 2018-10-16 | 国家电网公司 | GIS mechanical failure diagnostic methods based on chaos cuckoo algorithm and system |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| US20160202670A1 (en) * | 2015-01-08 | 2016-07-14 | Northwestern University | System and method for sequential action control for nonlinear systems |
| CN108663202A (en) * | 2018-05-03 | 2018-10-16 | 国家电网公司 | GIS mechanical failure diagnostic methods based on chaos cuckoo algorithm and system |
Non-Patent Citations (3)
| Title |
|---|
| KONSTANTIN DRAGOMIRETSKIY 等: "Variational Mode Decomposition", 《IEEE TRANSACTIONS ON SIGNAL PROCESSING》 * |
| 唐贵基等: "参数优化变分模态分解方法在滚动轴承早期故障诊断中的应用", 《西安交通大学学报》 * |
| 张梦田等: "基于变分模态分解的混合储能容量优化配置", 《储能科学与技术》 * |
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