CN112631320B - Unmanned aerial vehicle self-adaptive control method and system - Google Patents
Unmanned aerial vehicle self-adaptive control method and system Download PDFInfo
- Publication number
- CN112631320B CN112631320B CN202011616892.XA CN202011616892A CN112631320B CN 112631320 B CN112631320 B CN 112631320B CN 202011616892 A CN202011616892 A CN 202011616892A CN 112631320 B CN112631320 B CN 112631320B
- Authority
- CN
- China
- Prior art keywords
- aerial vehicle
- unmanned aerial
- control law
- control
- torque
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 51
- 238000012937 correction Methods 0.000 claims abstract description 44
- 238000013528 artificial neural network Methods 0.000 claims abstract description 28
- 238000005312 nonlinear dynamic Methods 0.000 claims abstract description 17
- 238000004458 analytical method Methods 0.000 claims abstract description 16
- 239000011159 matrix material Substances 0.000 claims description 20
- 238000001914 filtration Methods 0.000 claims description 17
- 238000005096 rolling process Methods 0.000 claims description 17
- 230000006870 function Effects 0.000 claims description 16
- 230000001133 acceleration Effects 0.000 claims description 14
- 230000003044 adaptive effect Effects 0.000 claims description 14
- 238000006073 displacement reaction Methods 0.000 claims description 9
- 238000006243 chemical reaction Methods 0.000 claims description 6
- 238000013507 mapping Methods 0.000 claims description 6
- 230000009466 transformation Effects 0.000 claims description 5
- 238000012545 processing Methods 0.000 claims description 3
- 238000004880 explosion Methods 0.000 abstract description 5
- 230000000750 progressive effect Effects 0.000 abstract description 2
- 238000004422 calculation algorithm Methods 0.000 description 20
- 238000013461 design Methods 0.000 description 20
- 230000033001 locomotion Effects 0.000 description 10
- 238000010586 diagram Methods 0.000 description 7
- 230000000694 effects Effects 0.000 description 7
- 230000008878 coupling Effects 0.000 description 6
- 238000010168 coupling process Methods 0.000 description 6
- 238000005859 coupling reaction Methods 0.000 description 6
- 238000004364 calculation method Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 4
- 238000004088 simulation Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 238000013178 mathematical model Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000011160 research Methods 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
- 230000009471 action Effects 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000009795 derivation Methods 0.000 description 1
- 230000003631 expected effect Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000010187 selection method Methods 0.000 description 1
- 239000013598 vector Substances 0.000 description 1
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)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The application discloses an unmanned aerial vehicle self-adaptive control method and system, wherein the method comprises the following steps: constructing a preliminary control law of a control variable according to the nonlinear dynamics model of the unmanned aerial vehicle; the neural network is utilized to compensate the total disturbance suffered by the unmanned aerial vehicle in the preliminary control law to obtain a primary correction control law; stability analysis is carried out on the primary correction control law to obtain a control parameter value range; obtaining a final control law; obtaining a vertical lift force value, a roll angle expected value and a pitch angle expected value according to the target position and the current position of the unmanned aerial vehicle and the final control law of the vertical lift force; and obtaining a roll torque value, a pitch torque value and a yaw torque value as control values according to the yaw period, the roll angle and pitch angle expected values, the vertical lift value and the final control laws of roll torque, pitch torque and yaw torque, and outputting the control values. The differential explosion problem is solved, large-scale progressive stability is guaranteed, and the dynamic performance and the anti-interference capability of the control system are improved.
Description
Technical Field
The application relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle self-adaptive control method and system.
Background
The four-rotor unmanned aerial vehicle is an aircraft which realizes a certain task by means of autonomous flight or remote control flight of a flight control system. The four-rotor unmanned aerial vehicle has the characteristics of simple structure, convenience in maintenance and carrying, light weight, strong concealment performance, high task execution efficiency, strong reliability and the like. Has exerted its own great value in various fields. In the entertainment field, aerial photographing is performed by using an unmanned aerial vehicle; in natural disasters, information collection is carried out on disaster conditions by using a four-rotor unmanned aerial vehicle; in biological research, tracking research of biological populations, and the like.
The unmanned aerial vehicle is a multi-input multi-output underactuated system and has the characteristics of strong nonlinearity and strong coupling. In the flight process of the unmanned aerial vehicle, the aerodynamic parameters can be perturbed and are easily interfered by the inside and the outside, so that an accurate mathematical model is difficult to build, and the stable flight of the unmanned aerial vehicle is seriously influenced. In addition, due to limitations of batteries, motors and the like, the unmanned aerial vehicle can provide limited acting force, and the stable flight and the endurance time of the unmanned aerial vehicle are also affected to a certain extent. Therefore, the unmanned aerial vehicle stable flight controller based on interference and input saturation limitation is designed, and the use range and the use value of the unmanned aerial vehicle can be improved.
Related technical scheme of unmanned aerial vehicle control: PID or cascade PID control is adopted. The PID control is a control strategy for eliminating the error by using the error information between the target and the actual behavior in a feedback mode. The method does not depend on an accurate mathematical model, is simple in design and convenient to debug, and has good robustness in a conventional environment. The principle of cascade PID control is shown in FIG. 1. The conventional PID controller has simple design and convenient debugging, so the conventional PID controller is widely applied to the four-rotor unmanned aerial vehicle, but has the following defects:
(1) PID control is a simple feedback control method, so that the unmanned aerial vehicle PID controller cannot realize higher dynamic performance under the conventional condition.
(2) PID control is a classical linear control method, whereas unmanned systems are strongly nonlinear systems. Therefore, the PID control algorithm can only ensure the stability of the unmanned aerial vehicle near the balance point, and cannot realize large-scale progressive stability.
(3) Under the interference environment, the dynamic performance of the unmanned aerial vehicle adopting the PID controller can be seriously influenced, even out of control.
Disclosure of Invention
In order to improve the dynamic performance, the large-range stability and the anti-interference capability of a control system, the application provides an unmanned aerial vehicle self-adaptive control method and system, which adopts the following technical scheme:
in a first aspect, the present application provides an adaptive control method for an unmanned aerial vehicle, including:
Constructing a preliminary control law of a control variable according to the established non-linear dynamics model of the unmanned aerial vehicle; the neural network is utilized to compensate the total disturbance suffered by the unmanned aerial vehicle in the preliminary control law to obtain a primary correction control law;
The stability analysis is carried out on the primary correction control law to obtain the value range of the control parameters in the control law; thereby obtaining the final control law of the control variable;
obtaining a desired value of a rolling angle and a desired value of a pitch angle according to the target position and the current position of the unmanned aerial vehicle and the final control law of the vertical lifting force;
And obtaining a roll torque value, a pitch torque value and a yaw torque value as control quantity output according to the expected value of the yaw angle, the expected value of the roll angle and the expected value of the pitch angle, the lift value in the vertical direction of the unmanned aerial vehicle and the final control laws of the roll torque, the pitch torque and the yaw torque.
By adopting the technical scheme, a cascade structure with the position ring as an outer control ring and the gesture ring as an inner control ring is established to realize decoupling control of high coupling motion of horizontal motion and pitching and rolling motions so as to realize higher dynamic performance of the unmanned aerial vehicle; by establishing a nonlinear dynamics model which is more attached to the actual motion characteristics of the unmanned aerial vehicle, the stability of the unmanned aerial vehicle in a larger motion range can be realized through the cascade structure; the RBF and other neural networks are utilized to estimate and compensate the total disturbance suffered by the unmanned aerial vehicle, so that the anti-interference capability of the unmanned aerial vehicle is improved, and better robustness is realized.
Preferably, before the step of constructing the preliminary control law of the control variable according to the established non-linear dynamics model of the unmanned aerial vehicle, the method further comprises:
the method for establishing the unmanned aerial vehicle nonlinear dynamics model specifically comprises the following steps of:
establishing an earth coordinate system and an unmanned aerial vehicle body coordinate system;
Determining the relation between a transformation matrix from the body coordinate system to the ground coordinate system and the attitude variable of the unmanned aerial vehicle;
Obtaining the relation between the position acceleration and the conversion matrix and the position total disturbance under the ground coordinate system by utilizing the rigid body theory;
acquiring the relation between the attitude acceleration and the attitude total disturbance of the unmanned aerial vehicle under the body coordinate system by utilizing a rigid body theory;
And obtaining the unmanned aerial vehicle nonlinear power model according to the vertical lifting force, the rolling torque, the pitching torque, the navigational yaw torque, the relation between the position acceleration and the conversion matrix and the position total disturbance and the relation between the unmanned aerial vehicle gesture acceleration and the gesture total disturbance by utilizing a Newton-Euler equation.
By adopting the technical scheme, the physical model of the unmanned aerial vehicle is simplified, so that the nonlinear power model of the unmanned aerial vehicle can be obtained, and a feasible foundation is laid for the next control algorithm design.
Preferably, the preliminary control law of the control variable is built according to the built non-linear dynamics model of the unmanned aerial vehicle; the step of compensating the total disturbance suffered by the unmanned aerial vehicle in the preliminary control law by utilizing the neural network to obtain a primary correction control law comprises the following steps of:
Designing a preliminary control law according to the dynamic surface control;
and introducing the RBF neural network into the preliminary control law, determining a weight updating rule and obtaining a primary correction control law.
By adopting the technical scheme, the problem of differential explosion existing in the traditional back-stepping method is solved by adopting the dynamic surface control technology, and the unmanned aerial vehicle system has good dynamic performance.
Preferably, the step of designing the preliminary control law according to the dynamic surface control includes:
Establishing a first error surface equation by taking any degree of freedom of the position or the gesture of the unmanned aerial vehicle as a dynamic surface and using a first moment track and a first moment expected track;
Designing a second moment expected track x 2 according to subsystem starting conditions and a Lyapunov function defined by a first error face;
carrying out low-pass filtering processing on the second moment expected track to obtain a relation between the second moment expected track and a low-pass filtering parameter and a low-pass filtering variable;
Establishing a second error face equation according to the second moment track and the low-pass filtering variable;
and designing a preliminary control law according to a Lyapunov function defined by the initial condition of the subsystem and the second error face, wherein the preliminary control law is expressed as a relational expression among the degree of freedom, the first error face, the second error face, the low-pass filtering variable and the total disturbance.
By adopting the technical scheme, the relation between the degree of freedom and the control variable and the first-order low-pass filtering variable is established by adopting the preliminary control law designed by combining the dynamic surface control technology and the Lyapunov stability function, the basic idea of the back-step design method is to decompose a complex nonlinear system into subsystems which do not exceed the system order, then design a part of the Lyapunov function and the intermediate virtual control quantity for each subsystem, and always 'back' to the whole system, and integrate the two to complete the design of the whole control law. The differential explosion problem of the traditional back-stepping method exists because each subsystem needs to carry out differential operation; according to the scheme, the complex operation relation of the nonlinear dynamic model is directly mapped into the control law, the differential operation of the subsystem involves first-order or second-order low-order differential operation, and a first-order low-pass filter is introduced to solve the problem of differential explosion existing in the traditional backstepping method, so that the unmanned aerial vehicle system has good dynamic performance.
Preferably, the step of introducing the RBF neural network into the preliminary control law, determining the weight updating rule and obtaining the primary correction control law includes:
Estimating the total disturbance in the preliminary control law by adopting an RBF network to obtain a mapping relation matrix between the total disturbance and the error input quantity;
And carrying out iterative computation and updating on the weights set in the mapping relation matrix according to the ideal weights set by the RBF network to obtain a primary correction control law.
By adopting the technical scheme, the RBF neural network algorithm is applied to the unmanned aerial vehicle control algorithm, the preliminary control law is optimized, and the total disturbance suffered by the unmanned aerial vehicle is estimated and compensated relative to the related control algorithm, so that the anti-interference capability of the unmanned aerial vehicle is improved, and better robustness is realized.
Preferably, the method further comprises the steps of after compensating the total disturbance suffered by the unmanned aerial vehicle in the preliminary control law by using the neural network to obtain a primary corrected control law:
When the output of the control variable is inconsistent with the actual input of the controlled object, taking the actual input with the smallest difference with the output as the output of the control variable, and adding anti-saturation compensation in the primary correction control law to obtain a secondary correction control law;
The step of obtaining the value range of the control parameters in the control law by performing stability analysis on the secondary control law comprises the following steps: and carrying out stability analysis on the secondary correction control law to obtain the value range of the control parameter in the control law.
By adopting the technical scheme, the self-adaptive dynamic surface control design method based on the neural network and the anti-saturation auxiliary system is designed, and the control algorithm combines the nonlinear dynamic surface control method, the disturbance estimation compensation based on the RBF neural network and the anti-saturation auxiliary system, so that the unmanned aerial vehicle is gradually stable in a large range and has strong anti-interference capability. And an anti-saturation auxiliary system is added in the control law design, so that the problem of input saturation limitation of the unmanned aerial vehicle is solved, and the practicability of the unmanned aerial vehicle is further improved.
Preferably, the step of obtaining the final control law of the control variable by obtaining the range of the control parameter values through stability analysis of the secondary correction control law includes:
Performing stability analysis according to the sum of Lyapunov functions defined by the second dynamic error, the first-order filtering error, the weight error and the anti-saturation compensation error to respectively obtain a control parameter value range in the secondary correction control law;
Verifying the value range of the control parameter according to a Lyapunov stability theoretical equation, and then using the value range as a final control law;
The final control law of lift force, rolling torque, pitching torque and navigational deviation torque in the vertical direction is obtained; the preliminary control law of the vertical lift force is based on the vertical displacement in the degree of freedom of the position of the unmanned aerial vehicle and is designed according to a dynamic surface control algorithm; the preliminary control law of the roll matrix is based on the roll angle direction rotation in the unmanned aerial vehicle attitude freedom degree and is designed according to a dynamic surface control algorithm; the preliminary control law of the pitching torque is based on the pitching angle direction rotation in the unmanned aerial vehicle attitude degree of freedom and is designed according to a dynamic surface control algorithm; the preliminary control law of the yaw torque is based on the yaw angle direction rotation in the unmanned aerial vehicle attitude freedom degree and is designed according to a dynamic surface control algorithm.
By adopting the technical scheme, the stability of the unmanned aerial vehicle system can be ensured according to the selected control parameters only by taking the proper values, the dynamic performance and the robustness can reach good effects, and the stability of each subsystem can be respectively verified according to the separability principle, so that the stability of the whole system can be ensured.
Preferably, the step of obtaining a final control law further comprises obtaining a final control law of the first intermediate variable and the second intermediate variable;
The step of obtaining the expected value of the rolling angle and the expected value of the pitch angle according to the target position and the current position of the unmanned aerial vehicle and the final control law of the vertical lifting force comprises the following steps:
obtaining a vertical lift value as control quantity output according to the target position and the current position of the unmanned aerial vehicle and the final control law of the vertical lift;
and obtaining a rolling angle expected value and a pitch angle expected value according to the vertical lift value and the final control laws of the first intermediate variable and the second intermediate variable.
By adopting the technical scheme, the decoupling of the position ring and the attitude ring is realized based on the high coupling characteristic of horizontal movement, pitching movement and rolling movement of the unmanned aerial vehicle.
Preferably, the preliminary control law of the first intermediate variable is designed based on the displacement in the horizontal direction in the freedom of the position of the unmanned aerial vehicle as a dynamic surface; the preliminary control law of the second intermediate variable is designed based on the displacement in the vertical direction in the freedom of the position of the unmanned aerial vehicle as a dynamic surface.
By adopting the technical scheme, the control law model design of the first intermediate variable and the second intermediate variable can be uniformly realized through the control law model for constructing the control variable, and the model is not required to be additionally built, so that the algorithm can be simplified on one hand, and the operation speed can be improved on the other hand.
In a second aspect, the present application also provides an adaptive control system for an unmanned aerial vehicle, including:
the storage is used for storing the unmanned aerial vehicle self-adaptive control program;
and the processor executes the steps of the unmanned aerial vehicle self-adaptive control method when the unmanned aerial vehicle self-adaptive control program is operated.
By adopting the technical scheme, the unmanned aerial vehicle self-adaptive control method is presented in the form of computer readable codes and stored in the memory, and when the processor runs the computer readable codes in the memory, the steps of the control method are executed to obtain the effects of improving the dynamic performance, the large-range stability and the anti-interference capability of the control system.
The comprehensive technical effects of the scheme are as follows:
(1) The control algorithm combining the dynamic surface control technology, the RBF neural network and the anti-saturation auxiliary system ensures the large-scale stability of the control system, realizes strong anti-interference capability, achieves good dynamic performance and solves the problem of input saturation limitation.
(2) The dynamic surface control technology is adopted to solve the problem of differential explosion existing in the traditional back-stepping method, and ensure that the unmanned aerial vehicle system has good dynamic performance.
(3) The RBF neural network is utilized to estimate and compensate the total disturbance suffered by the unmanned aerial vehicle, so that the anti-interference capability of the unmanned aerial vehicle is improved, and better robustness is realized.
(4) And an anti-saturation auxiliary system is added in the control law design, so that the problem of input saturation limitation of the unmanned aerial vehicle is solved, and the practicability of the unmanned aerial vehicle is further improved.
Drawings
Fig. 1 is a block diagram of a cascade PID control angle control of the background art.
Fig. 2 is a control block diagram of an adaptive control system for a drone according to another embodiment of the present application.
Fig. 3 is a flowchart of a method for adaptively controlling a unmanned aerial vehicle according to an embodiment of the present application.
Fig. 4 is a simplified diagram of the physical structure of the quadrotor drone of step S10 of fig. 3.
Fig. 5 is a schematic diagram of the establishment of the earth coordinate system in step S10 in fig. 3.
Fig. 6 is a schematic diagram of the establishment of the body coordinates of the unmanned aerial vehicle in step S10 in fig. 3.
Fig. 7 is a graph of MATLAB simulation experiment results of the related PID control method.
Fig. 8 is a diagram of MATLAB simulation experiment results of a method for adaptive control of an unmanned aerial vehicle according to still another embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the accompanying drawings.
The present embodiment is only for explanation of the present application and is not to be construed as limiting the present application, and modifications to the present embodiment, which may not creatively contribute to the present application as required by those skilled in the art after reading the present specification, are all protected by patent laws within the scope of claims of the present application. For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In addition, the term "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In this context, unless otherwise specified, the term "/" generally indicates that the associated object is an "or" relationship.
Embodiments of the unmanned aerial vehicle self-adaptive control method and system of the present application are described in further detail below with reference to the accompanying drawings.
An embodiment of the present application provides an adaptive control system for an unmanned aerial vehicle, as shown in fig. 2, where the system includes a memory and a processor; the memory is used for storing an unmanned aerial vehicle self-adaptive control program; the processor executes the steps of the unmanned aerial vehicle self-adaptive control method when running the unmanned aerial vehicle self-adaptive control program, and specifically comprises a position instruction output unit, a gesture instruction output unit, a position control system, a gesture control system and a four-rotor wing model.
The unmanned aerial vehicle is provided with four inputs (expected values of target position coordinates x, y and z and a navigation deflection angle psi of the unmanned aerial vehicle), and six outputs (namely the unmanned aerial vehicle has six degrees of freedom x, y and z,Θ, ψ) whose horizontal motion is highly coupled with pitch, roll motion. Therefore, the controller is designed to be in a cascade structure, the position control system is used as an outer ring, namely a position ring, and the gesture control system is used as an inner ring, namely a gesture ring, so that decoupling control is realized. The overall control block diagram is shown in fig. 2.
The following describes the implementation of the unmanned aerial vehicle self-adaptive control method in detail with reference to the unmanned aerial vehicle self-adaptive control system:
As shown in fig. 3, another embodiment of the present application provides an adaptive control method for an unmanned aerial vehicle, including:
s10, establishing a non-linear dynamics model of the unmanned aerial vehicle;
and establishing a coordinate system, determining a rotation relation between the machine body coordinate system and an inertial coordinate system, and deriving a non-linear dynamics equation of the unmanned aerial vehicle by utilizing a rigid body theory and a Newton-Euler equation to obtain a four-rotor model.
S20, constructing a preliminary control law of a control variable according to the established non-linear dynamics model of the unmanned aerial vehicle; the neural network is utilized to compensate the total disturbance suffered by the unmanned aerial vehicle in the preliminary control law to obtain a primary correction control law;
Firstly, designing a preliminary control law of each control variable according to a dynamic surface control technology; and secondly, introducing an RBF neural network, a BP neural network and the like into the control law, and determining a weight updating rule. The control variables here include vertical lift U 1, roll torque U 2, pitch torque U 3, yaw torque U 4. In the application, the RBF network is adopted, only the weight from the hidden layer to the output layer needs to be adjusted, and the calculated amount is small. Other neural networks, such as BP neural networks, are theoretically possible, but the weights of the input layer and the hidden layer of the BP neural network need to be adjusted, so that the calculation amount of the BP-like neural network is relatively large and the calculation time is long.
S30, when the output of the control variable is inconsistent with the actual input of the controlled object, taking the actual input with the smallest difference with the output as the output of the control variable, and adding anti-saturation compensation in the primary correction control law to obtain a secondary correction control law;
An anti-saturation auxiliary compensation is added to the control law to solve the input saturation problem. The input saturation problem can be understood as that the actual input of the unmanned aerial vehicle is limited by various reasons such as design and load, a certain range exists, and when the ideal output finally obtained by the final control law calculation exceeds the actually-achievable input range, only the actual input with the smallest difference with the calculated output can be selected as the output of the control system, and the condition is used as compensation to correct the primary control law, so that the secondary correction control law is obtained.
S40, obtaining a value range of a control parameter in the control law by performing stability analysis on the secondary correction control law; thereby obtaining the final control law of the control variable;
Stability analysis is carried out on the secondary correction control law to respectively obtain the value ranges of control parameters in the control laws of the rolling torque U 2, the pitching torque U 3, the navigational yaw torque U 4, the first intermediate variable U x, the second intermediate variable U y and the lifting force U 1 required by the Z-axis direction, and the final control law of U 1、U2、U3、U4、Ux、Uy is obtained according to the obtained value ranges of the control parameters.
And determining the value range of each control parameter in the secondary correction control law by using the Lyapunov stability theory and the Young inequality.
S50, obtaining a vertical lift force value, a roll angle expected value and a pitch angle expected value according to the target position and the current position of the unmanned aerial vehicle and the final control law of the vertical lift force;
In an embodiment of the solution, a vertical lift value can be obtained according to a target position and a current position of the unmanned aerial vehicle and a final control law of vertical lift, and a roll angle expected value can be obtained based on a relationship between the vertical lift value and the final control law of the control variable including an intermediate variable (i.e., not being a control variable output control amount) And a pitch angle expected value θ d.
Referring to fig. 2, the position control system of the controller is used as an outer ring controller to construct a final control law model of the vertical lift force and the intermediate variable of the unmanned aerial vehicle based on design and solution, and according to the target position instruction output by the receiving position instruction output unit and the final control law of the current position and the vertical lift force of the unmanned aerial vehicle extracted from the four-rotor model, the lift force U 1 required in the vertical direction can be solved and output as a control quantity; in addition, the method is based on the vertical lift force value U 1, the final control law of intermediate variables and the expected rolling angle valueThe relation between the expected value theta d of the pitch angle and the expected value theta d of the pitch angle can be finally solved to calculate the expected value/>And a pitch angle expected value θ d.
S60, obtaining a roll torque value, a pitch torque value and a yaw torque value as control values according to the expected value of the yaw angle, the expected value of the roll angle and the expected value of the pitch angle of the unmanned aerial vehicle, the vertical lift force value, and the final control laws of the roll torque, the pitch torque and the yaw torque.
Referring again to fig. 2, the attitude control system of the controller acts as an inner loop controller based on the desired value of roll angle output by the position control systemAnd pitch angle expected value θ d and attitude angle ψ d of target position of the unmanned aerial vehicle output by the attitude instruction output unit, and current attitude (roll angle/>) of the unmanned aerial vehicle extracted from the four-rotor modelThe relation among the pitch angle theta, the yaw angle phi), the pitch angle expected value, the final control laws of the roll torque, the pitch torque and the yaw torque can be used for calculating a roll torque value U 2, a pitch torque value U 3 and a yaw torque value U 4, and the vertical lift value U 1 obtained by the position control system is communicated and output to a four-rotor model as a control quantity so as to realize self-adaptive control of the unmanned aerial vehicle.
In the embodiment of the application, aiming at the control problems of internal and external disturbance and input saturation limitation of the four-rotor unmanned aerial vehicle, a self-adaptive dynamic surface control algorithm based on a neural network and an anti-saturation auxiliary system is provided. Firstly, realizing good track tracking control effect of the unmanned aerial vehicle based on a dynamic surface control algorithm; under the condition that the internal and external disturbance exists in the unmanned aerial vehicle, the universal approximation capability of the RBF neural network is utilized to estimate the total disturbance and make compensation control, and the unmanned aerial vehicle achieves a good anti-disturbance effect; finally, taking into account the input saturation limit, the introduction of an anti-saturation auxiliary system ensures that the control force remains within the desired range.
S10 specifically comprises the following steps:
s11, establishing an earth coordinate system and an unmanned aerial vehicle body coordinate system;
in an embodiment of the application, a four-rotor unmanned aerial vehicle is taken as an example to describe the establishment of a nonlinear dynamics model of the unmanned aerial vehicle, and before modeling, a physical model is necessarily simplified. The unmanned aerial vehicle is simplified into a structure consisting of four horn, as shown in fig. 4. Further assuming that the quadrotor is a rigid body, the shape and mass are symmetrical about the center, and the mass remains unchanged.
First, an earth coordinate system and a body coordinate system are established. As shown in fig. 5, the ground coordinate system is defined as follows: with a point on the ground as a reference, it can be defined that the X-axis points to the east-plus horizontal direction, the Y-axis points to the north-plus horizontal direction, and the Z-axis is perpendicular to the XOY plane. Referring to fig. 6, the body coordinate system is defined as follows: one arm is selected as an Xb shaft, the Yb shaft is the direction of the other arm perpendicular to the Xb, and the Zb shaft is perpendicular to the XOY plane. Respectively define the roll angle asThe pitch angle is θ and the yaw angle is ψ.
S12, determining a transformation matrix from the body coordinate system to the ground coordinate system and unmanned aerial vehicle attitude variables (roll angle)Pitch angle θ, yaw angle ψ);
further determining a transformation matrix from the body coordinate system to the ground coordinate system as follows:
Roll angle here The pitch angle theta and the yaw angle psi are parameters for representing the relation between the machine body coordinate system and the ground coordinate system: assuming that an object exists in space, if the three-dimensional position coordinates of the object in the body coordinate system are determined, the roll angle/>, is also knownSpecific values of pitch angle theta and yaw angle phi; then by calculating the transformation matrix, the three-dimensional position coordinates of the object in the earth coordinate system can be determined. Roll angle/>, in equation (1)The pitch angle θ and the yaw angle ψ can be regarded as variables.
S13, obtaining the relation between the position acceleration and the conversion matrix and the position total disturbance under the ground coordinate system by utilizing a rigid body theory;
Under the ground coordinate system, there are:
x, y and z respectively represent the position coordinates of the unmanned aerial vehicle under the ground coordinate system, The second derivative of the three directions of x, y and z is represented, namely the acceleration of the unmanned aerial vehicle in the three coordinate directions of x, y and z, wherein f is a lift coefficient, Ω is the rotating speed of the rotor wing, and Δa is the total disturbance; i is a subscript of a cumulative sign Σ, and represents the number of unmanned aerial vehicle rotors, in this embodiment, four-rotor unmanned aerial vehicle, i is an integer between [1,4], m represents the mass of the unmanned aerial vehicle, g represents the gravitational acceleration, Δa 1 is the total interference in the Xb direction, Δa 2 is the total interference in the Yb direction, and Δa 3 is the total interference in the Zb direction.
S14, obtaining unmanned aerial vehicle attitude variable (roll angle) under organism coordinate system by utilizing rigid body theoryPitch angle θ, yaw angle ψ) acceleration and attitude total disturbance;
the machine body coordinate system comprises the following components:
Wherein l is the distance from the rotor to the center point of the unmanned aerial vehicle, d is the motor anti-torque coefficient, I x、Iy、Iz is the moment of inertia rotating around the x, y and z axes respectively, and delta a 4 is the rolling angle rotating around the x axis The total disturbance in direction Δa 5 is the total disturbance in the direction of rotation about the y-axis, i.e. pitch angle θ, and Δa 6 is the total disturbance in the direction of rotation about the z-axis, i.e. yaw angle ψ.
S15, obtaining the unmanned aerial vehicle nonlinear power model according to the definitions of vertical lift force U 1, rolling torque U 2, pitching torque U 3 and navigational yaw torque U 4 and the relation between the position acceleration and the conversion matrix and the relation between the unmanned aerial vehicle attitude acceleration and the attitude total disturbance by utilizing a Newton-Euler equation. The specific process is as follows:
And (3) making:
Finally, the following unmanned aerial vehicle nonlinear dynamics model formula is obtained:
u 1 is the lifting force in the Zb direction under the machine body coordinate system, U 2 is the torque around the Xb axis under the machine body coordinate system, and the corresponding freedom roll angle Is controlled by (a); u 3 is torque around Yb axis under the machine body coordinate system, and corresponds to theta control of pitch angle; u 4 is the torque about the Zb axis in the machine frame coordinate system, corresponding to the control of the yaw angle ψ.
S20 specifically comprises:
S21, designing a preliminary control law of each control variable according to dynamic surface control;
The preliminary control law of the vertical lift force U 1 is based on the vertical displacement z in the degree of freedom of the position of the unmanned aerial vehicle and is designed according to a dynamic surface control algorithm; the preliminary control law of the roll matrix U 2 is based on the roll angle in the unmanned plane attitude freedom degree The direction rotates and is designed according to a dynamic surface control algorithm; the preliminary control law of the pitching torque U 3 is based on the rotation of the pitching angle theta direction in the attitude freedom degree of the unmanned aerial vehicle, and is designed according to a dynamic surface control algorithm; the preliminary control law of the yaw torque U 4 is based on the yaw angle psi direction rotation in the unmanned aerial vehicle attitude freedom degree and is designed according to a dynamic surface control algorithm. The preliminary control law of the first intermediate variable U x is based on horizontal displacement x in the degree of freedom of the position of the unmanned aerial vehicle and is designed according to a dynamic surface control algorithm; the preliminary control law of the second intermediate variable U y is based on the vertical displacement y in the degree of freedom of the position of the unmanned aerial vehicle and is designed according to a dynamic surface control algorithm.
S22, introducing the RBF neural network into the preliminary control law of each control variable, determining a weight updating rule and obtaining a primary correction control law.
And constructing a network model for the total disturbance in the initial control law by using the RBF neural network, optimizing the weight of initial parameters in the model by using a correlation mechanism of the network model, obtaining an estimated model for the total disturbance when the error reaches the design requirement, substituting the estimated model into the initial control law to replace the total disturbance, and obtaining the primary correction control law.
S21 specifically comprises:
S211, constructing a dynamic surface controlled by the unmanned aerial vehicle by taking any degree of freedom x in the position or the gesture of the unmanned aerial vehicle as a subsystem, and constructing a first error surface z 1 equation by a first moment track x 1 and a first moment expected track x 1d;
then equation (2) may be:
Wherein:
(6) The equation consists of six subsystems, and the associated term for each subsystem is shown in equation (7). Each subsystem can be written in the form:
x 1 is Six subsystems can be written in the above form.
The controller design method of each subsystem is consistent, and one subsystem is taken as an example for design:
defining a first error plane:
z1＝x1-x1d (9)
Where z 1 is the track error, x 1d is the desired track;
S212, designing a second moment expected track x 2 according to subsystem starting conditions and a Lyapunov function defined by a first error face;
deriving (9):
Defining a Lyapunov stability function:
Then
And (3) design:
S213, expecting track for second time Performing first-order low-pass filtering to obtain a second moment expected track/>The relation between the low-pass filter parameters and the low-pass filter variable a;
Taking a as Low pass filter/>And satisfies the following:
The following steps are deduced:
S214, establishing a second error face z 2 equation according to the second moment track x 2 and the low-pass filtering variable;
defining a second error plane:
z2＝x2-a (16)
derivative of (16):
S215, designing a Ux preliminary control law according to a subsystem starting condition and a Lyapunov function defined by a second error face, wherein the Ux preliminary control law is expressed as a relational expression among the degree of freedom x, a first error face z 1, a second error face z 2, a low-pass filtering variable a and total disturbance delta;
Defining a Lyapunov function:
And (5) deriving to obtain:
/>
preliminarily designing a control law of Ux:
s22 specifically comprises the following steps:
s221, estimating the total disturbance delta by adopting an RBF network to obtain a mapping relation matrix between the total disturbance delta and the error input quantity z;
since Δ is unknown, the RBF network is used to estimate Δ:
Δ＝θ*ξ(e)+σ* (21)
S222, carrying out iterative computation and updating on the weights set in the mapping relation matrix according to the ideal weights set by the RBF network, and obtaining a primary correction control law.
Wherein the input isΘ * is an ideal weight, |θ *||≤θM;σ* is an approximation error, |σ *|≤σM.
The adaptive law is designed as follows:
Wherein Γ is a constant matrix and η is a small design parameter;
the primary correction control law is:
s30 specifically comprises:
S31, when the output control quantity is inconsistent with the actual input of the controlled object, outputting the actual input with the smallest difference with the control quantity as the control quantity; and accordingly, the primary correction control law obtained in the step S222 is corrected, and the secondary correction control rate is obtained.
Further consider the effect of saturation of the system input, i.e. the actual control has upper and lower limits:
-um≤u≤un (24)
When the output of the controller is inconsistent with the actual input of the controlled object, selecting an anti-saturation auxiliary design system for compensation, namely:
wherein, Where Δu=u-v, epsilon is a very small positive real number, lambda is a variable introduced to attenuate the influence of input saturation on the system, k is a parameter to be designed, u is the actual control action, and v is the control rate of the design.
The secondary correction control law is designed as follows:
s32, the degree of freedom x, y, z and the degree of freedom of the attitude of the unmanned aerial vehicle can be respectively designed according to the formula (26) Final control laws Ux, uy, U 1、U2、U3、U4 for θ, ψ:
The specific position control system comprises a first intermediate variable subsystem (output Ux), a second intermediate variable subsystem (output Uy) and a lift subsystem (output U 1); the attitude control system comprises a roll subsystem (output U 2), a pitch subsystem (output U 3), and a yaw subsystem (output U 4), wherein parameters are Epsilon, k 1,k2,z1,z2, lambda subscript x corresponds to each parameter of the first intermediate variable subsystem Ux secondary correction control law, subscript y corresponds to each parameter of the second intermediate variable subsystem Uy secondary correction control law, subscript z corresponds to each parameter of the lift subsystem U 1 secondary correction control law, subscript/>The corresponding roll subsystem U 2 corrects the control law parameters twice, the subscript symbol theta corresponds to the pitch subsystem U 3 corrects the control law parameters twice, and the subscript symbol psi corresponds to the yaw subsystem U 4 corrects the control law parameters twice.
S40 specifically comprises the following steps:
s41, carrying out stability analysis according to the sum of Lyapunov functions defined by the second dynamic error, the first-order filtering error, the weight error and the anti-saturation compensation error to respectively obtain the value range of the control parameter in the secondary control law;
According to the separability principle, the stability of each subsystem can be respectively proved to ensure the stability of the whole system. The stability analysis and parameter selection method of each subsystem are consistent, and one subsystem is taken as an example for design.
Defining a first order filter error:
defining weight errors as follows:
Defining a Lyapunov function:
/>
derivative on V 1:
The filtered derivative is:
There is a continuous non-negative continuous function at this point:
then there is:
Derivative on V 3:
Derivative on V 4:
According to
Deducing:
The following inequality exists according to the above derivation and Young inequality:
wherein, 2 μ max(Γ-1) is the maximum eigenvalue of Γ -1.
Order theR is a positive number.
The control parameter selection range is as follows:
k2＞1+r
η＞2rμmax(Γ-1) (39)
s42, verifying the value range of the control parameter according to the Lyapunov stability theory equation, and then using the value range as a final control law.
According to the lyapunov stability theory, the following conditions are satisfied:
as long as the proper r value is taken, the stability of the unmanned aerial vehicle system is ensured according to the control parameter selected in the step (39), and the dynamic performance and the robustness achieve good effects.
It should be noted that the bold parameters in the above formula are vectors.
S50 includes:
s51, obtaining a vertical lift value as control quantity output according to the target position and the current position of the unmanned aerial vehicle and the final control law of the vertical lift;
the secondary correction control law of the vertical lift force can be obtained through the calculation:
the control parameters in the control law can be obtained through stability analysis Epsilon z,k1z,k2z,z1z,z2z,λz, so as to obtain the final control law of the vertical lift U 1; the roll angle/> can be calculated based on the target position coordinates and the current position coordinates of the unmanned aerial vehicleA pitch angle θ; m and g are known amounts, whereby U 1 can be obtained.
S52, obtaining a roll angle expected value and a pitch angle expected value according to the vertical lift value and the final control laws of the first intermediate variable and the second intermediate variable.
In principle, the second correction control of the first intermediate variable Ux and the second intermediate variable Uy can be obtained by the above-described calculation:
the control parameters in the control law can be obtained through stability analysis Epsilon x,k1x,k2x,z1x,z2x,λx range of valuesThe value range of epsilon y,k1y,k2y,z1y,z2y,λy is based on the obtained U 1, and then specific values of a first intermediate variable Ux and a second intermediate variable Uy are obtained;
Order the
From equation (41), the desired roll angle can be calculatedAnd pitch angle θ d:
The Ux and Uy are specific values output by the outer ring controller, and the related declination angle psi is an actual declination angle value. In the attitude control, it is necessary to know the expected values of the three attitude angles. In practice, only the yaw angle ψ (see specifically the command of ψ of the input attitude control system in fig. 2) is given in advance with the desired value d. It is therefore necessary to solve for the expected value of the roll angle from equation (41) for Ux and Uy Expected value θ d of pitch angle. MATLAB simulation experiments prove that the technical scheme of the application is feasible and can realize the expected effect. Referring to fig. 7, the gray curve is the desired trajectory and the black curve is the actual unmanned trajectory. As can be seen from fig. 7, the unmanned aerial vehicle using the conventional PID method can stably fly, but the dynamic performance and robustness are to be improved. Referring to fig. 8, the gray curve is the expected estimate, and the black curve is the trajectory of the actual drone. As can be seen from fig. 8, the unmanned aerial vehicle adopting the method of the application realizes stable flight, and has good dynamic performance and strong robustness.
Based on the same inventive concept, the embodiment of the application provides a computer readable storage medium, which comprises various steps in the unmanned aerial vehicle self-adaptive control method flow when being loaded and executed by a processor.
The computer-readable storage medium includes, for example: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional modules is illustrated, and in practical application, the above-described functional allocation may be performed by different functional modules according to needs, i.e. the internal structure of the apparatus is divided into different functional modules to perform all or part of the functions described above. The specific working processes of the above-described systems, devices and units may refer to the corresponding processes in the foregoing method embodiments, which are not described herein.
In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical functional division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk.
The foregoing embodiments are only used to describe the technical solution of the present application in detail, but the descriptions of the foregoing embodiments are only used to help understand the method and the core idea of the present application, and should not be construed as limiting the present application. Variations or alternatives, which are easily conceivable by those skilled in the art, are included in the scope of the present application.
Claims (6)
1. The unmanned aerial vehicle self-adaptive control method is characterized by comprising the following steps of:
Constructing a preliminary control law of a control variable according to the established non-linear dynamics model of the unmanned aerial vehicle; the neural network is utilized to compensate the total disturbance suffered by the unmanned aerial vehicle in the preliminary control law to obtain a primary correction control law;
The stability analysis is carried out on the primary correction control law to obtain the value range of the control parameters in the control law; thereby obtaining the final control law of the control variable;
Obtaining a vertical lift value, a roll angle expected value and a pitch angle expected value according to the target position and the current position of the unmanned aerial vehicle and the final control law of the vertical lift;
According to the expected value of the yaw angle, the expected value of the roll angle, the expected value of the pitch angle, the lift value in the vertical direction, the roll torque, the pitch torque and the final control law of the yaw torque of the unmanned aerial vehicle, obtaining the roll torque value, the pitch torque value and the yaw torque value as control values and outputting;
the preliminary control law of the control variable is built according to the built non-linear dynamics model of the unmanned aerial vehicle; the step of compensating the total disturbance suffered by the unmanned aerial vehicle in the preliminary control law by utilizing the neural network to obtain a primary correction control law comprises the following steps of:
Designing a preliminary control law according to the dynamic surface control;
Introducing an RBF neural network into the preliminary control law, determining a weight updating rule and obtaining a primary correction control law;
The step of designing the preliminary control law according to the dynamic surface control comprises the following steps:
Establishing a first error surface equation by taking any degree of freedom of the position or the gesture of the unmanned aerial vehicle as a dynamic surface and using a first moment track and a first moment expected track;
Designing a second moment expected track according to subsystem starting conditions and Lyapunov functions defined by the first error face Carrying out low-pass filtering processing on the second moment expected track to obtain a relation between the second moment expected track and a low-pass filtering parameter and a low-pass filtering variable;
Establishing a second error face equation according to the second moment track and the low-pass filtering variable;
Designing a preliminary control law according to a Lyapunov function defined by a subsystem initial condition and a second error face, wherein the preliminary control law is expressed as a relational expression among the degree of freedom, the first error face, the second error face, a low-pass filtering variable and the total disturbance;
The step of introducing the RBF neural network into the preliminary control law, determining the weight updating rule and obtaining the primary correction control law comprises the following steps:
Estimating the total disturbance in the preliminary control law by adopting an RBF network to obtain a mapping relation matrix between the total disturbance and the error input quantity;
Performing iterative computation and updating on the weights set in the mapping relation matrix according to ideal weights set by the RBF network to obtain a primary correction control law;
After the neural network is utilized to compensate the total disturbance suffered by the unmanned aerial vehicle in the preliminary control law to obtain the primary correction control law, the method further comprises the following steps:
When the output of the control variable is inconsistent with the actual input of the controlled object, taking the actual input with the smallest difference with the output as the output of the control variable, and adding anti-saturation compensation in the primary correction control law to obtain a secondary correction control law;
The stability analysis is carried out on the secondary correction control law to obtain the value range of the control parameters in the control law; thereby obtaining the final control law of the control variable.
2. The unmanned aerial vehicle adaptive control method of claim 1, further comprising, prior to the step of constructing the preliminary control law of the control variables from the established unmanned aerial vehicle nonlinear dynamics model:
the method for establishing the unmanned aerial vehicle nonlinear dynamics model specifically comprises the following steps of:
establishing an earth coordinate system and an unmanned aerial vehicle body coordinate system;
Determining the relation between a transformation matrix from the body coordinate system to the ground coordinate system and the attitude variable of the unmanned aerial vehicle;
Obtaining the relation between the position acceleration and the conversion matrix and the position total disturbance under the ground coordinate system by utilizing the rigid body theory;
acquiring the relation between the attitude acceleration and the attitude total disturbance of the unmanned aerial vehicle under the body coordinate system by utilizing a rigid body theory;
And obtaining the unmanned aerial vehicle nonlinear power model according to the vertical lifting force, the rolling torque, the pitching torque, the navigational yaw torque, the relation between the position acceleration and the conversion matrix and the position total disturbance and the relation between the unmanned aerial vehicle gesture acceleration and the gesture total disturbance by utilizing a Newton-Euler equation.
3. The unmanned aerial vehicle adaptive control method of claim 1, wherein the step of obtaining the final control law of the control variable by obtaining the control parameter value range by performing stability analysis on the secondary correction control law comprises:
Performing stability analysis according to the sum of Lyapunov functions defined by the second dynamic error, the first-order filtering error, the weight error and the anti-saturation compensation error to respectively obtain a control parameter value range in the secondary correction control law;
Verifying the value range of the control parameter according to a Lyapunov stability theoretical equation, and then using the value range as a final control law;
The final control law of lift force, rolling torque, pitching torque and navigational deviation torque in the vertical direction is obtained; the preliminary control law of the vertical lift force is designed based on the fact that the vertical displacement in the freedom degree of the position of the unmanned aerial vehicle is a dynamic surface; the preliminary control law of the rolling torque is designed based on the fact that the rolling angle direction in the unmanned aerial vehicle attitude freedom degree rotates to be a dynamic surface; the preliminary control law of the pitching torque is designed based on the fact that the pitching angle direction in the attitude freedom degree of the unmanned aerial vehicle rotates to be a dynamic surface; the preliminary control law of the yaw torque is designed based on the fact that the yaw angle direction rotation in the unmanned aerial vehicle attitude freedom degree is a dynamic surface.
4. The unmanned aerial vehicle adaptive control method of claim 3, wherein the step of obtaining a final control law further comprises obtaining a final control law for the first intermediate variable, the second intermediate variable;
The step of obtaining the expected value of the rolling angle and the expected value of the pitch angle according to the target position and the current position of the unmanned aerial vehicle and the final control law of the vertical lifting force comprises the following steps:
obtaining a vertical lift value as control quantity output according to the target position and the current position of the unmanned aerial vehicle and the final control law of the vertical lift;
and obtaining a rolling angle expected value and a pitch angle expected value according to the vertical lift value and the final control laws of the first intermediate variable and the second intermediate variable.
5. The unmanned aerial vehicle adaptive control method of claim 4, wherein the preliminary control law of the first intermediate variable is designed based on a horizontal displacement in the degree of freedom of the position of the unmanned aerial vehicle as a dynamic plane; the preliminary control law of the second intermediate variable is designed based on the displacement in the vertical direction in the freedom of the position of the unmanned aerial vehicle as a dynamic surface.
6. An unmanned aerial vehicle adaptive control system, comprising:
the storage is used for storing the unmanned aerial vehicle self-adaptive control program;
A processor executing the steps of the unmanned aerial vehicle adaptive control method of any one of claims 1 to 5 when running the unmanned aerial vehicle adaptive control program.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2020110039183 | 2020-09-22 | ||
| CN202011003918 | 2020-09-22 |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112631320A CN112631320A (en) | 2021-04-09 |
| CN112631320B true CN112631320B (en) | 2024-04-26 |
Family
ID=75287308
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011616892.XA Active CN112631320B (en) | 2020-09-22 | 2020-12-31 | Unmanned aerial vehicle self-adaptive control method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112631320B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117891177A (en) * | 2024-03-15 | 2024-04-16 | 国网浙江省电力有限公司宁波供电公司 | Unmanned aerial vehicle controller model construction method, device, equipment and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104049640A (en) * | 2014-06-27 | 2014-09-17 | 金陵科技学院 | Unmanned air vehicle attitude robust fault tolerance control method based on neural network observer |
| CN104730920A (en) * | 2015-04-16 | 2015-06-24 | 大连海事大学 | Adaptive dynamic surface controller structure of neural network and method for designing adaptive dynamic surface controller structure |
| CN109062055A (en) * | 2018-09-10 | 2018-12-21 | 南京航空航天大学 | A kind of Near Space Flying Vehicles control system based on Back-stepping robust adaptive dynamic surface |
| CN110320807A (en) * | 2019-07-24 | 2019-10-11 | 西北工业大学 | The Elastic Vehicles data screening self-adaptation control method decomposed based on singular perturbation |
| CN110456643A (en) * | 2019-07-24 | 2019-11-15 | 西北工业大学 | Elastic Vehicles historical data learning adaptive control method based on singular perturbation |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20200064868A1 (en) * | 2016-12-06 | 2020-02-27 | Amimon Ltd. | Unmanned aerial vehicle control |
-
2020
- 2020-12-31 CN CN202011616892.XA patent/CN112631320B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104049640A (en) * | 2014-06-27 | 2014-09-17 | 金陵科技学院 | Unmanned air vehicle attitude robust fault tolerance control method based on neural network observer |
| CN104730920A (en) * | 2015-04-16 | 2015-06-24 | 大连海事大学 | Adaptive dynamic surface controller structure of neural network and method for designing adaptive dynamic surface controller structure |
| CN109062055A (en) * | 2018-09-10 | 2018-12-21 | 南京航空航天大学 | A kind of Near Space Flying Vehicles control system based on Back-stepping robust adaptive dynamic surface |
| CN110320807A (en) * | 2019-07-24 | 2019-10-11 | 西北工业大学 | The Elastic Vehicles data screening self-adaptation control method decomposed based on singular perturbation |
| CN110456643A (en) * | 2019-07-24 | 2019-11-15 | 西北工业大学 | Elastic Vehicles historical data learning adaptive control method based on singular perturbation |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112631320A (en) | 2021-04-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Xiang et al. | Robust fuzzy 3D path following for autonomous underwater vehicle subject to uncertainties | |
| CN110442020B (en) | Novel fault-tolerant control method based on whale optimization algorithm | |
| CN106647781B (en) | Control method based on Repetitive control compensation fuzzy neuron PID quadrotor | |
| Xu et al. | Reliable tracking control for under-actuated quadrotors with wind disturbances | |
| CN108445895B (en) | Robust control method for position control of tilting type three-rotor unmanned aerial vehicle | |
| Labbadi et al. | Novel robust super twisting integral sliding mode controller for a quadrotor under external disturbances | |
| Yan et al. | Consensus formation tracking for multiple AUV systems using distributed bioinspired sliding mode control | |
| CN108638068B (en) | Design method of flying robot control system with redundant mechanical arm | |
| CN112859913B (en) | Multi-quad-rotor unmanned helicopter attitude consistency optimal control method considering output constraint | |
| CN114326756A (en) | Adaptive preset performance track tracking control method considering input quantization | |
| CN112631320B (en) | Unmanned aerial vehicle self-adaptive control method and system | |
| Duan et al. | Attitude tracking control of small-scale unmanned helicopters using quaternion-based adaptive dynamic surface control | |
| Kuo et al. | Quaternion-based adaptive backstepping RFWNN control of quadrotors subject to model uncertainties and disturbances | |
| Azid et al. | Robust motion control of nonlinear quadrotor model with wind disturbance observer | |
| dos Santos et al. | An experimental validation of reinforcement learning applied to the position control of UAVs | |
| CN113961010A (en) | Four-rotor plant protection unmanned aerial vehicle tracking control method based on anti-saturation finite time self-adaptive neural network fault-tolerant technology | |
| CN117452975A (en) | Security performance cooperative formation control design method for four-rotor unmanned aerial vehicle cluster | |
| CN116483124A (en) | Anti-interference four-rotor unmanned aerial vehicle formation control method for wireless speed measurement | |
| CN108845508B (en) | CMAC-sliding mode integrated control-based semi-physical simulation control method for unmanned aerial vehicle | |
| CN112034872B (en) | Integral compensation deterministic strategy gradient control method for quad-rotor unmanned aerial vehicle | |
| Santos‐Sánchez et al. | Finite horizon nonlinear optimal control for a quadrotor: Experimental results | |
| CN113093782A (en) | Unmanned aerial vehicle designated performance attitude control method and system | |
| CN114167720A (en) | Tilting type three-rotor unmanned aerial vehicle trajectory tracking control method based on observer | |
| Zhou et al. | Energy-based trajectory tracking control of under-actuated unmanned surface vessels | |
| Sanca et al. | Stability analysis of a multirotor UAV with robust backstepping controller |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |