CN113759949A - Flexible rack unmanned aerial vehicle control method and device and electronic equipment - Google Patents
Flexible rack unmanned aerial vehicle control method and device and electronic equipment Download PDFInfo
- Publication number
- CN113759949A CN113759949A CN202111106688.8A CN202111106688A CN113759949A CN 113759949 A CN113759949 A CN 113759949A CN 202111106688 A CN202111106688 A CN 202111106688A CN 113759949 A CN113759949 A CN 113759949A
- Authority
- CN
- China
- Prior art keywords
- unmanned aerial
- aerial vehicle
- uncertainty
- flexible
- model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 50
- 238000013178 mathematical model Methods 0.000 claims abstract description 30
- 238000013016 damping Methods 0.000 claims abstract description 19
- 239000011159 matrix material Substances 0.000 claims description 41
- 230000003044 adaptive effect Effects 0.000 claims description 17
- 238000004590 computer program Methods 0.000 claims description 15
- 238000010276 construction Methods 0.000 claims description 10
- 238000006073 displacement reaction Methods 0.000 claims description 9
- 238000006243 chemical reaction Methods 0.000 claims description 6
- 238000005452 bending Methods 0.000 claims description 5
- 239000000126 substance Substances 0.000 claims description 4
- 230000009897 systematic effect Effects 0.000 claims description 4
- 230000001133 acceleration Effects 0.000 claims description 3
- 238000012937 correction Methods 0.000 claims description 3
- 230000005484 gravity Effects 0.000 claims description 3
- 238000004088 simulation Methods 0.000 description 11
- 238000013461 design Methods 0.000 description 10
- 230000000694 effects Effects 0.000 description 9
- 238000010586 diagram Methods 0.000 description 7
- 230000008569 process Effects 0.000 description 7
- 230000004044 response Effects 0.000 description 7
- 230000001360 synchronised effect Effects 0.000 description 5
- 230000005540 biological transmission Effects 0.000 description 4
- 238000004891 communication Methods 0.000 description 4
- 238000010168 coupling process Methods 0.000 description 4
- 238000005859 coupling reaction Methods 0.000 description 4
- 230000010355 oscillation Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 230000008878 coupling Effects 0.000 description 3
- 238000009795 derivation Methods 0.000 description 3
- 230000007246 mechanism Effects 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 230000002093 peripheral effect Effects 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 230000003068 static effect Effects 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000003491 array Methods 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 230000001413 cellular effect Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000007774 longterm Effects 0.000 description 2
- 230000001902 propagating effect Effects 0.000 description 2
- RZVHIXYEVGDQDX-UHFFFAOYSA-N 9,10-anthraquinone Chemical compound C1=CC=C2C(=O)C3=CC=CC=C3C(=O)C2=C1 RZVHIXYEVGDQDX-UHFFFAOYSA-N 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000000575 pesticide Substances 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 239000007921 spray Substances 0.000 description 1
- 230000006641 stabilisation Effects 0.000 description 1
- 238000011105 stabilization Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- 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
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/10—Simultaneous control of position or course in three dimensions
- G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
Landscapes
- Engineering & Computer Science (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Feedback Control In General (AREA)
Abstract
The invention relates to a flexible rack unmanned aerial vehicle control method, a flexible rack unmanned aerial vehicle control device and electronic equipment, wherein the method comprises the following steps: the method comprises the steps that a flexible rack unmanned aerial vehicle is equivalent to a spring damping mass system to construct a mathematical model of the flexible rack unmanned aerial vehicle; converting system parameter errors and system dynamic modeling errors into input uncertainty of a system to obtain a flexible rack unmanned aerial vehicle model containing uncertainty; constructing an L1 self-adaptive output feedback controller according to a flexible rack unmanned aerial vehicle model containing uncertainty and a mathematical model of a flexible rack unmanned aerial vehicle; and controlling the flexible rack unmanned aerial vehicle by using an L1 self-adaptive output feedback controller. According to the invention, the L1 self-adaptive output feedback controller is used for controlling the unmanned aerial vehicle, so that the steady-state error of the system can be eliminated, the large unmanned aerial vehicle still has good flight attitude control capability under the condition of insufficient frame rigidity, and the stability and robustness of the unmanned aerial vehicle in flight are improved.
Description
Technical Field
The invention relates to the technical field of flexible rack unmanned aerial vehicle control, in particular to a flexible rack unmanned aerial vehicle control method and device, electronic equipment and a computer readable storage medium.
Background
The rotary wing machine has good maneuverability and stability, and is widely concerned by scholars at home and abroad. Four rotors, six rotors and many flexible frame unmanned aerial vehicle of rotor such as because simple structure, reliable and stable are widely used in fields such as taking photo by plane, commodity circulation, pesticide spray and high altitude investigation. However, research is currently focused primarily on small flexible rack drones. With the development of unmanned technology, the requirements on the carrying capacity of the rotary wing aircraft are higher and higher. However, along with the light design and the improvement of high carrying capacity, the problem of insufficient frame rigidity of the large-scale flexible frame unmanned aerial vehicle is also obvious, and the flying stability and robustness of the flexible frame unmanned aerial vehicle are influenced. Also, there are a number of scholars paying attention to flexible-arm type flexible-frame drones, such as Nixie wearable quadrotors.
Aiming at the research of a large-scale flexible frame unmanned aerial vehicle, the application number CN202011277770.2 belongs to the technical field of flight simulation of flexible frame unmanned aerial vehicles, and discloses a modeling and semi-physical simulation method and system for a medium-large-scale flexible frame unmanned aerial vehicle, wherein a dynamic model of the flexible frame unmanned aerial vehicle is independent from a traditional view system. By adopting a Matlab/Simulink graphical modeling method, the difficulty of model modification is reduced, and the visualization effect of model simulation and analysis is improved. Application number CN201920393243.4 has designed a large-scale flexible frame unmanned aerial vehicle's structure, and this utility model's preceding main motor and back main motor drive the screw in preceding main screw mechanism and the back main screw mechanism respectively and rotate, provide sufficient power for large-scale flexible frame unmanned aerial vehicle and make it lift off, and the flap mechanism of the organism left and right sides is folding to make the organism can keep balance through the extension of wing. Application number CN201910904419.2 proposes a strong auto-coupling PI cooperative control method for a large flexible frame unmanned aerial vehicle UAV. Theoretical analysis and simulation results show that the EAC-PI cooperative control system has good global robust stability.
Therefore, the influence of insufficient rigidity of the frame of the flexible frame unmanned aerial vehicle on the maneuverability and stability of the flexible frame unmanned aerial vehicle is not considered in the existing control method of the flexible frame unmanned aerial vehicle, and the problem of poor obstacle avoidance effect is solved.
Disclosure of Invention
In order to solve the above problems, embodiments of the present invention provide a flexible rack drone control method, a flexible rack drone control device, an electronic device, and a computer-readable storage medium.
A flexible rack unmanned aerial vehicle control method comprises the following steps:
step 1: the flexible rack unmanned aerial vehicle is equivalent to a spring damping mass system to obtain a stress model of the flexible rack unmanned aerial vehicle;
step 2: obtaining the external torque of the flexible frame unmanned aerial vehicle according to the stress model;
and step 3: acquiring a virtual control variable of the flexible frame unmanned aerial vehicle;
and 4, step 4: obtaining a mathematical model of the flexible frame unmanned aerial vehicle according to the stress model, the closed external moment and the virtual control variable;
and 5: acquiring uncertainty of the flexible frame unmanned aerial vehicle; the uncertainty comprises a system dynamic modeling error, a system parameter error, a system input error and external disturbance of the system;
step 6: converting the system parameter error and the system dynamic modeling error into input uncertainty of a system to obtain a flexible rack unmanned aerial vehicle model containing uncertainty;
and 7: constructing an L1 self-adaptive output feedback controller according to the uncertainty-containing flexible rack unmanned aerial vehicle model and the mathematical model of the flexible rack unmanned aerial vehicle;
and 8: and controlling the flexible rack unmanned aerial vehicle by using the L1 self-adaptive output feedback controller.
Preferably, the flexible frame unmanned aerial vehicle's stress model is:
wherein M is body mass, xeIs a unit vector, y, of the x-axis direction of the terrestrial coordinate systemeIs a unit vector, z, of the terrestrial coordinate system in the y-axis directioneIs a unit vector of the earth coordinate system in the z-axis direction, K is the elastic coefficient of the machine arm, znbIs the displacement of the end of the horn in the z-axis direction, and n is 1,2,3,4, zbIs the displacement of the machine body in the z-axis direction, C is the damping coefficient of the machine arm, R3For conversion matrix from terrestrial coordinate system to body coordinate system, FnIs the lift force generated by the power unit, and n is 1,2,3,4, L is the length of the unmanned aerial vehicle arm, EI is the bending rigidity, g is the acceleration of gravity, K isxCoefficient of air resistance in x-axis direction, KyCoefficient of air resistance in the y-axis direction, KzThe air resistance coefficient in the z-axis direction is shown, theta is the pitch angle, phi is the roll angle, and psi is the yaw angle.
Preferably, the step 2: obtaining the external force moment of the flexible frame unmanned aerial vehicle according to the stress model, and the external force moment comprises the following steps:
obtaining the combined external moment of the flexible frame unmanned aerial vehicle according to the stress model and the Euler rotation equation; the combined external moment is as follows:
wherein M isxTotal external moment in the x-axis direction, MyTotal external moment in the y-axis direction, MzTotal external moment in the z-axis direction, IxMoment of inertia in the x-axis direction, IyMoment of inertia in the y-axis direction and IzIs the moment of inertia in the z-axis direction.
Preferably, the virtual control variables include:
wherein, U1Unmanned aerial vehicle ascent control amount, U2Unmanned aerial vehicle angle of pitch control, U3Unmanned aerial vehicle roll angle control, U4The yaw angle control quantity of the unmanned aerial vehicle, B is a motor reaction torque proportionality coefficient, omega1Is the rotational speed, ω, of the first electrical machine2Is the rotational speed, ω, of the second motor3Is the rotational speed, ω, of the third motor4The rotational speed of the fourth motor.
Preferably, the mathematical model of the flexible frame unmanned aerial vehicle is as follows:
preferably, the step 6: converting the system parameter error and the system dynamic modeling error into input uncertainty of a system to obtain a flexible rack unmanned aerial vehicle model with uncertainty, wherein the method comprises the following steps:
step 6.1: constructing a state equation containing uncertainty of the flexible frame type unmanned aerial vehicle according to the uncertainty of the flexible frame type unmanned aerial vehicle;
step 6.2: converting the system parameter errors and the system dynamic modeling errors into input uncertainty of a system according to the state equation to obtain a flexible rack unmanned aerial vehicle model containing uncertainty; wherein the uncertainty-containing flexible frame unmanned aerial vehicle model is:
wherein x isa(t) is a system state variable, AaIs a system matrix, BaFor input (control) matrix, σa(t) is the system input error, ua(t) is the system input signal, Na(t) is the system parameter and modeling error, θa(t) is the systematic identification error, ya(t) is the system output signal, CaA matrix is output for the system.
Preferably, the L1 adaptive output feedback controller is:
wherein the content of the first and second substances,
predicting system state variables of the reference model for output, AMHurwitz system matrix for output prediction reference model, BMTo output the input (control) matrix of the prediction reference model,
in order to output the input error of the prediction reference model,
for outputting an output signal of the prediction reference model, CMFor outputting an output matrix, T, of the prediction reference modelsIn order to sample the real time,
for the final input error at the previous moment, phi (T)s) Is a matrix of correction errors of n × n, ur(iTs) For input error at the previous moment, ΛMIs a real matrix, and the matrix is a real matrix,
is the output signal of the previous moment ua(s) is the system input signal ua(t), C(s) is a low-pass filter,
is nMThe unit matrix of order, r(s) is the Laplace transform of the reference signal,
is composed of
Is performed by the laplace transform.
The invention also provides a flexible frame unmanned aerial vehicle control device, which comprises:
the stress model building module is used for enabling the flexible rack unmanned aerial vehicle to be equivalent to a spring damping mass system to obtain a stress model of the flexible rack unmanned aerial vehicle;
the closed external moment establishing module is used for obtaining a closed external moment of the flexible frame unmanned aerial vehicle according to the stress model;
the virtual control variable acquisition module is used for acquiring virtual control variables of the flexible frame unmanned aerial vehicle;
the mathematical model construction module is used for obtaining a mathematical model of the flexible frame unmanned aerial vehicle according to the stress model, the closed external moment and the virtual control variable;
the uncertainty acquisition module is used for acquiring uncertainty of the flexible frame unmanned aerial vehicle; the uncertainty comprises a system dynamic modeling error, a system parameter error, a system input error and external disturbance of the system;
the uncertainty-containing unmanned aerial vehicle model construction module is used for converting the system parameter errors and the system dynamic modeling errors into input uncertainty of a system to obtain an uncertainty-containing flexible rack unmanned aerial vehicle model;
the feedback controller building module is used for building an L1 self-adaptive output feedback controller according to the uncertainty-containing flexible rack unmanned aerial vehicle model and the mathematical model of the flexible rack unmanned aerial vehicle;
and the control module is used for controlling the flexible rack unmanned aerial vehicle by utilizing the L1 self-adaptive output feedback controller.
