CN113253617A - Online self-adaptive control method for quad-rotor unmanned aerial vehicle - Google Patents
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Abstract
The invention provides an online self-adaptive control method for a quad-rotor unmanned aerial vehicle, which comprises the following steps: analyzing the nonlinear coupling characteristic of the model based on a mechanism model of the quad-rotor unmanned aerial vehicle, and introducing an intermediate variable to process the coupling problem of the model on two loops of position and attitude to obtain an outer loop position subsystem and an inner loop attitude subsystem; for the outer ring position subsystem, designing a robust controller based on a sliding mode control algorithm according to a first-order tracking error, and introducing an exponential approximation law into the controller to serve as a robust item to realize the suppression of the outer ring control system on external disturbance; for the inner ring attitude subsystem, in order to solve the modeling deviation caused by an uncertain part in a mechanism model, designing a fuzzy compensation strategy, and establishing a self-adaptive fuzzy sliding mode controller based on fuzzy compensation for real-time identification and online adjustment of model parameters; and the stability of the model of the quad-rotor unmanned aerial vehicle is proved according to the analysis of the outer ring position subsystem and the inner ring attitude subsystem.
Description
Technical Field
The invention relates to the field of control of quad-rotor unmanned aerial vehicles, in particular to an online self-adaptive control method for a quad-rotor unmanned aerial vehicle.
Background
In recent years, quad-rotor unmanned aerial vehicles are widely applied in the fields of military affairs, agriculture, search and rescue, fire fighting, environmental protection, personal entertainment and the like. The popularity of drones benefits from their safety and mobility. Compared with a helicopter and a fixed-wing unmanned aerial vehicle, the four-rotor wing unmanned aerial vehicle has the remarkable advantages of strong vertical take-off and landing capability, strong maneuverability, simple mechanical structure, low cost and the like. Furthermore, the flexible design capability of its dimensions and specifications allows the quad-rotor to be quickly adapted to the requirements of a given industry. Despite these advantages, in an actual flight environment, the ideal trajectory tracking performance of a quad-rotor is always affected by uncertain internal and external non-linearity factors. Since the four-rotor system is a typical dynamic nonlinear system, and has the characteristics of strong coupling and under-actuation, and due to the influence of body vibration and air resistance, the model has uncertainty, so that an accurate system model is difficult to obtain.
Disclosure of Invention
The invention provides an online self-adaptive control method for a quad-rotor unmanned aerial vehicle, and aims to solve the problem that the quad-rotor unmanned aerial vehicle cannot realize real-time adjustment of control parameters and body states.
In order to achieve the above object, an embodiment of the present invention provides an online adaptive control method for a quad-rotor drone, including:
analyzing the nonlinear coupling characteristic of the model based on a mechanism model of the quad-rotor unmanned aerial vehicle, and introducing an intermediate variable to process the coupling problem of the model on two loops of position and attitude to obtain an outer loop position subsystem and an inner loop attitude subsystem;
for the outer ring position subsystem, designing a robust controller based on a sliding mode control algorithm according to a first-order tracking error, and introducing an exponential approximation law into the controller to serve as a robust item to realize the suppression of the outer ring control system on external disturbance;
designing a fuzzy compensation strategy aiming at model uncertainty for the inner ring attitude subsystem, and establishing a self-adaptive fuzzy sliding mode controller based on fuzzy compensation for real-time identification and online adjustment of model parameters;
and the stability of the model of the quad-rotor unmanned aerial vehicle is proved according to the analysis of the outer ring position subsystem and the inner ring attitude subsystem.
Wherein, the step 1 specifically comprises:
the dynamic equation of a quadrotor relative to an inertial coordinate system can be generally expressed as follows according to newton mechanics and newton-lagrange equation:
decoupling the outer ring position subsystem by introducing two virtual variable sums;
wherein the content of the first and second substances,
the model of the outer ring subsystem with the applied internal and external disturbances is:
wherein the content of the first and second substances,
representing the internal uncertainty of the system in the three x, y and z directions,
representing gusts in the actual environment, external disturbances caused by sudden change factors;
decoupling the inner ring attitude subsystem;
according to the model expression in equation (1), the equation for the inner ring attitude subsystem can be expressed as
Wherein the content of the first and second substances,
。
wherein, the step 2 specifically comprises:
by introducing three virtual variables Ux, Uy and Uz, the translational motion equation can be simplified as:
two state variables x are defined1，x2Equation (5) can be rewritten as the following state space form:
wherein the content of the first and second substances,𝑈𝑥 = 𝑈1(𝑐𝑜𝑠𝜙𝑠𝑖𝑛𝜃𝑐𝑜𝑠𝜓 + 𝑠𝑖𝑛𝜙𝑠𝑖𝑛𝜓) ，𝑥1 , 𝑥2respectively representing the speed and the acceleration in the x direction and used for describing the motion state of the system in the x direction𝑥Direction;
defining a virtual variable, and introducing a first-order tracking error,
defining two error variables𝑒1𝑥, 𝑒2𝑥And is provided with
Introducing slip form surfaces
From equation (8), we obtain:
the robustness of the position control system is improved by using an exponential approximation law:
the control quantities are finally obtained as follows:
defining Lyapunov functions
And obtaining a derivative:
when it is satisfied with
So that, sliding surface s → 0, the position control subsystem is asymptotically stable;
the control quantities in the y and z directions can further be calculated:
wherein the step 3 comprises:
the method comprises the steps of solving modeling deviation caused by an uncertain part in a mechanism model by using a fuzzy compensation model, establishing a self-adaptive fuzzy sliding mode controller based on fuzzy compensation for real-time identification and online adjustment of model parameters, inhibiting buffeting of a control system on a sliding mode surface by adopting an improved super-distortion algorithm, and realizing real-time identification of parameters of a closed-loop control system by integrating the fuzzy model and a robust sliding mode controller into a unified frame.
The scheme of the invention has the following beneficial effects:
the online self-adaptive control method for the quad-rotor unmanned aerial vehicle depends on an actual mechanism model, gives consideration to both internal uncertainty and external disturbance of the model, and has good robustness. The system is decoupled into the inner and outer ring subsystems according to the nonlinear coupling characteristic of the system, the characteristics and disturbance characteristics of the subsystems are fully considered, the controller is independently designed, and the stability of the control process is effectively guaranteed. In the design of the inner ring controller, according to the thought of feedback compensation control, a fuzzy modeling method is used for self-adaptive compensation of uncertainty in the model so as to effectively restore the real system dynamics; the self-adaptive fuzzy sliding mode controller established on the basis adopts an improved super-distortion algorithm, so that buffeting of the control system on a sliding mode surface can be effectively inhibited, and the robustness of the whole closed-loop control system is improved.
Drawings
Fig. 1 is a schematic flow diagram of an online adaptive control method for a quad-rotor drone according to the present invention;
fig. 2 is a general architecture diagram of the online adaptive control method for a quad-rotor drone of the present invention;
FIG. 3 is an architecture diagram of a robust sliding mode control method of the outer ring position subsystem of the present invention;
FIG. 4 is an architecture diagram of the adaptive sliding mode control method of the inner loop attitude subsystem based on fuzzy compensation according to the present invention;
FIG. 5 is a diagram of a designed online fuzzy approximation model architecture;
FIG. 6 is a schematic diagram of the control concept of the adaptive robust sliding mode controller of the present invention;
FIG. 7 is a schematic diagram of the roll angle attitude tracking result and error of the present invention;
FIG. 8 is a schematic view of pitch angle attitude tracking results and errors of the present invention;
FIG. 9 is a schematic diagram of a course angle attitude tracking result and an error of the present invention;
FIG. 10 is a schematic diagram of white Gaussian noise introduced in the simulation of the present invention;
FIG. 11 is a diagram illustrating the trace tracking result under random noise according to the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1 and 2, an embodiment of the present invention provides an online adaptive control method for a quad-rotor drone, including: for the model characteristics of the outer ring position subsystem, a robust controller based on a sliding mode control algorithm is developed; the integral of the first-order tracking error is introduced into the controller, and an exponential approximation law is adopted as a robust term, so that the external disturbance is restrained by the outer ring control system, and the robust tracking performance of the system is effectively improved. Secondly, for an inner ring attitude subsystem, in order to solve modeling deviation caused by an uncertain part in a mechanism model, a fuzzy compensation strategy aiming at model uncertainty is designed, and a self-adaptive fuzzy sliding mode controller based on fuzzy compensation is established and used for real-time identification and online adjustment of model parameters. The sub-control systems are integrated into a unified closed-loop system, theoretical analysis and strict proof are carried out on the consistent stability of the whole system, and compared with a plurality of common methods, the superior control performance is reflected.
When the position subsystem is controlled, buffeting caused by control switching of the attitude subsystem is regarded as external interference, and integration of a first-order tracking error is introduced to improve robustness to the external interference. During attitude control, buffeting caused by control switching of the position subsystem is regarded as external interference, and fuzzy and super-distortion algorithms are introduced to ensure the robustness of the system to the interference. In this way, jitter caused by control switching is suppressed.
1) Construction of four-rotor unmanned aerial vehicle model
The dynamic equation of a quadrotor relative to an inertial coordinate system can be generally expressed as follows according to newton mechanics and newton-lagrange equation:
in actual flight, the lift of the quadrotors is mainly generated by the rotation of the propellers. Firstly, the rotating speed of each propeller is obtained through back calculation according to the gravity of the airplane, and the airplane is enabled to offset the self gravity through four propellers, so that the basic lift is obtained. Then, on the basis of the basic thrust, the controller mainly performs incremental and decremental adjustments, i.e., the rotational speeds of the four motors are adjusted according to the deviation of the target point from the current position. As shown in formula (1), the rotation information is respectively related to U2, U3 and U4; similarly, positional accelerations x ̈, y ̈, z ̈ are related to U1, m,. Practical problems may lead to difficulties in modeling and control: (1) different forms of gusts in the actual flight environment, namely sudden change, high frequency, cyclone and the like, affect the robustness of the controller; (2) the uncertainty of the model may be caused by the vibration of the body due to external factors such as air resistance. An effective method is to decouple the position and attitude parameters and develop an adaptive control algorithm based on the inner and outer ring subsystems. When the target track is tracked, the outer loop position controller calls a corresponding algorithm according to the input target track and the actual position information to control the angle of the four rotors. And transmitting the output regular speed deviation to the inner ring attitude controller. And then the inner ring adjusts the attitude of the airplane according to the deviation and transmits the thrust information to a final execution unit.
A. Outer loop position subsystem decoupling
By introducing two virtual variables and providing a decoupling method, the coupling relation between an inner ring control system and an outer ring control system is solved:
wherein the content of the first and second substances,
the model of the outer ring subsystem with the applied internal and external disturbances is:
wherein，
Representing the internal uncertainty of the system in the three x, y and z directions,
representing external disturbances caused by gusts, sudden changes, etc. in the actual environment.
B. Inner ring attitude subsystem decoupling
According to the model expression in equation (1), the equation for the inner ring attitude subsystem can be expressed as
Wherein the content of the first and second substances,
。
2) construction of outer loop position controller
As described in (3), by introducing three virtual variables Ux, Uy and Uz, the translational motion equation can be simplified as:
although model uncertainties and disturbances are unpredictable in actual flight and cannot be obtained as a priori knowledge, the effects of these factors can be mitigated by introducing control terms. Here a robust sliding mode controller is constructed to achieve precise stable motion in three dimensions as shown in fig. 3.
The design process of the controller is illustrated by taking the x direction as an example. The controller ensures that the system is asymptotically bounded by designing virtual variables, so that the control output can not only inhibit interference, but also realize asymptotic tracking of a reference signal. Two state variables x are defined1，x2Equation (5) can be rewritten as the following state space form:
wherein, = 1 (+); 1, 2 respectively represent speed and acceleration in the x direction, and are used for describing the moving state direction of the system in the x direction. Inspired by backstepping algorithm, a virtual variable is defined, and a first-order tracking error is introduced to ensure the robustness of tracking.
Defining two error variables𝑒1𝑥,𝑒2𝑥And is provided with
Introducing slip form surfaces
From equation (8), we obtain:
the robustness of the position control system is improved by using an exponential approximation law:
the control quantities are finally obtained as follows:
defining Lyapunov functions
And obtaining a derivative:
when it is satisfied with
The sliding surface s → 0, the position control subsystem is asymptotically stable.
The control quantities in the y and z directions can further be calculated:
3) construction of inner ring attitude controller
Aiming at the attitude sub-control system, an online self-adaptive fuzzy sliding mode controller is provided. As shown in fig. 4, this method uses fuzzy models and SMCs, respectively, to overcome uncertainty and external interference. In addition, in order to alleviate the buffeting problem in the conventional sliding mode control, an improved STA is introduced, and a buffeting switching function is applied to a high-order derivative of a sliding mode variable by introducing an integral term and an exponential term. On the basis, an online self-adaptive strategy is designed, a fuzzy model and a robust sliding mode controller are integrated into a unified frame, and real-time identification of control system parameters is realized.
A. Fuzzy approximation system
Since it is difficult to obtain an accurate f (t, q, q) equation, there must be a fuzzy system f ̂ (t, q, q ̇) height approaching f (t, q, q ̇) according to the general approximation theorem. Thus, a two-dimensional fuzzy controller approximation f (t, q, q ̇) may be constructed. To ensure the accuracy of the approximation, it is defined here as the approximation error ε.
As can be seen from the equation (4), the attitude control system has three angle variables, which should be considered comprehensively when designing the fuzzy system. Here we use pitch angle ϕ as an example. A detailed blurring system is shown in fig. 5.
Assuming that A1 and A2 represent fuzzy result sets of φ 1 and φ 2, respectively, a fuzzy rule is established as follows:
in fuzzy system, a single-value fuzzy device is used to calculate rule result and function value
Corresponding to the maximum value of the membership function. Furthermore, with the aid of a product inference engine, the inference conclusion can be stated as
. Thus, the system output obtained based on the center-averaged deblurring is:
defining parameters𝜔𝜙And introduce a new weight𝜁(𝜙) The fuzzy output may then be further converted into:
then, l1l2(l1 = 1,2, ⋯ , 𝑚; l2 =1,2, ⋯ , 𝑛) Weight of Member𝜁(𝜙) Can be expressed as:
suppose that
The following can be obtained:
where ε represents the approximation error.
Definition of
Comprises the following steps:
for the
Comprises the following steps:
the dynamic attitude equation can be rewritten as
（19）
B. Self-adaptive sliding film controller based on fuzzy compensation
With the former fuzzy approximation system, model uncertainty of the system can be compensated. In order to obtain satisfactory tracking accuracy and robust performance against external interference, an adaptive robust controller is further provided. In the controller, an improved STA is introduced as a robust term to relieve the buffeting problem of the traditional sliding mode controller. The robust approximation law changes with the real-time feedback state and the fuzzy approximation term. The structure of the robust sliding mode algorithm is shown in fig. 6.
Introducing slip form surfaces
And is provided with
. Thus, sqCan be further expressed as:
it is well known that when sliding the mode function sq→ 0, the altitude error e and its derivative exponentially converge to zero. Thus, can obtain
An improved STA algorithm is proposed as a robust term of the SMC, and inherits the characteristics of the traditional linear and nonlinear STAs in the aspect of interference suppression so as to overcome buffeting of a second-order sliding mode and improve the robustness of a system to external interference:
the control quantities are finally obtained as follows:
defining Lyapunov functions
And obtaining a derivative:
it is clear that,
is satisfied with
(ii) a Furthermore, when satisfying
This is true. Therefore, only calculation is needed here𝑠𝑞The boundary condition of 0 may be satisfied. The final derived boundary conditions are as follows:
and finally, integrating the sub-control systems into a unified closed-loop system, so that the precise and stable control of the quad-rotor unmanned aerial vehicle under the conditions of uncertain internal parameters and external disturbance can be realized.
The online self-adaptive control method for the quad-rotor unmanned aerial vehicle depends on an actual mechanism model, gives consideration to both internal uncertainty and external disturbance of the model, and has good robustness. The system is decoupled into the inner and outer ring subsystems according to the nonlinear coupling characteristic of the system, the characteristics and disturbance characteristics of the subsystems are fully considered, the controller is independently designed, and the stability of the control process is effectively guaranteed. In the design of the inner ring controller, according to the thought of feedback compensation control, a fuzzy modeling method is used for self-adaptive compensation of uncertainty in the model so as to effectively restore the real system dynamics; the self-adaptive fuzzy sliding mode controller established on the basis adopts an improved super-distortion algorithm, so that buffeting of the control system on a sliding mode surface can be effectively inhibited, and the robustness of the whole closed-loop control system is improved.
The simulation and comparison experiments of the present invention were developed based on three common control algorithms. This was done to show its implementation in detail and to verify the validity of the proposed method. The parameters of the quadrotors used in the simulation are shown in table 1.
TABLE 1 quad-rotor correlation
1. Attitude tracking simulation
In order to illustrate the robustness and convergence performance of the proposed fuzzy adaptive sliding mode controller, we take the attitude subsystem as an example, and perform comparative simulation on the attitude tracking effect and the input stability of the attitude subsystem.
(1) Cascade PID controller (PID)
(2) Improved sliding mode controller (M-SOSM)
(3) Fuzzy sliding mode controller
(4) Fuzzy adaptive sliding mode controller (AdapFuzzy M-SOSM)
Of the three controllers, the cascade PID controller comprises a double closed-loop PID control strategy of angular velocity and angular velocity. Sliding mode control of the M-SOSM controller and the fuzzy M-SOSM controller is optimized by adopting an improved super-distortion algorithm (STA), and the algorithm is the same as the robust term of the fuzzy self-adaptive M-SOSM controller. This is also to demonstrate robustness against interference and on-line approximation performance. The model uncertainty of the pose subsystem is chosen as:
as shown in equation (27), the multi-frequency function u (t) and the sinusoidal signal τ (t) are respectively used as a frequency conversion reference signal and a high-frequency dynamic external interference.
The parameters used in the comparative simulation are shown in table 2:
TABLE 2 control parameters used in attitude simulation procedure
The fuzzy membership functions are illustrated as follows:
when selecting the membership weight Wq, the tracking effect is found to be the same as the initial tracking effect before the tracking process is stableThe starting value increases and decreases. But has no effect on the tracking convergence time. This means that when selecting the initial value of Wq, a smaller initial value should be selected as much as possible. Here, when the membership weight takes on a value of
The tracking effect and tracking error for the three attitude angles are shown in fig. 7-9. As can be seen from the figure, the high frequency interference can be effectively relieved by adopting the improved supertwist algorithm. Furthermore, the tracking error of the controller decreases very fast, converging to zero within 0.5s, compared to M-SOSM and fuzzy M-SOSM. This means that the introduction of adaptive laws and fuzzy systems greatly improves the tracking accuracy. Compared with a PID (proportion integration differentiation) cascade controller and a second-order sliding mode controller, the self-adaptive control strategy is proved to be capable of achieving a satisfactory tracking effect only by carrying out minimum adjustment on time (nearly 0.6 s). In addition, the tracking errors of other algorithms are respectively in the sum [ -0.7,0.4 ]]、[-0.3,0.3]And [0,0.3]In the meantime. The Root Mean Square Error (RMSE) of the comparative simulations is quantitatively summarized in table 3. Given these results, and compared to the cascaded PID, M-SOSM, and fuzzy M-SOSM, the controller was designed to have faster convergence speed and stability in attitude tracking stability.
TABLE 3 quantitative comparison of tracking Performance
2. Trajectory tracking simulation
The effectiveness and the mobility of the control strategy are verified through trajectory tracking simulation. The object trajectory used is a cylindrical spiral, as shown in equation (29),
the simulation time is 20s, and the initial position coordinates and the attitude of the four rotors are set to be [ x, y, z ] = [ 000 ]; further, [ ϕ, θ, ψ ] = [ 000 ] represents uncertainty of the model. Assume the model uncertainty part as follows:
two forms of gusts were proposed in the simulation: high frequency random perturbations (high frequency sinusoidal signals) and multi-frequency random perturbations (square wave signals). The interference used in the tracking simulation includes the following two parts:
relevant studies show that the parameter cx does not affect the stability of the control system, but has a direct relation with the convergence time. If the parameter cx is too large, strong buffeting can be generated when the convergence speed is too high; if the parameter a is too small, the convergence time is longer. Therefore, in this simulation, we used the empirical value cx = 5. The fuzzy basis vectors for the height and attitude controllers are w = [0.1 one (75, 1) ] and w = [ zeros (25, 1) ]. In addition, because actual factors such as measurement noise, navigation error and the like can influence the control performance to a certain extent, additional tracking simulation is carried out in a random noise environment, and the effectiveness of the proposed control strategy is verified. In the simulation, relevant parameters such as simulation time, initial position coordinates, target trajectory, model uncertainty, external disturbance and the like are analyzed, and the results are shown in fig. 10 to 11. The random noise of fig. 10 reflects the simulation of measurement noise and navigation errors in an actual environment. Referring to the processing of measurement errors in some documents, when factors such as measurement techniques, people and instruments are ignored, the values and signs of the measurement errors randomly vary in an unpredictable manner, following a normal distribution. Therefore, in the simulation, we chose white gaussian noise as the measurement noise, which obeyed a normal distribution of 0 means and 0.05 variance. The random number generator generates noise with a sampling period of 0.001 s. Fig. 11 shows the result of trajectory tracking. The result shows that the control system has stable tracking performance despite Gaussian random noise and can quickly converge and track the cylindrical spiral track. According to the tracking error, the position response of the quad-rotor unmanned aerial vehicle to random noise and external interference is in a fluctuation trend, the amplitude is very small (is limited to +/-0.1 m), and the suppression effect of the proposed controller on the random noise is proved. Table 4 shows confidence indicators in x, y, z directions with 0.95 confidence for random noise.
TABLE 4 confidence level under random measurement noise (95%)
To sum up, to four rotor unmanned aerial vehicle at the accurate trail tracking problem under time-varying model uncertainty and external disturbance, on the basis of modeling four rotor unmanned aerial vehicle kinematics and dynamics mechanism, carry out the decoupling zero to four rotor unmanned aerial vehicle's interior outer loop model, effectively solve the coupling problem between four rotor unmanned aerial vehicle position parameter and the gesture parameter. Firstly, developing a robust controller based on a sliding mode control algorithm for the model characteristics of an outer ring position subsystem; the integral of the first-order tracking error is introduced into the controller, so that the robust tracking performance of the system to external interference is effectively improved. Secondly, for the inner ring attitude subsystem, a fuzzy algorithm and sliding mode control are combined, an adaptive fuzzy sliding mode controller based on an improved STA algorithm is designed, and real-time identification and adjustment of parameters are effectively achieved while robustness of the system to external interference is guaranteed. Finally, these sub-control systems are integrated into a unified closed-loop system. When the position subsystem is controlled, buffeting caused by control switching of the attitude subsystem is regarded as external interference, and integration of a first-order tracking error is introduced to improve robustness to the external interference. During attitude control, buffeting caused by control switching of the position subsystem is regarded as external interference, fuzzy and super-distortion algorithms are introduced, robustness of the system to the interference is guaranteed, and buffeting caused by control switching is effectively restrained.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (4)
1. An online adaptive control method for a quad-rotor drone, comprising:
analyzing the nonlinear coupling characteristic of the model based on a mechanism model of the quad-rotor unmanned aerial vehicle, and introducing an intermediate variable to process the coupling problem of the model on two loops of position and attitude to obtain an outer loop position subsystem and an inner loop attitude subsystem;
for the outer ring position subsystem, designing a robust controller based on a sliding mode control algorithm according to a first-order tracking error, and introducing an exponential approximation law into the controller to serve as a robust item to realize the suppression of the outer ring control system on external disturbance;
designing a fuzzy compensation strategy aiming at model uncertainty for the inner ring attitude subsystem, and establishing a self-adaptive fuzzy sliding mode controller based on fuzzy compensation for real-time identification and online adjustment of model parameters;
and the stability of the model of the quad-rotor unmanned aerial vehicle is proved according to the analysis of the outer ring position subsystem and the inner ring attitude subsystem.
2. An online adaptive control method for a quad-rotor unmanned aerial vehicle according to claim 1, wherein the step 1 specifically comprises:
the dynamic equation of a quadrotor relative to an inertial coordinate system can be generally expressed as follows according to newton mechanics and newton-lagrange equation:
decoupling the outer ring position subsystem by introducing two virtual variable sums;
wherein the content of the first and second substances,
the model of the outer ring subsystem with the applied internal and external disturbances is:
wherein the content of the first and second substances,
representing the internal uncertainty of the system in the three x, y and z directions,
representing gusts in the actual environment, external disturbances caused by sudden change factors;
decoupling the inner ring attitude subsystem;
according to the model expression in equation (1), the equation for the inner ring attitude subsystem can be expressed as
Wherein the content of the first and second substances,
。
3. an online adaptive control method for a quad-rotor drone according to claim 1, characterized in that said step 2 comprises in particular:
by introducing three virtual variables Ux, Uy and Uz, the translational motion equation can be simplified as:
two state variables x are defined1，x2Equation (5) can be rewritten as the following state space form:
wherein the content of the first and second substances,𝑈𝑥 = 𝑈1(𝑐𝑜𝑠𝜙𝑠𝑖𝑛𝜃𝑐𝑜𝑠𝜓 + 𝑠𝑖𝑛𝜙𝑠𝑖𝑛𝜓) ，𝑥1 , 𝑥2respectively representing the speed and the acceleration in the x direction and used for describing the motion state of the system in the x direction𝑥Direction;
defining a virtual variable, and introducing a first-order tracking error,
defining two error variables𝑒1𝑥, 𝑒2𝑥And is provided with
Introducing slip form surfaces
From equation (8), we obtain:
the robustness of the position control system is improved by using an exponential approximation law:
the control quantities are finally obtained as follows:
defining Lyapunov functions
And obtaining a derivative:
when it is satisfied with
So that, sliding surface s → 0, the position control subsystem is asymptotically stable;
the control quantities in the y and z directions can further be calculated:
4. an online adaptive control method for a quad-rotor drone according to claim 1, wherein said step 3 comprises:
the method comprises the steps of solving modeling deviation caused by an uncertain part in a mechanism model by using a fuzzy compensation model, establishing a self-adaptive fuzzy sliding mode controller based on fuzzy compensation for real-time identification and online adjustment of model parameters, inhibiting buffeting of a control system on a sliding mode surface by adopting an improved super-distortion algorithm, and realizing real-time identification of parameters of a closed-loop control system by integrating the fuzzy model and a robust sliding mode controller into a unified frame.
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|---|---|---|---|---|
| CN113721465A (en) * | 2021-08-30 | 2021-11-30 | 东南大学 | Plug-and-play unmanned aerial vehicle self-adaptive flight control system and method |
| CN114296471A (en) * | 2021-11-17 | 2022-04-08 | 湖北航天飞行器研究所 | Unmanned aerial vehicle accurate landing control method based on full-strapdown downward-looking camera |
| CN117908384A (en) * | 2024-03-19 | 2024-04-19 | 南京航空航天大学 | Adaptive sliding mode-based cascade control method and system for variant aircraft |
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2021
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| BOWEN XU 等: "An Online Adaptive Control Strategy for Trajectory Tracking of Quadrotors Based on Fuzzy Approximation and Robust Sliding Mode Algorithm", 《IEEE》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113721465A (en) * | 2021-08-30 | 2021-11-30 | 东南大学 | Plug-and-play unmanned aerial vehicle self-adaptive flight control system and method |
| CN113721465B (en) * | 2021-08-30 | 2023-10-31 | 东南大学 | Unmanned aerial vehicle self-adaptive flight control system and method capable of realizing plug and play |
| CN114296471A (en) * | 2021-11-17 | 2022-04-08 | 湖北航天飞行器研究所 | Unmanned aerial vehicle accurate landing control method based on full-strapdown downward-looking camera |
| CN114296471B (en) * | 2021-11-17 | 2024-05-24 | 湖北航天飞行器研究所 | Unmanned aerial vehicle accurate landing control method based on full strapdown downward-looking camera |
| CN117908384A (en) * | 2024-03-19 | 2024-04-19 | 南京航空航天大学 | Adaptive sliding mode-based cascade control method and system for variant aircraft |
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