CN113093782A - Unmanned aerial vehicle designated performance attitude control method and system - Google Patents

Unmanned aerial vehicle designated performance attitude control method and system Download PDF

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CN113093782A
CN113093782A CN202110390631.9A CN202110390631A CN113093782A CN 113093782 A CN113093782 A CN 113093782A CN 202110390631 A CN202110390631 A CN 202110390631A CN 113093782 A CN113093782 A CN 113093782A
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error
aerial vehicle
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state
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CN113093782B (en
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任鸿儒
邱俊豪
鲁仁全
李鸿一
马慧
刘洋
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Guangdong University of Technology
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/08—Control of attitude, i.e. control of roll, pitch, or yaw
    • G05D1/0808—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft
    • G05D1/0816—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft to ensure stability
    • G05D1/0833—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft to ensure stability using limited authority control

Abstract

The invention relates to a method and a system for controlling the designated performance attitude of an unmanned aerial vehicle, which comprises the following steps: establishing a dynamic model for the attitude physical characteristics of the unmanned aerial vehicle; establishing a state observer system model according to the dynamic model, and converting the observer system model into an unconstrained state model; obtaining an error conversion model according to a set designated performance model, and obtaining an error system model according to an unconstrained state model and the error conversion model; the Lyapunov function is determined according to the error system model, and the self-adaptive rate and the control law are obtained through solving.

Description

Unmanned aerial vehicle designated performance attitude control method and system
Technical Field
The invention relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle designated performance attitude control method and system.
Background
The statements herein merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Quad-rotor unmanned aerial vehicle is because its small and exquisite organism, nimble operation, cheap cost and stronger environmental suitability, therefore no matter in civilian or military aspect, quad-rotor unmanned aerial vehicle all has extensive application scene. Wherein, to the high-rise problem of putting out a fire, if can utilize four rotor unmanned aerial vehicle to carry out the condition of a fire investigation, help fire fighter knows the inside condition rapidly, makes accurate judgement. However, in a complex fire scene environment, the actuators of quad-rotor drones are more susceptible to environmental impact, resulting in flight instability. Therefore, the attitude designated performance control of the quadrotor unmanned aerial vehicle considering the faults of the actuator has very important practical significance.
In recent years, attitude control of quad-rotor drones with actuator failure has received wide attention and has achieved a number of important research efforts. The inventor finds that the solved control law is difficult to enable the system to be fast and stable and the tracking accuracy is not high due to the problem of actuator faults of the attitude system of the quad-rotor unmanned aerial vehicle.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a method for controlling the designated performance attitude of an unmanned aerial vehicle, which can improve the tracking accuracy.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, an embodiment of the present invention provides a method for controlling an attitude of an unmanned aerial vehicle, including the following steps:
establishing a dynamic model for the attitude physical characteristics of the unmanned aerial vehicle;
establishing a state observer system model according to the dynamic model, and converting the observer system model into an unconstrained state model;
obtaining an error conversion model according to a set designated performance model, and obtaining an error system model according to an unconstrained state model and the error conversion model;
and determining a Lyapunov function according to the error system model, and solving to obtain the self-adaptive rate and the control law.
Optionally, a dynamic model of the unmanned aerial vehicle is established based on the inertial coordinate system and the body coordinate system.
Optionally, the dynamic model is converted into a state model, the state model is disassembled into a position subsystem model and an angle subsystem model, the position subsystem model, the angle subsystem model and the actuator fault model are combined for conversion, and a corresponding observer system model is established for each converted position subsystem model and angle subsystem model.
Optionally, the observer system model is converted into an unconstrained state model based on state information fed back by the observer system model.
Optionally, a nonlinear mapping technology is introduced, and the observer system model is converted into an unconstrained state model based on state information fed back by the observer system model.
Optionally, a Nussbaum function is used for compensating actuator faults and a neural network is used for approximating a system nonlinear function, and the adaptive rate and the control rate are solved.
Optionally, the error system model is obtained by deriving the unconstrained state model and the error conversion model.
In a second aspect, an embodiment of the present invention provides an unmanned aerial vehicle performance-specifying attitude control system, including:
a modeling module: the method is used for establishing a dynamic model for the attitude physical characteristics of the unmanned aerial vehicle;
an unconstrained state model obtaining module: the observer system model is used for establishing a state observer system model according to the dynamic model and converting the observer system model into an unconstrained state model;
an error system model acquisition module: and the system model is used for obtaining an error conversion model according to the set specified performance model and obtaining an error system model according to the unconstrained state model and the error conversion model.
A control rate acquisition module: and the method is used for determining the Lyapunov function according to the error system model and solving to obtain the self-adaptive rate and the control law.
In a third aspect, an embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the method for controlling the designated performance attitude of the drone according to the first aspect.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the drone specification performance attitude control method of the first aspect.
The invention has the beneficial effects that:
1. according to the method, the state constraint problem of the original four-rotor unmanned aerial vehicle system is converted into the traditional unconstrained control problem by establishing an unconstrained state model; and the system meets the preset performance index by introducing a specified performance model. Not only can increase unmanned aerial vehicle system's flexibility to make system tracking error converge to the small enough scope as desired through appointing the performance model, thereby improve the tracking accuracy.
2. The method of the invention compensates the actuator failure by using the Nussbaum technique to deal with the unknown gain problem. Compared with the general control algorithm design, the failure of the actuator is considered to be more consistent with the actual system and environment, and the method has practical significance.
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The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
FIG. 1 is a flowchart of a method of example 1 of the present invention;
fig. 2 is a schematic view of a dynamic model of an unmanned aerial vehicle according to embodiment 1 of the present invention;
FIG. 3 is a first diagram illustrating a tracking effect according to embodiment 1 of the present invention;
FIG. 4 is a second schematic diagram illustrating a tracking effect in embodiment 1 of the present invention;
FIG. 5 is a third schematic view of the tracking effect of embodiment 1 of the present invention;
Detailed Description
Example 1
The embodiment discloses a method for controlling the designated performance attitude of an unmanned aerial vehicle, which comprises the following steps as shown in fig. 1:
step 1: establishing dynamic model for attitude physical characteristics of unmanned aerial vehicle
This four rotor unmanned aerial vehicle of implementing comprises the horn of a pair of fixed and strict symmetry in alternately, and four rotor centers are the organism focus, and on four motors on the horn were fixed respectively to four rotors, four rotors were located the coplanar. The structure is shown in fig. 2. Based on OeXeYeZeInertial frame and ObXbYbZbAnd establishing a four-rotor unmanned aerial vehicle model according to the body coordinate system:
Figure BDA0003016579630000041
wherein x, y and z represent coordinates of the centroid of the quad-rotor unmanned aerial vehicle in an inertial coordinate system, phi, theta and psi are respectively represented as a roll angle, a pitch angle and a yaw angle of the quad-rotor unmanned aerial vehicle, m represents mass, l represents distance from the center of a propeller to the center of gravity, g represents gravity acceleration, and I represents gravity accelerationxx,Iyy,IzzRepresenting the moment of inertia of the drone about the x, y, z axes relative to the coordinate system of the body, JpRepresenting the propeller moment of inertia, omegarRepresenting the propeller speed margin with a controller input of ui(i＝z,φ,θ,ψ)。
Then, the dynamics model of the quad-rotor drone may be further transformed into a state model:
Figure BDA0003016579630000051
the above state models can be decomposed into an x, y, z position subsystem model and a phi, theta, psi angle subsystem model.
Figure BDA0003016579630000052
Wherein, ax＝ay＝0，
Figure BDA0003016579630000053
Figure BDA0003016579630000054
For actuator ui(i ═ x, y, z, Φ, θ, ψ), the actuator failure model is as follows:
Figure BDA0003016579630000055
wherein u isi(t) represents the output of the actual controller, uif(t) represents the input to the controller. biIndicating an unknown control direction.
Figure BDA0003016579630000061
Indicative of an additive failure of the controller and is a bounded unknown non-linear function. 0<si<1 denotes the unknown fault index of the controller.
For the convenience of subsequent observer system model design, the position subsystem model, the angle subsystem model and the actuator fault model are combined to perform the following conversion:
Figure BDA0003016579630000062
wherein, betai＝bi(1-si)βiIs the fault gain parameter of the actuator.
Figure BDA0003016579630000063
After transformation, the unknown gain portions of the actuator faults of each position subsystem model and angle subsystem model are separated.
Under the building environment of high-rise buildings, the posture of the quad-rotor unmanned aerial vehicle cannot be changed by a large margin due to environmental limitation. In addition, the complex fire scene environment can also make unmanned aerial vehicle's executor and each sensor module receive the influence and easily break down. The unmanned aerial vehicle is easy to fly and destabilize due to actuator faults, the system cannot obtain accurate coordinates and angle parameters due to sensor faults, and the unmanned aerial vehicle is easy to deviate from a preset flying track or even crash due to the use of the parameters with large deviation. Therefore, the control targets of the present embodiment are: under the condition that actuator faults and unmanned aerial vehicle state parameters are not measurable, the output of the four-rotor unmanned aerial vehicle system can track given signals, meanwhile, performance indexes meet preset requirements, and it is guaranteed that all signals of the system are consistent and finally bounded.
Step 2: aiming at the problem that the state parameters of the unmanned aerial vehicle are not measurable, a corresponding observer system model is established for the position subsystem model and the angle subsystem model after the coordinate conversion, and the observer system model can enable the observer state to track each corresponding state and angle parameter of the unmanned aerial vehicle and feed back the state and angle parameter in real time.
Introducing a state observer technology, and designing an observer system model as follows:
Figure BDA0003016579630000071
wherein
Figure BDA0003016579630000072
And
Figure BDA0003016579630000073
in order to observe the signal of the observer system,
Figure BDA0003016579630000074
observe and track the corresponding χ1iThe signal(s) is (are) transmitted,
Figure BDA0003016579630000075
observe and track the corresponding χ2iThe signal(s) is (are) transmitted,
Figure BDA0003016579630000076
to observe errors,/1iAnd l2iTo design the parameters and to satisfy the Helvelts polynomial p(s) ═ s2+l1is+l2i(i ═ x, y, z, Φ, θ, ψ). The design principle of the observer is as follows: using the difference between the outputs of the position and angle subsystem models and the observer system model (i.e., the observation error e)1i) And (4) making a feedback signal so as to continuously adjust each observation state of the observer system model and enable the observer system model to track each state and angle signal of the unmanned aerial vehicle system.
And step 3: and on the basis of state information fed back by the observer system model, converting the observer system into an unconstrained system by utilizing nonlinear mapping.
In order to solve the problem of limited flight attitude of the unmanned aerial vehicle, a nonlinear mapping technology is introduced, and the following coordinate transformation is carried out to convert an observer system model into an unconstrained state model:
Figure BDA0003016579630000077
in the formula, s2iIs composed of
Figure BDA00030165796300000716
The state of the device after the conversion is completed,
Figure BDA0003016579630000078
is composed of
Figure BDA0003016579630000079
The upper bound of the constraint of (2),
Figure BDA00030165796300000710
is composed of
Figure BDA00030165796300000711
Is lower bound. As can be obtained from the above formula, the,
Figure BDA00030165796300000712
is constrained at
Figure BDA00030165796300000713
I.e. the corresponding original signal x2iIs constrained at
Figure BDA00030165796300000714
Within the range of (
Figure BDA00030165796300000715
Is betaiUpper bound of, Δ2iIs e2iUpper bound of observation error) betaiFurthermore, the design may constrain x due to the next step's specified performance1iUpper and lower bounds of (1), therefore, this step is not aligned with x1iAnd (6) carrying out constraint.
And 4, step 4: obtaining an error conversion model according to a set designated performance model, and obtaining an error system model according to an unconstrained state model and the error conversion model;
and designing a designated performance model to enable the system to meet the preset performance index.
The performance model is specified as follows:
Figure BDA0003016579630000081
wherein the content of the first and second substances,
Figure BDA0003016579630000082
ζ,n,μ∞,tfis a positive design parameter. Zeta and mu∞Is chosen as the maximum allowable initial error, mu∞Is selected as the maximum steady state error allowed, n is selected as the desired convergence speed, tfChosen as the maximum convergence time allowed (after which the constrained tracking error is constrained to (-mu)∞,μ∞) In between). t is t0For the initial moment of operation of the system (t is here designed)00), t is the system operating time, and μ (t) is the designed specified performance model.
The error conversion model is obtained according to the specified performance model as follows:
Figure BDA0003016579630000083
Figure BDA0003016579630000084
ydiis given as a tracking target, ydiThe method comprises the steps that a given target tracking track is designed to be a known function or a constant value and used for simulating an expected flight path of the unmanned aerial vehicle, the unmanned aerial vehicle flies according to the track by the given target track, and a performance model and an error conversion model are designed to enable x1iBound at (-zeta-mu)∞,ζ+μ∞). And (3) carrying out derivation on the error conversion model and the unconstrained state model of the previous step, wherein the obtained error model is as follows:
Figure BDA0003016579630000085
wherein the content of the first and second substances,
Figure BDA0003016579630000086
z2ifor a virtual error signal, w2iIs the output signal of the filter, alpha1iIs an input signal of the filter while being a virtual control signal, lambda2iIs the filtering error.
In this embodiment, the error transformation model aims to constrain the tracking error
Figure BDA0003016579630000087
(restricted range (-zeta-mu)∞,ζ+μ∞) Mapping to unconstrained error ξ)1i(t) (its range (- ∞, + ∞)) and the purpose of the error model here is to derive all sets of error terms that need to be converged together for later computation into the Lyapunov function.
If the design of a specified performance model is not carried out, the tracking error x &isnot processed1iAnd (4) performing constraint, so that an error conversion model does not exist.
And 5: aiming at the error system model in the last step, a Lyapunov function is established, the Nussbaum technology is used for processing the fault of the actuator, the actuator is compensated, a neural network is used for approximating the nonlinear function of the system, and finally the self-adaptive rate and the control law are solved.
The lyapunov function was determined as follows:
Figure BDA0003016579630000091
wherein r is0iIs a positive design parameter. Definition of thetai＝||W1i||2，θiIs an ideal self-adaptive rate, and the self-adaptive rate,
Figure BDA0003016579630000092
is the actual rate of adaptation and is,
Figure BDA0003016579630000093
designing a Nussbaum function
Figure BDA0003016579630000094
Simultaneous approximation of unknown nonlinear function F using neural network functions1iI.e. by
Figure BDA0003016579630000095
W1iIs an ideal weight vector, S1iIs a vector of basis functions, δ1iIs the function approximation error. To ViCalculating time derivative, substituting the above results
Figure BDA0003016579630000096
To make it possible to
Figure BDA0003016579630000097
Solving for the virtual control signal alpha of the form1iAdaptive rate thetaiAnd control law uif：
Figure BDA0003016579630000098
Wherein, c1iAnd c2iIs a positive design parameter.
Figure BDA0003016579630000099
Is an intermediate variable whose initial value is a set constant. During the operation of the system, the system is in operation,
Figure BDA0003016579630000101
will update law according to it
Figure BDA0003016579630000102
Are continuously updated and will be updated
Figure BDA0003016579630000103
Substituting into the Nussbaum function
Figure BDA0003016579630000104
Finally, the obtained product is
Figure BDA0003016579630000105
Substituting the virtual control signal alpha1i。
And after the self-adaptive rate and the control rate are obtained, controlling the action of the unmanned aerial vehicle according to the obtained self-adaptive rate and the control law.
To confirm the effectiveness of this example, a simulation experiment was performed as follows:
in the present simulation experiment, the control target is the tracking attitude angle phid＝cos(t)，θd＝cos(t)，ψd1. The model used in this example has the parameters m 1, g 9.8, Ixx＝0.5，Iyy＝0.4，Izz＝0.6，l＝0.5，JP＝0.01，Ωr1. In addition, the initial position and the initial angle of the quad-rotor unmanned aerial vehicle are both [0.8,1.2 and 1 ]]T。
The scheme provides a self-adaptive neural network specified performance control scheme aiming at a type of quad-rotor unmanned aerial vehicle system with actuator faults, so that each attitude angle tends to be consistent with a tracking target, and meanwhile, the attitude angle of the unmanned aerial vehicle meets a certain constraint condition.
And (4) analyzing results:
derived from the selected Lyapunov function:
Figure BDA0003016579630000106
the control law obtained based on the method can enable all signals of the system to be consistent and finally be bounded, so all angles and states can be consistent with the tracking target.
As can be seen from fig. 3, 4 and 5, the roll angle, the pitch angle and the yaw angle of the quad-rotor unmanned aerial vehicle can be kept consistent with the objective function, the tracking effect is good, and the amplitude of the output signal is kept in a safe range.
The embodiment realizes the tracking control of the quad-rotor unmanned aerial vehicle system based on the self-adaptive backstepping method, and considers the problem of system actuator faults. Through the design of the designated performance function, the system meets the preset performance. And the actuator fault of the Nussbaum technology compensation system is adopted, so that the influence of the actuator fault on the system stability is reduced. Meanwhile, the state-limited system is converted into a non-constrained system through nonlinear transformation. The scheme not only enables the attitude angle of the system to be consistent with the tracking target, but also enables the tracking effect of the attitude system to meet the preset performance index.
The scheme of the disclosure provides a new performance function, and unlike the prior art, the method can change the convergence speed and the convergence time of the system by adjusting the relevant parameters of the performance function, thereby increasing the flexibility of the design of the designated performance.
Example 2:
the embodiment discloses unmanned aerial vehicle appointed performance attitude control system, include:
a modeling module: the method is used for establishing a dynamic model for the attitude physical characteristics of the unmanned aerial vehicle;
an unconstrained state model obtaining module: the observer system model is used for establishing a state observer system model according to the dynamic model and converting the observer system model into an unconstrained state model;
an error system model acquisition module: and the system model is used for obtaining an error conversion model according to the set specified performance model and obtaining an error system model according to the unconstrained state model and the error conversion model.
A control rate acquisition module: and the method is used for determining the Lyapunov function according to the error system model and solving to obtain the self-adaptive rate and the control law.
Example 3:
the embodiment discloses an electronic device, which comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, and is characterized in that the processor executes the program to realize the unmanned aerial vehicle designated performance attitude control method in the embodiment 1.
Example 4:
the present embodiment discloses a computer-readable storage medium on which a computer program is stored, which when executed by a processor implements the drone specification performance attitude control method described in embodiment 1.
"computer-readable storage medium" shall be taken to include a single medium or multiple media containing one or more sets of instructions; it should also be understood to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by a processor and that cause the processor to perform any of the methods of the present invention.
It will be appreciated by those skilled in the art that the steps of the invention described above may be implemented using general purpose computer means, or alternatively they may be implemented using program code executable by computing means, whereby the steps may be stored in memory means for execution by the computing means, or may be implemented as separate integrated circuit modules, or may have a plurality of modules or steps implemented as a single integrated circuit module. The present invention is not limited to any specific combination of hardware and software.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.

Claims (10)

1. An unmanned aerial vehicle designated performance attitude control method is characterized by comprising the following steps:
establishing a dynamic model for the attitude physical characteristics of the unmanned aerial vehicle;
establishing a state observer system model according to the dynamic model, and converting the observer system model into an unconstrained state model;
obtaining an error conversion model according to a set designated performance model, and obtaining an error system model according to an unconstrained state model and the error conversion model;
and determining a Lyapunov function according to the error system model, and solving to obtain the self-adaptive rate and the control law.
2. The method of claim 1, wherein the model of the drone is based on an inertial coordinate system and a body coordinate system.
3. The method as claimed in claim 1, wherein the method comprises converting a dynamic model into a state model, disassembling the state model into a position subsystem model and an angle subsystem model, converting in combination with the position subsystem model, the angle subsystem model and an actuator fault model, and establishing a corresponding observer system model for each of the converted position subsystem model and angle subsystem model.
4. The method of claim 1, wherein the observer system model is transformed into an unconstrained state model based on state information fed back by the observer system model.
5. The method of claim 1, wherein a nonlinear mapping technique is introduced to convert the observer system model to an unconstrained state model based on state information fed back by the observer system model.
6. The method of claim 1, wherein Nussbaum functions are used to compensate for actuator faults and neural network approximation system non-linear functions, and finally the adaptation and control rates are solved.
7. The method of claim 1, wherein the error system model is derived from an unconstrained state model and an error transformation model.
8. An unmanned aerial vehicle designated performance attitude control system, comprising:
a modeling module: the method is used for establishing a dynamic model for the attitude physical characteristics of the unmanned aerial vehicle;
an unconstrained state model obtaining module: the observer system model is used for establishing a state observer system model according to the dynamic model and converting the observer system model into an unconstrained state model;
an error system model acquisition module: and the system model is used for obtaining an error conversion model according to the set specified performance model and obtaining an error system model according to the unconstrained state model and the error conversion model.
A control rate acquisition module: and the method is used for determining the Lyapunov function according to the error system model and solving to obtain the self-adaptive rate and the control law.
9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the drone-specific performance attitude control method of any one of claims 1-7 when executing the program.
10. A computer-readable storage medium, on which a computer program is stored, the program, when executed by a processor, implementing the drone-specific performance attitude control method of any one of claims 1 to 7.
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