The invention also provides an electronic device, which comprises a bus, a transceiver (a display unit/an output unit, an input unit), a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the transceiver, the memory and the processor are connected through the bus, and the electronic device is characterized in that the computer program realizes the steps in the flexible rack unmanned aerial vehicle control method when being executed by the processor.
The invention also provides a computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps of any one of the above-mentioned methods for controlling a flexible-frame drone.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention relates to a flexible rack unmanned aerial vehicle control method, a flexible rack unmanned aerial vehicle control device, electronic equipment and a computer readable storage medium, wherein the method comprises the following steps: the method comprises the steps that a flexible rack unmanned aerial vehicle is equivalent to a spring damping mass system to construct a mathematical model of the flexible rack unmanned aerial vehicle; and constructing an L1 self-adaptive output feedback controller according to the flexible rack unmanned aerial vehicle model containing the uncertainty and the mathematical model of the flexible rack unmanned aerial vehicle to control the flexible rack unmanned aerial vehicle. According to the invention, the flexible unmanned aerial vehicle is equivalent to a spring damping mass system, the construction of a mathematical model of the flexible unmanned aerial vehicle is completed, the dynamic characteristics of the flexible unmanned aerial vehicle can be accurately expressed, and the simulation of the dynamic characteristics of the unmanned aerial vehicle is more accurate; meanwhile, an L1 self-adaptive output feedback controller is constructed based on a mathematical model, and the L1 self-adaptive output feedback controller is used for controlling the flexible unmanned aerial vehicle such as a frame, so that the steady-state error of the system can be eliminated, the high flight attitude control capability is still realized under the condition that the frame rigidity of the large unmanned aerial vehicle is insufficient, and the stability and the robustness of the unmanned aerial vehicle during flying are improved.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a method for controlling a flexible rack drone in an embodiment provided by the present invention;
fig. 2 is a schematic diagram of a cantilever model of a single arm of an unmanned aerial vehicle according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a cantilever beam model equivalent to a spring-damped mass system in an embodiment provided by the present invention;
fig. 4 is a schematic diagram of a spring damping mass equivalent system of the whole unmanned aerial vehicle in the embodiment of the invention;
fig. 5 is a schematic diagram illustrating an uncertainty classification of a flexible-chassis-type drone according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of uncertainty classification for a controller-oriented design in an embodiment provided by the present invention;
FIG. 7 is a schematic diagram of a first drone control system in an embodiment provided by the present invention;
FIG. 8 is a schematic view of a second drone control system in an example provided by the present invention;
fig. 9 is an attitude response curve of the conventional PID controller controlling the lower rigid frame and the flexible frame drone in the embodiment provided by the present invention;
fig. 10 is an attitude response curve of a flexible-gantry drone under a conventional PID controller and output feedback L1 adaptive controller (scenario one) in an embodiment provided by the present invention;
fig. 11 is an attitude response curve of a flexible-rack drone under the first and second solutions of the output feedback L1 adaptive controller in the embodiment provided by the present invention;
fig. 12 is a schematic structural diagram of an electronic device for executing a flexible rack drone control method in an embodiment provided by the present invention.
Detailed Description
In the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
The invention aims to provide a flexible frame unmanned aerial vehicle control method, a flexible frame unmanned aerial vehicle control device and electronic equipment, which are used for ensuring that the large unmanned aerial vehicle still has good flight attitude control capability under the condition of insufficient frame rigidity.
Referring to fig. 1-6, a method for controlling a flexible frame drone includes:
s1: the flexible rack unmanned aerial vehicle is equivalent to a spring damping mass system to obtain a stress model of the flexible rack unmanned aerial vehicle;
specifically, regarding a single horn of the unmanned aerial vehicle as a cantilever beam model, and equivalent to a spring damping mass system, as shown in fig. 2 to 3, it can be obtained that:
wherein F is the lift generated by the propeller, L is the unmanned aerial vehicle arm length, EI is the bending stiffness, αbIs the angle of bending, ωbIs deflection, K is the elastic coefficient of the horn, C is the damping coefficient of the horn, m is the mass of the power unit of the unmanned aerial vehicle, x1、x2Respectively the displacement of power pack and unmanned aerial vehicle organism.
Finishing to obtain:
wherein, T is the lift that passes through the frame transmission and act on the unmanned aerial vehicle organism.
As shown in fig. 4, the whole unmanned aerial vehicle is equivalent to a spring damping mass system. Wherein, Ob-XbYbZbIs a body coordinate system, Oe-exeyezIs a world coordinate system, theta is a pitch angle, phi is a roll angle, psi is a yaw angle, FnLift force generated for the power unit, znbFor displacement of the end of the horn in the z-axis direction, zbThe displacement of the machine body in the z-axis direction is shown, and M is the machine body mass. According to the formula (2):
according to Newton's theorem, the stress model of the flexible frame unmanned aerial vehicle is obtained as follows:
wherein M is body mass, xeIs a unit vector, y, of the x-axis direction of the terrestrial coordinate systemeIs a unit vector, z, of the terrestrial coordinate system in the y-axis directioneIs a unit vector of the earth coordinate system in the z-axis direction, K is the elastic coefficient of the machine arm, znbIs the displacement of the end of the horn in the z-axis direction, and n is 1,2,3,4, zbIs the displacement of the machine body in the z-axis direction, C is the damping coefficient of the machine arm, R3For conversion matrix from terrestrial coordinate system to body coordinate system, FnIs the lift force generated by the power unit, and n is 1,2,3,4, L is the length of the unmanned aerial vehicle arm, EI is the bending rigidity, g is the acceleration of gravity, K isxCoefficient of air resistance in x-axis direction, KyCoefficient of air resistance in the y-axis direction, KzThe air resistance coefficient in the z-axis direction is shown, theta is the pitch angle, phi is the roll angle, and psi is the yaw angle.
S2: obtaining the external torque of the flexible frame unmanned aerial vehicle according to the stress model;
in practical application, when the attitude angle changes in small increments, according to the euler rotation equation, the resultant external moment of the unmanned aerial vehicle in the body coordinate system can be obtained as follows:
wherein M isxTotal external moment in the x-axis direction, MyTotal external moment in the y-axis direction, MzTotal external moment in the z-axis direction, IxMoment of inertia in the x-axis direction, IyMoment of inertia in the y-axis direction and IzIs the moment of inertia in the z-axis direction.
S3: acquiring a virtual control variable of the flexible frame unmanned aerial vehicle;
since the quad-rotor drone system is a complex under-actuated system with four inputs and six outputs, four virtual control variables are obtained:
wherein, U1Unmanned aerial vehicle ascent control amount, U2Unmanned aerial vehicle angle of pitch control, U3Unmanned aerial vehicle roll angle control, U4The yaw angle control quantity of the unmanned aerial vehicle, B is a motor reaction torque proportionality coefficient, omegan(n-1, 2,3,4) is the speed of each motor, further ω1Is the rotational speed, ω, of the first electrical machine2Is the rotational speed, ω, of the second motor3Is the rotational speed, ω, of the third motor4The rotational speed of the fourth motor.
S4: obtaining a mathematical model of the flexible frame unmanned aerial vehicle according to the stress model, the closed external moment and the virtual control variable; in the embodiment of the invention, the mathematical model of the flexible frame unmanned aerial vehicle is obtained without considering air resistance and gyro moment as follows:
S1-S4 in the invention is mainly the process of establishing the mathematical model of the flexible frame unmanned aerial vehicle, the flexible unmanned aerial vehicle is equivalent to a spring damping mass system, the establishment of the mathematical model of the flexible unmanned aerial vehicle is completed, the dynamic characteristics of the flexible unmanned aerial vehicle can be accurately expressed, the simulation of the dynamic characteristics of the unmanned aerial vehicle is more accurate, meanwhile, the problem of the flexible frame is expressed on the mathematical model, the foundation can be laid for the design of a subsequent controller, and the control of the flight attitude of the flexible frame is further completed.
S5: acquiring uncertainty of the flexible frame unmanned aerial vehicle; the uncertainty comprises a system dynamic modeling error, a system parameter error, a system input error and external disturbance of the system;
s6: converting system parameter errors and system dynamic modeling errors into input uncertainty of a system to obtain a flexible rack unmanned aerial vehicle model containing uncertainty;
wherein, S6 specifically includes:
s6.1: constructing a state equation containing uncertainty of the flexible frame type unmanned aerial vehicle according to the uncertainty of the flexible frame type unmanned aerial vehicle;
s6.2: and converting the system parameter error and the system dynamic modeling error into input uncertainty of the system according to a state equation to obtain the flexible rack unmanned aerial vehicle model containing uncertainty.
Further, in embodiments of the present invention, the uncertainty of the flexible chassis drone includes a match uncertainty and a no-match uncertainty. The matching uncertainty mainly comprises a system dynamic modeling error, a system parameter error and a system input error. The non-matching uncertainty is mainly an external disturbance of the system. Fig. 5 shows a schematic of two uncertainties in the system. Therefore, the state equation of the flexible-frame-like drone containing uncertainty can be expressed as:
wherein, Delta A is the system parameter and modeling error of the system, and sigmaa(t) is the system input error, θa(t) is the systematic identification error, daAnd (t) the external interference of the system, the control problem caused by the flexibility problem of the frame is mainly researched, and the influence of the external interference on the flight stability of the unmanned aerial vehicle is not considered temporarily.
By mathematical derivation, let Δ a be BaΝa(t), converting the system parameters and modeling errors into input uncertainty of the system, and thus obtaining a flexible rack unmanned aerial vehicle model with uncertainty as shown in fig. 6 by using the mathematical expression:
wherein x isa(t) is a system state variable, AaIs a system matrix, BaFor input (control) matrix, σa(t) is the system input error, ua(t) is the system input signal, Na(t) is the system parameter and modeling error, θa(t) is the systematic identification error, ya(t) is the system output signal, CaA matrix is output for the system.
In the invention, the S5-S6 analyzes and arranges the uncertainty in the flexible frame, represents various uncertainties of the unmanned aerial vehicle with the flexible frame, and converts the modeling and system uncertainties into the uncertainty of system input through mathematical derivation, namely the flexibility problem can be regarded as the output uncertainty problem, so that the uncertainty in the model can be almost eliminated, the construction precision of the mathematical model of the unmanned aerial vehicle is greatly improved, and the design of a controller is further laid.
S7: constructing an L1 self-adaptive output feedback controller according to a flexible rack unmanned aerial vehicle model containing uncertainty and a mathematical model of a flexible rack unmanned aerial vehicle;
in the step, the design of an output feedback L1 adaptive controller is carried out on the basis of the flexibility modeling problem and the output uncertainty problem which are analyzed in the S1-S6 part. The total error of system input is brought into the system input signal, namely the uncertainty of output is taken as the basis, so that an output prediction model, an adaptive law and a control law are deduced, and the design of the whole controller is completed. Based on the characteristics of the controller, the L1 adaptive controller can be used for controlling the angular speed or angle of the aircraft, so that two sets of design schemes of the controller are obtained.
The output feedback L1 adaptive control algorithm is suitable for a closed loop stabilization system, and the controller of the invention has two schemes. On the basis of building a cascade PID controller, an L1 controller is respectively built on an angle ring PID controller and an angular velocity ring PID controller. As shown in fig. 7-8, scheme one and scheme two, respectively.
Because the invention researches the flight control problem of the unmanned aerial vehicle under the condition of frame flexibility, and mainly influences of pitch/roll channels according to the modeling condition, the design of the output feedback L1 adaptive controller of the invention is constructed based on a single output system.
For closed loop systems there are:
ya(s)＝Gn(s)(ua(s)+Ea(s)) (10)
wherein u isa(s) is the system input signal ua(t) laplace transform; y isa(s) is the system output signal ya(t) laplace transform; gn(s) is the single-channel transfer function of the closed-loop system; ea(s) representing the system input error Ν in a closed loop systema(t)xa(t)、θa(t) sum of (d).
According to the Lipschitz continuity condition, a continuity assumption of system uncertainty can be obtained. Has a constant Li> 0 and L0> 0, such that:
where f is the unknown correspondence, and f (t, y) is the time-varying nonlinear uncertainty and perturbation.
(1) The construction process of the output prediction model (OutputPredictor) is as follows:
given a bounded continuous reference input signal ra(t) of (d). The control purpose is to design an output feedback controller ua(t) making the system output ya(t) according to the desired reference model GM(s) tracking the reference input ra(t) of (d). From equation (11) we can obtain:
rewrite equation (12) to a standard state space form:
wherein (A)M,BM,CM) Is a reference model GMIs minimal to implement. For the closed loop system given in equation (13), the output prediction model can be obtained as:
(2) the adaptive law (AdaptationLaw) calculation process is as follows:
AMis a Hurwitz matrix whose algebraic Lyapunov equation is:
wherein n isMIs a reference system matrix AMOrder of (1), QMIs a real block diagonal matrix that exists,
is nMThe matrix of real numbers of the order,
is nMAn identity matrix of order.
Wherein the content of the first and second substances,
is a real matrix, CMIs a reference system GM(s) real output matrix, TMIs that
The null space of (a).
The error between the predicted and measured values defining the reference model is:
based on equations (15) (16) (17), the adaptation law can be found as:
(3) the control law (ControlLaw) calculation process is as follows:
based on (16) (18), the control signal is defined as:
where c(s) is a low pass filter, which follows the following rule:
wherein | | xi | purpleL1Represents the L1 norm of the corresponding system, L ═ L1,L2,···L3]TIs a vector of sets of constants in the continuous assumption of system uncertainty described by equation (11).
To this end, the L1 adaptive output feedback controller for a nominal closed loop system is as follows:
wherein the content of the first and second substances,
predicting system state variables of the reference model for output, AMHurwitz system matrix for output prediction reference model, BMTo output the input (control) matrix of the prediction reference model,
in order to output the input error of the prediction reference model,
for outputting an output signal of the prediction reference model, CMFor outputting an output matrix, T, of the prediction reference modelsIn order to sample the real time,
for the final input error at the previous moment, phi (T)s) Is a matrix of correction errors of n × n, ur(iTs) For input error at the previous moment, ΛMIs a real matrix, and the matrix is a real matrix,
is the output signal of the previous moment ua(s) is the system input signal ua(t), C(s) is a low-pass filter,
is nMThe unit matrix of order, r(s) is the Laplace transform of the reference signal,
is composed of
Is performed by the laplace transform.
S8: and controlling the flexible rack unmanned aerial vehicle by using an L1 self-adaptive output feedback controller.
The control method of the flexible-rack unmanned aerial vehicle is suitable for flexible-rack unmanned aerial vehicles made of flexible materials with extremely strong plasticity and large-scale-rack unmanned aerial vehicles with insufficient rigidity. According to the invention, the L1 self-adaptive output feedback controller is used for controlling the flexible frame unmanned aerial vehicle, so that the large unmanned aerial vehicle still has good flight attitude control capability under the condition of insufficient frame rigidity.
The L1 adaptive output feedback controller constructed in accordance with the present invention is further described below with reference to specific embodiments. Firstly, simulation is carried out based on a simulink module in matlab, and the control effect of the output feedback L1 adaptive controller is verified through comparison with the traditional PID controller. And by comparing the first scheme with the second scheme, the advantages and the disadvantages of the two schemes are verified.
Fig. 9 is a control simulation of a rigid-body and flexible-frame unmanned aerial vehicle using a conventional PID controller, with reference signals being 20-degree step signals and sinusoidal signals, respectively. The rigid frame response curve adjustment time is less than 2 seconds. Therefore, the system has no overshoot and steady-state errors and has good tracking performance. However, the response curve of the flexible frame oscillates around the reference signal curve, the adjustment time is long, overshoot and steady-state errors exist, and the tracking performance is obviously poor.
Fig. 10 is a simulation result of attitude tracking of the flexible frame drone by comparing an L1 adaptive output feedback controller control angle loop (scheme one) with a traditional PID controller. The traditional PID controller can track the reference signal, but the response curve has obvious oscillation, compared with the reference signal, the oscillation amplitude is in the range of 10% -20%, and the system has obvious steady-state error. The oscillation amplitude of the L1 self-adaptive output feedback controller is less than 5%, the adjusting time is obviously reduced, the steady-state error is eliminated, and the tracking effect is obviously improved.
Fig. 11 is a result of attitude tracking simulation of the flexible frame drone by comparing the L1 adaptive output feedback controller control angular velocity loop (case two) with the case one controller. Compared with the traditional PID controller and the scheme I, the scheme basically eliminates the problem of curve oscillation, does not have overshoot and steady-state errors, but has slow response speed and good tracking effect.
Under the condition that the physical structure of the unmanned aerial vehicle cannot be continuously optimized, the contradiction between the light weight design and the high load requirement of the large rotor unmanned aerial vehicle is found out and reflected on the problem of the rigidity of the frame, the problem of the rigidity of the frame is equivalent to a spring damping mass system, the construction of a mathematical model of the flexible unmanned aerial vehicle is completed, meanwhile, the generation and classification of uncertainty of the flexible unmanned aerial vehicle are analyzed, and an output feedback L1-based adaptive controller is designed based on an adaptive algorithm to ensure the flight stability of the flexible frame unmanned aerial vehicle.
The invention also provides a flexible frame unmanned aerial vehicle control device, which comprises:
the stress model building module is used for enabling the flexible rack unmanned aerial vehicle to be equivalent to a spring damping mass system to obtain a stress model of the flexible rack unmanned aerial vehicle;
the closed external moment establishing module is used for obtaining a closed external moment of the flexible frame unmanned aerial vehicle according to the stress model;
the virtual control variable acquisition module is used for acquiring virtual control variables of the flexible frame unmanned aerial vehicle;
the mathematical model construction module is used for obtaining a mathematical model of the flexible frame unmanned aerial vehicle according to the stress model, the closed external moment and the virtual control variable;
the uncertainty acquisition module is used for acquiring uncertainty of the flexible frame unmanned aerial vehicle; the uncertainty comprises a system dynamic modeling error, a system parameter error, a system input error and external disturbance of the system;
the uncertainty-containing unmanned aerial vehicle model construction module is used for converting system parameter errors and system dynamic modeling errors into input uncertainty of a system to obtain an uncertainty-containing flexible rack unmanned aerial vehicle model;
the feedback controller building module is used for building an L1 self-adaptive output feedback controller according to the flexible rack unmanned aerial vehicle model containing uncertainty and the mathematical model of the flexible rack unmanned aerial vehicle;
and the control module is used for controlling the flexible rack unmanned aerial vehicle by utilizing the L1 self-adaptive output feedback controller.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
according to the invention, the flexible unmanned aerial vehicle is equivalent to a spring damping mass system, and the uncertainty of modeling and the system is converted into the uncertainty of system input through mathematical derivation, namely the flexibility problem can be regarded as the output uncertainty problem, so that the uncertainty in the model can be almost eliminated, and the simulation of the dynamic characteristics of the unmanned aerial vehicle is more accurate; meanwhile, an L1 self-adaptive output feedback controller is constructed based on a mathematical model, and the L1 self-adaptive output feedback controller is used for controlling the flexible unmanned aerial vehicle such as a frame, so that the steady-state error of the system can be eliminated, the high flight attitude control capability is still realized under the condition that the frame rigidity of the large unmanned aerial vehicle is insufficient, and the stability and the robustness of the unmanned aerial vehicle during flying are improved.
In addition, an embodiment of the present invention further provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and operable on the processor, where the transceiver, the memory, and the processor are connected via the bus, and when the computer program is executed by the processor, the processes of the flexible rack drone control method embodiment are implemented, and the same technical effects can be achieved, and are not described herein again to avoid repetition.
Specifically, referring to fig. 12, an embodiment of the present invention further provides an electronic device, which includes a bus 1110, a processor 1120, a transceiver 1130, a bus interface 1140, a memory 1150, and a user interface 1160.
In an embodiment of the present invention, the electronic device further includes: a computer program stored on the memory 1150 and executable on the processor 1120, the computer program when executed by the processor 1120 implementing the processes of one of the flexible rack drone control method embodiments described above.
A transceiver 1130 for receiving and transmitting data under the control of the processor 1120.
In embodiments of the invention in which a bus architecture (represented by bus 1110) is used, bus 1110 may include any number of interconnected buses and bridges, with bus 1110 connecting various circuits including one or more processors, represented by processor 1120, and memory, represented by memory 1150.
The bus 1110 may also connect various other circuits such as peripherals, voltage regulators, or power management circuits to provide an interface between the bus 1110 and the transceiver 1130, as is well known in the art. Therefore, the embodiments of the present invention will not be further described.
The transceiver 1130 may be one element or may be multiple elements, such as multiple receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. For example: the transceiver 1130 receives external data from other devices, and the transceiver 1130 transmits data processed by the processor 1120 to other devices. Depending on the nature of the computer system, a user interface 1160 may also be provided, such as: touch screen, physical keyboard, display, mouse, speaker, microphone, trackball, joystick, stylus.
It is to be appreciated that in embodiments of the invention, the memory 1150 may further include memory located remotely with respect to the processor 1120, which may be coupled to a server via a network. One or more portions of the above-described networks may be an ad hoc network (ad hoc network), an intranet (intranet), an extranet (extranet), a Virtual Private Network (VPN), a Local Area Network (LAN), a Wireless Local Area Network (WLAN), a Wide Area Network (WAN), a Wireless Wide Area Network (WWAN), a Metropolitan Area Network (MAN), the Internet (Internet), a Public Switched Telephone Network (PSTN), a plain old telephone service network (POTS), a cellular telephone network, a wireless fidelity (Wi-Fi) network, and combinations of two or more of the above. For example, the cellular telephone network and the wireless network may be a global system for Mobile Communications (GSM) system, a Code Division Multiple Access (CDMA) system, a Worldwide Interoperability for Microwave Access (WiMAX) system, a General Packet Radio Service (GPRS) system, a Wideband Code Division Multiple Access (WCDMA) system, a Long Term Evolution (LTE) system, an LTE Frequency Division Duplex (FDD) system, an LTE Time Division Duplex (TDD) system, a long term evolution-advanced (LTE-a) system, a Universal Mobile Telecommunications (UMTS) system, an enhanced Mobile Broadband (eMBB) system, a mass Machine Type Communication (mtc) system, an ultra reliable Low Latency Communication (urrllc) system, or the like.
It is to be understood that the memory 1150 in embodiments of the present invention can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. Wherein the nonvolatile memory includes: Read-Only Memory (ROM), Programmable ROM (PROM), Erasable PROM (EPROM), Electrically Erasable PROM (EEPROM), or Flash Memory.
The volatile memory includes: random Access Memory (RAM), which acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as: static random access memory (Static RAM, SRAM), Dynamic random access memory (Dynamic RAM, DRAM), Synchronous Dynamic random access memory (Synchronous DRAM, SDRAM), Double Data rate Synchronous Dynamic random access memory (Double Data RateSDRAM, DDRSDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and direct memory bus RAM (DRRAM). The memory 1150 of the electronic device described in the embodiments of the invention includes, but is not limited to, the above and any other suitable types of memory.
In an embodiment of the present invention, memory 1150 stores the following elements of operating system 1151 and application programs 1152: an executable module, a data structure, or a subset thereof, or an expanded set thereof.
Specifically, the operating system 1151 includes various system programs such as: a framework layer, a core library layer, a driver layer, etc. for implementing various basic services and processing hardware-based tasks. Applications 1152 include various applications such as: media Player (Media Player), Browser (Browser), for implementing various application services. A program implementing a method of an embodiment of the invention may be included in application program 1152. The application programs 1152 include: applets, objects, components, logic, data structures, and other computer system executable instructions that perform particular tasks or implement particular abstract data types.
In addition, an embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements each process of the flexible rack drone control method embodiment, and can achieve the same technical effect, and in order to avoid repetition, details are not repeated here.
The computer-readable storage medium includes: permanent and non-permanent, removable and non-removable media may be tangible devices that retain and store instructions for use by an instruction execution apparatus. The computer-readable storage medium includes: electronic memory devices, magnetic memory devices, optical memory devices, electromagnetic memory devices, semiconductor memory devices, and any suitable combination of the foregoing. The computer-readable storage medium includes: phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), non-volatile random access memory (NVRAM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic tape cartridge storage, magnetic tape disk storage or other magnetic storage devices, memory sticks, mechanically encoded devices (e.g., punched cards or raised structures in a groove having instructions recorded thereon), or any other non-transmission medium useful for storing information that may be accessed by a computing device. As defined in embodiments of the present invention, the computer-readable storage medium does not include transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses traveling through a fiber optic cable), or electrical signals transmitted through a wire.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, electronic device and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions in actual implementation, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may also be an electrical, mechanical or other form of connection.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to solve the problem to be solved by the embodiment of the invention.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present invention may be substantially or partially contributed by the prior art, or all or part of the technical solutions may be embodied in a software product stored in a storage medium and including instructions for causing a computer device (including a personal computer, a server, a data center, or other network devices) to execute all or part of the steps of the methods of the embodiments of the present invention. And the storage medium includes various media that can store the program code as listed in the foregoing.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the present invention, and the present invention shall be covered by the claims. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A flexible frame unmanned aerial vehicle control method is characterized by comprising the following steps:
step 1: the flexible rack unmanned aerial vehicle is equivalent to a spring damping mass system to obtain a stress model of the flexible rack unmanned aerial vehicle;
step 2: obtaining the external torque of the flexible frame unmanned aerial vehicle according to the stress model;
and step 3: acquiring a virtual control variable of the flexible frame unmanned aerial vehicle;
and 4, step 4: obtaining a mathematical model of the flexible frame unmanned aerial vehicle according to the stress model, the closed external moment and the virtual control variable;
and 5: acquiring uncertainty of the flexible frame unmanned aerial vehicle; the uncertainty comprises a system dynamic modeling error, a system parameter error, a system input error and external disturbance of the system;
step 6: converting the system parameter error and the system dynamic modeling error into input uncertainty of a system to obtain a flexible rack unmanned aerial vehicle model containing uncertainty;
and 7: constructing an L1 self-adaptive output feedback controller according to the uncertainty-containing flexible rack unmanned aerial vehicle model and the mathematical model of the flexible rack unmanned aerial vehicle;
and 8: and controlling the flexible rack unmanned aerial vehicle by using the L1 self-adaptive output feedback controller.
2. The method of claim 1, wherein the force model of the flexible-frame drone is:
wherein M is body mass, xeIs a unit vector, y, of the x-axis direction of the terrestrial coordinate systemeIs a unit vector, z, of the terrestrial coordinate system in the y-axis directioneIs a unit vector of the earth coordinate system in the z-axis direction, K is the elastic coefficient of the machine arm, znbIs the displacement of the end of the horn in the z-axis direction, and n is 1,2,3,4, zbIs the displacement of the machine body in the z-axis direction, C is the damping coefficient of the machine arm, R3For conversion matrix from terrestrial coordinate system to body coordinate system, FnIs the lift force generated by the power unit, and n is 1,2,3,4, L is the length of the unmanned aerial vehicle arm, EI is the bending rigidity, g is the acceleration of gravity, K isxCoefficient of air resistance in x-axis direction, KyCoefficient of air resistance in the y-axis direction, KzThe air resistance coefficient in the z-axis direction is shown, theta is the pitch angle, phi is the roll angle, and psi is the yaw angle.
3. The flexible rack drone controlling method according to claim 2, characterized in that said step 2: obtaining the external force moment of the flexible frame unmanned aerial vehicle according to the stress model, and the external force moment comprises the following steps:
obtaining the combined external moment of the flexible frame unmanned aerial vehicle according to the stress model and the Euler rotation equation; the combined external moment is as follows:
wherein M isxTotal external moment in the x-axis direction、MyTotal external moment in the y-axis direction, MzTotal external moment in the z-axis direction, IxMoment of inertia in the x-axis direction, IyMoment of inertia in the y-axis direction and IzIs the moment of inertia in the z-axis direction.
4. The flexible rack drone controlling method of claim 3, wherein the virtual control variables include:
wherein, U1Unmanned aerial vehicle ascent control amount, U2Unmanned aerial vehicle angle of pitch control, U3Unmanned aerial vehicle roll angle control, U4The yaw angle control quantity of the unmanned aerial vehicle, B is a motor reaction torque proportionality coefficient, omega1Is the rotational speed, ω, of the first electrical machine2Is the rotational speed, ω, of the second motor3Is the rotational speed, ω, of the third motor4The rotational speed of the fourth motor.
5. The method of claim 4, wherein the mathematical model of the flexible rack drone is:
6. the flexible rack drone controlling method according to claim 5, characterized in that said step 6: converting the system parameter error and the system dynamic modeling error into input uncertainty of a system to obtain a flexible rack unmanned aerial vehicle model with uncertainty, wherein the method comprises the following steps:
step 6.1: constructing a state equation containing uncertainty of the flexible frame type unmanned aerial vehicle according to the uncertainty of the flexible frame type unmanned aerial vehicle;
step 6.2: converting the system parameter errors and the system dynamic modeling errors into input uncertainty of a system according to the state equation to obtain a flexible rack unmanned aerial vehicle model containing uncertainty; wherein the uncertainty-containing flexible frame unmanned aerial vehicle model is:
wherein x isa(t) is a system state variable, AaIs a system matrix, BaFor input (control) matrix, σa(t) is the system input error, ua(t) is the system input signal, Na(t) is the system parameter and modeling error, θa(t) is the systematic identification error, ya(t) is the system output signal, CaA matrix is output for the system.
7. The flexible rack drone controlling method of claim 6, wherein the L1 adaptive output feedback controller is:
wherein the content of the first and second substances,
predicting system state variables of the reference model for output, AMHurwitz system matrix for output prediction reference model, BMTo output the input (control) matrix of the prediction reference model,
in order to output the input error of the prediction reference model,
for outputting an output signal of the prediction reference model, CMPredicting an output matrix of a reference model for output，TsIn order to sample the real time,
for the final input error at the previous moment, phi (T)s) Is a matrix of correction errors of n × n, ur(iTs) For input error at the previous moment, ΛMIn the form of a real matrix,
is the output signal of the previous moment ua(s) is the system input signal ua(t), C(s) is a low-pass filter,
is nMThe unit matrix of order, r(s) is the Laplace transform of the reference signal,
is composed of
Is performed by the laplace transform.
8. A flexible frame unmanned aerial vehicle controlling means characterized by, includes:
the stress model building module is used for enabling the flexible rack unmanned aerial vehicle to be equivalent to a spring damping mass system to obtain a stress model of the flexible rack unmanned aerial vehicle;
the closed external moment establishing module is used for obtaining a closed external moment of the flexible frame unmanned aerial vehicle according to the stress model;
the virtual control variable acquisition module is used for acquiring virtual control variables of the flexible frame unmanned aerial vehicle;
the mathematical model construction module is used for obtaining a mathematical model of the flexible frame unmanned aerial vehicle according to the stress model, the closed external moment and the virtual control variable;
the uncertainty acquisition module is used for acquiring uncertainty of the flexible frame unmanned aerial vehicle; the uncertainty comprises a system dynamic modeling error, a system parameter error, a system input error and external disturbance of the system;
the uncertainty-containing unmanned aerial vehicle model construction module is used for converting the system parameter errors and the system dynamic modeling errors into input uncertainty of a system to obtain an uncertainty-containing flexible rack unmanned aerial vehicle model;
the feedback controller building module is used for building an L1 self-adaptive output feedback controller according to the uncertainty-containing flexible rack unmanned aerial vehicle model and the mathematical model of the flexible rack unmanned aerial vehicle;
and the control module is used for controlling the flexible rack unmanned aerial vehicle by utilizing the L1 self-adaptive output feedback controller.
9. An electronic device comprising a bus, a transceiver (display unit/output unit, input unit), a memory, a processor and a computer program stored on the memory and executable on the processor, the transceiver, the memory and the processor being connected by the bus, characterized in that the computer program, when executed by the processor, implements the steps in a method of flexible rack drone control according to any one of claims 1 to 7.
10. A computer-readable storage medium, having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps in a flexible rack drone controlling method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111106688.8A CN113759949A (en) | 2021-09-22 | 2021-09-22 | Flexible rack unmanned aerial vehicle control method and device and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111106688.8A CN113759949A (en) | 2021-09-22 | 2021-09-22 | Flexible rack unmanned aerial vehicle control method and device and electronic equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113759949A true CN113759949A (en) | 2021-12-07 |
Family
ID=78796744
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111106688.8A Pending CN113759949A (en) | 2021-09-22 | 2021-09-22 | Flexible rack unmanned aerial vehicle control method and device and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113759949A (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110597291A (en) * | 2019-10-09 | 2019-12-20 | 酷黑科技（北京）有限公司 | Detection method, device and system |
| CN111324138A (en) * | 2020-04-09 | 2020-06-23 | 中北大学 | Four-rotor attitude designated time performance-guaranteeing output feedback control method |
| CN112847327A (en) * | 2019-11-12 | 2021-05-28 | 中国科学院沈阳自动化研究所 | Compliant control method for joint of cooperative robot |
-
2021
- 2021-09-22 CN CN202111106688.8A patent/CN113759949A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110597291A (en) * | 2019-10-09 | 2019-12-20 | 酷黑科技（北京）有限公司 | Detection method, device and system |
| CN112847327A (en) * | 2019-11-12 | 2021-05-28 | 中国科学院沈阳自动化研究所 | Compliant control method for joint of cooperative robot |
| CN111324138A (en) * | 2020-04-09 | 2020-06-23 | 中北大学 | Four-rotor attitude designated time performance-guaranteeing output feedback control method |
Non-Patent Citations (2)
| Title |
|---|
| JIAXING CHE.ETC: "Integrated Flight/Structural Mode Control for Very Flexible Aircraft Using L1 Adaptive Output Feedback Controller", 《AIAA GUIDANCE, NAVIGATION, AND CONTROL CONFERENCE》, pages 1 - 19 * |
| 朱虎 等: "基于L1 动态逆的自主空中加油对接控制", 《兵工自动化》, vol. 37, no. 1, pages 19 - 23 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Guo et al. | Multiple observers based anti-disturbance control for a quadrotor UAV against payload and wind disturbances | |
| Shao et al. | Robust dynamic surface trajectory tracking control for a quadrotor UAV via extended state observer | |
| Zuo | Adaptive trajectory tracking control design with command filtered compensation for a quadrotor | |
| Sanz et al. | Robust control of quadrotors based on an uncertainty and disturbance estimator | |
| CN109947126B (en) | Control method, device and equipment of quad-rotor unmanned aerial vehicle and readable medium | |
| Zhu et al. | Robust attitude control of a 3-DOF helicopter considering actuator saturation | |
| Wu et al. | Quantized fixed-time fault-tolerant attitude control for hypersonic reentry vehicles | |
| Abdollahi et al. | Simplified fuzzy‐Padé controller for attitude control of quadrotor helicopters | |
| Shao et al. | Model‐assisted extended state observer and dynamic surface control–based trajectory tracking for quadrotors via output‐feedback mechanism | |
| Liu et al. | Robust command filtered adaptive backstepping control for a quadrotor aircraft | |
| Xu et al. | USDE-based continuous sliding mode control for quadrotor attitude regulation: Method and application | |
| Choudhary | Optimal feedback control of a twin rotor MIMO system | |
| Sonneville et al. | Sensitivity analysis for multibody systems formulated on a Lie group | |
| Yang et al. | Modeling and Robust Trajectory Tracking Control for a Novel Six‐Rotor Unmanned Aerial Vehicle | |
| Mishra et al. | Robust attitude control for quadrotors based on parameter optimization of a nonlinear disturbance observer | |
| Mechali et al. | Robust Finite‐Time Trajectory Tracking Control of Quadrotor Aircraft via Terminal Sliding Mode‐Based Active Antidisturbance Approach: A PIL Experiment | |
| Zhou et al. | A hybrid robust adaptive control for a quadrotor UAV via mass observer and robust controller | |
| Kuo et al. | Quaternion-based adaptive backstepping RFWNN control of quadrotors subject to model uncertainties and disturbances | |
| Duan et al. | Attitude tracking control of small-scale unmanned helicopters using quaternion-based adaptive dynamic surface control | |
| Saif et al. | Modelling of quad‐rotor dynamics and Hardware‐in‐the‐Loop simulation | |
| Srinivasarao et al. | Bond graph modeling and multi-body dynamics of a twin rotor system | |
| Yao | Robust adaptive finite-time attitude tracking control of a 3D pendulum with external disturbance: numerical simulations and hardware experiments | |
| Labbadi et al. | Robust tracking control for a quadrotor subjected to disturbances using new hyperplane-based fast Terminal Sliding Mode | |
| Khodaverdian et al. | Attitude stabilization of spacecraft simulator based on modified constrained feedback linearization model predictive control | |
| CN112631320B (en) | Unmanned aerial vehicle self-adaptive control method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |