CN118276444A - Four-rotor unmanned aerial vehicle path tracking control method based on nonlinear model prediction - Google Patents
Four-rotor unmanned aerial vehicle path tracking control method based on nonlinear model prediction Download PDFInfo
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Abstract
The invention provides a four-rotor unmanned aerial vehicle path tracking control method based on nonlinear model prediction, and belongs to the technical field of unmanned aerial vehicle automatic control. Comprising the following steps: setting a reference path, wherein the reference path is a desired state quantity set; establishing a four-rotor unmanned aerial vehicle motion mathematical model; acquiring the rotating speeds of 4 propeller motors, and inputting the rotating speeds into the four-rotor unmanned aerial vehicle motion mathematical model to predict the state quantity at the next moment; performing feedback compensation on the predicted state quantity according to the current state quantity of the four-rotor unmanned aerial vehicle to obtain a corrected state quantity; acquiring an expected state quantity corresponding to the next moment in the reference path, substituting the expected state quantity and the correction state quantity into an objective function, and solving a control quantity of the four-rotor unmanned aerial vehicle at the next moment by using the objective function; and the control quantity at the next moment is acted on the quadrotor unmanned aerial vehicle.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle automatic control, in particular to a four-rotor unmanned aerial vehicle path tracking control method based on nonlinear model prediction.
Background
With the rapid development of the electric power technology in China, the timely inspection of the electric equipment in the transformer substation becomes an important guarantee for the safe and reliable operation of the transformer substation. The four-rotor unmanned aerial vehicle has the characteristics of higher flying height, wide visual angle, effective elimination of inspection dead angles and the like, and becomes high-efficiency airborne equipment for substation inspection. The inspection of the substation unmanned aerial vehicle is mainly carried out outdoors, and because the outdoor environment interference such as gusts exists, the control input and the state output of the four-rotor unmanned aerial vehicle have the characteristics of constraint and the like, the four-rotor unmanned aerial vehicle is controlled by a controller strategy with high performance.
Aiming at external disturbance and constraint conditions of the four-rotor unmanned aerial vehicle, students at home and abroad have studied quite much in recent years. ZHOU Laihong et al in 2018 propose a track tracking control of a four-rotor unmanned aerial vehicle based on an improved back-stepping method, which is used for resisting constant value interference and variable value interference by adding an error integral and a saturation function to design an integral saturation back-stepping control strategy; wu Yuewen et al in 2022 propose a sliding mode active disturbance rejection attitude controller design of a four-rotor unmanned aerial vehicle, wherein the active disturbance rejection controller is designed by an inner loop and an outer loop through an inner loop control algorithm, and the system response speed and the anti-disturbance performance are improved by a non-singular terminal sliding mode controller designed by the outer loop; zhang Jianzhong et al in 2019 propose a track tracking control method of a four-rotor unmanned aerial vehicle based on an extended state observer, which estimates disturbance items by introducing a linear extended state observer and compensates by combining a backstepping controller, so that the anti-interference capability of the four-rotor unmanned aerial vehicle is improved; the three algorithms are all control algorithms aiming at external interference environments, and can well resist interference aiming at external interference, so that good track tracking control is performed, but the four-rotor unmanned aerial vehicle is a nonlinear model, and the actuator has the characteristic of multiple constraints, and in actual control, multiple constraints of the actuator are needed to be considered, so that the defects of insufficient robustness and inaccurate system model of a control strategy under the constraint condition are overcome.
Wang Yingxun et al in 2024 propose an integrated control method for four-rotor maneuvering trajectory tracking under multiple constraint conditions, wherein a MPC control method is adopted to process multiple constraint conditions to track a complex trajectory, and a four-rotor unmanned aerial vehicle based on a model predictive control algorithm (MPC) can perform path tracking under the condition of constraint and external environment disturbance. However, the four-rotor unmanned aerial vehicle is a nonlinear model, and the MPC is designed based on the linear model, so that the nonlinear model needs to be linearized, certain errors can be generated in the linearization process, the control effect is poor, and a path cannot be tracked well finally.
Disclosure of Invention
In view of the above, the invention provides a four-rotor unmanned aerial vehicle path tracking control method based on nonlinear model prediction, which can avoid linearization errors and perform good path tracking control on the condition that the four-rotor unmanned aerial vehicle with a nonlinear model is constrained.
The technical scheme adopted by the embodiment of the invention for solving the technical problems is as follows:
A four-rotor unmanned aerial vehicle path tracking control method based on nonlinear model prediction, wherein the control object is an X-type four-rotor unmanned aerial vehicle, comprising:
step S1, setting a reference path, wherein the reference path is a desired state quantity set;
step S2, establishing a four-rotor unmanned aerial vehicle motion mathematical model;
s3, obtaining the rotating speeds of 4 propeller motors, and inputting the rotating speeds into the four-rotor unmanned aerial vehicle motion mathematical model to predict the state quantity at the next moment;
Step S4, carrying out feedback compensation on the predicted state quantity according to the current state quantity of the quadrotor unmanned aerial vehicle to obtain a corrected state quantity;
S5, acquiring an expected state quantity corresponding to the next moment in the reference path, substituting the expected state quantity and the correction state quantity into an objective function, and solving a control quantity of the four-rotor unmanned aerial vehicle at the next moment by using the objective function;
and S6, applying the control quantity at the next moment to the quadrotor unmanned aerial vehicle.
Preferably, the input of the four-rotor unmanned aerial vehicle motion mathematical model is the rotation speed [ omega 1,ω2,ω3,ω4 ] of 4 propeller motors, the output is the predicted state quantity X P,X, y and z are three directional speeds respectively,Θ, ψ are respectively expressed as pitch angle, yaw angle, roll angle; in the four-rotor unmanned aerial vehicle motion mathematical model:
Under the machine body coordinate system, the four-rotor unmanned aerial vehicle generates a rotation moment [ M x,My,Mz ] around the machine body three axes through high-speed rotation of four propellers, and the rotation moment is expressed as:
In the operation process of the unmanned aerial vehicle, the total pulling force along the vertical direction is as follows:
Wherein, L a＝Lb = L is the length of the horn, α is the thrust coefficient of the unmanned aerial vehicle, and β is the rotor anti-torque coefficient;
The nonlinear dynamics model of the four-rotor unmanned aerial vehicle is as follows:
Wherein m is the mass of the four-rotor unmanned aerial vehicle; g is gravity acceleration; i x,Iy,Iz is the triaxial moment of inertia of the quadrotor unmanned aerial vehicle respectively; representing the positional acceleration of the quadrotor unmanned, Representing the angular acceleration of the quadrotor unmanned aerial vehicle; by aligningAndX P is obtained by twice integration.
Preferably, the step S4 includes:
step S41, obtaining a predicted state quantity X p (k+1) obtained by predicting the k+1 time in the step S3;
Step S42, calculating an error e (k) according to the current actual state quantity Y (k) of the quadrotor unmanned aerial vehicle:
e(k)＝Y(k)-Xp(k+1)
Y(k)＝X(k)
in step S43, the correction state quantity X b (k+1) at time k+1 is calculated:
Xb(k+1)＝Xp(k+1)+Kb·e(k)
where K b is the feedback coefficient.
Preferably, the step S5 includes:
step S51, obtaining an expected state quantity X r (k+1) corresponding to the k+1 time in the reference path;
Step S52, substituting the desired state quantity and the correction state quantity into an objective function J, and solving an optimal control sequence { U (k+i) } using the objective function: wherein the discrete four rotor nonlinear system satisfies:
The objective function J is expressed as:
Wherein Q and R are respectively a state weight matrix and an input weight matrix; u (k+i) is the input of the system at time k+i;
wherein, the constraint condition of the solving process comprises:
Wherein U min is the minimum input matrix, and U max is the maximum input matrix; x min is the minimum state matrix of the four-rotor unmanned aerial vehicle, and X max is the maximum state matrix of the four-rotor unmanned aerial vehicle;
and step S53, taking the first element U (k+1) in the optimal control sequence { U (k+i) } as the control quantity of the four-rotor unmanned aerial vehicle at the next moment.
Preferably, the control amount U of the quadrotor unmanned aerial vehicle is expressed as:
According to the technical scheme, the four-rotor unmanned aerial vehicle path tracking control method based on nonlinear model prediction provided by the embodiment of the invention is characterized in that a reference path is firstly set; establishing a four-rotor unmanned aerial vehicle motion mathematical model; acquiring the rotating speeds of 4 propeller motors, and inputting the rotating speeds into a four-rotor unmanned aerial vehicle motion mathematical model to predict the state quantity at the next moment; performing feedback compensation on the predicted state quantity according to the current state quantity of the four-rotor unmanned aerial vehicle to obtain a corrected state quantity; acquiring an expected state quantity corresponding to the next moment in a reference path, substituting the expected state quantity and the correction state quantity into an objective function, and solving a control quantity of the four-rotor unmanned aerial vehicle at the next moment by using the objective function; and (5) the control quantity at the next moment is acted on the quadrotor unmanned aerial vehicle. By the method, linearization errors can be avoided, good path tracking control is performed on the condition that the four-rotor unmanned aerial vehicle of the nonlinear model is constrained, and guarantee is provided for the substation inspection stability of the four-rotor unmanned aerial vehicle.
Drawings
Fig. 1 is a flowchart of a method for controlling path tracking of a four-rotor unmanned aerial vehicle based on nonlinear model prediction.
Fig. 2 is a schematic diagram of a four-rotor unmanned aerial vehicle body coordinate system.
FIG. 3 is a control block diagram of a nonlinear model predictive algorithm.
Fig. 4 is a first reference trajectory diagram.
Fig. 5 is an effect graph of tracking a track in three directions of x, y and z based on the reference track of fig. 3.
Fig. 6 is a tracking error map based on the reference track of fig. 3.
Fig. 7 is a graph of control inputs based on the reference trajectory of fig. 8.
Fig. 8 is a second reference trajectory diagram.
Fig. 9 is an effect graph of tracking a track in three directions of x, y, and z based on the reference track of fig. 8.
Fig. 10 is a tracking error map based on the reference track of fig. 8.
Fig. 11 is a graph of control inputs based on the reference trajectory of fig. 8.
Fig. 12 is a schematic device structure diagram of a hardware running environment according to an embodiment of the present application.
Detailed Description
The technical scheme and technical effects of the present invention are further elaborated below in conjunction with the drawings of the present invention.
The invention provides a four-rotor unmanned aerial vehicle path tracking control method based on nonlinear model prediction, wherein the control object is an X-type four-rotor unmanned aerial vehicle, and certain assumption is made for the unmanned aerial vehicle: ① The structure of the quadrotor unmanned aerial vehicle is completely symmetrical; ② The gravity center of the rotor is positioned at the geometric center of the quadrotor unmanned plane; ③ The unmanned aerial vehicle is a complete rigid body. The body coordinate system established based on the quadrotor unmanned aerial vehicle is shown in fig. 2.
As shown in fig. 1, the method for controlling path tracking of the four-rotor unmanned aerial vehicle based on nonlinear model prediction comprises the following steps:
Step S1, setting a reference path which is a desired state quantity set;
step S2, establishing a four-rotor unmanned aerial vehicle motion mathematical model;
s3, obtaining the rotating speeds of 4 propeller motors, and inputting the rotating speeds into a four-rotor unmanned aerial vehicle motion mathematical model to predict the state quantity at the next moment;
Step S4, carrying out feedback compensation on the predicted state quantity according to the current state quantity of the quadrotor unmanned aerial vehicle to obtain a corrected state quantity;
s5, acquiring an expected state quantity corresponding to the next moment in the reference path, substituting the expected state quantity and the correction state quantity into an objective function, and solving a control quantity of the four-rotor unmanned aerial vehicle at the next moment by using the objective function;
And S6, applying the control quantity at the next moment to the quadrotor unmanned aerial vehicle.
Preferably, the input of the four-rotor unmanned aerial vehicle motion mathematical model is the rotation speed [ omega 1,ω2,ω3,ω4 ] of 4 propeller motors, the output is the predicted state quantity X P,X, y and z are three directional speeds respectively,Θ, ψ are respectively expressed as pitch angle, yaw angle, roll angle; in the four rotor unmanned aerial vehicle motion mathematical model:
Under the machine body coordinate system, the four-rotor unmanned aerial vehicle generates a rotation moment [ M x,My,Mz ] around the machine body three axes through high-speed rotation of four propellers, and the rotation moment is expressed as:
In the operation process of the unmanned aerial vehicle, the total pulling force along the vertical direction is as follows:
Wherein, L a＝Lb = L is the length of the horn, α is the thrust coefficient of the unmanned aerial vehicle, and β is the rotor anti-torque coefficient;
the nonlinear dynamics model of the four-rotor unmanned aerial vehicle is obtained by deducting the Euler Lagrange equation:
Wherein m is the mass of the four-rotor unmanned aerial vehicle; g is gravity acceleration; i x,Iy,Iz is the triaxial moment of inertia of the quadrotor unmanned aerial vehicle respectively; Indicating the positional acceleration of the quadrotor unmanned, The angular acceleration of the quadrotor unmanned plane is represented; by aligningAndX P is obtained by twice integration. Substituting the formula (1) into the formula (3) to obtain a final nonlinear dynamics model of the quadrotor unmanned aerial vehicle as a prediction model of the controller.
Preferably, step S4 includes:
Step S41, obtaining a predicted state quantity X p (k+1) obtained by predicting the k+1 time in step S3;
step S42, calculating an error e (k) according to the current actual state quantity Y (k) of the quadrotor unmanned aerial vehicle:
e(k)＝Y(k)-Xp(k+1) (4)
Y(k)＝X(k) (5)
in step S43, the correction state quantity X b (k+1) at time k+1 is calculated:
Xb(k+1)＝Xp(k+1)+Kb·e(k) (6)
where K b is the feedback coefficient.
Preferably, step S5 includes:
Step S51, obtaining an expected state quantity X r (k+1) corresponding to the k+1 moment in the reference path;
Step S52, substituting the desired state quantity and the corrected state quantity into the objective function J, and solving the optimal control sequence { U (k+i) } using the objective function: wherein the discrete four rotor nonlinear system satisfies:
the objective function J is expressed as:
Wherein Q and R are respectively a state weight matrix and an input weight matrix, which are both in a diagonal matrix form; u (k+i) is the input of the system at time k+i;
therefore, the NMPC solving problem can be converted into a nonlinear programming problem according to the objective function and the prediction model. Constraints of the solving process include:
Wherein U min is the minimum input matrix, and U max is the maximum input matrix; x min is the minimum state matrix of the four-rotor unmanned aerial vehicle, and X max is the maximum state matrix of the four-rotor unmanned aerial vehicle; the first part of the objective function is to find the optimal solution by minimizing J so that the predicted state Xp (k) reaches the desired state Xr (k) as much as possible in N times; the second part is then the energy constraint on the control input so that the desired state is reached with less energy; (J takes the minimum value of U (k)) and secondly, in order to prevent the situation that the voltage of the motor of the four rotors is too large to cause the loss of control of the four rotors, a certain constraint is carried out on the control input quantity.
Step S53, taking the first element U (k+1) in the optimal control sequence { U (k+i) } as the control quantity of the four-rotor unmanned aerial vehicle at the next moment.
Preferably, the control amount U of the quadrotor unmanned aerial vehicle is expressed as:
The NLP problem is solved herein using a sequential quadratic programming method (Sequential quadratic programming, SQP). Solving the NLP problem at the K moment to obtain an optimal control sequence U (K+i), and enabling the first element U (K+1) to act on the quadrotor unmanned aerial vehicle. In the actual control process, a certain deviation exists between the predicted value of the predicted model and the state of the actual four rotors, so that a feedback process is designed to correct the predicted value to a certain extent. The error e (K) =y (K) -X p (k+1) is introduced here, and then weighted by the feedback coefficient K b to obtain the corrected prediction model output X b(k+1)＝Xp(k+1)+Kb ·e (K). And then carrying the corrected state X b (k+1) of the quadrotor unmanned aerial vehicle at the next moment into an NLP problem to solve, and obtaining an optimal control sequence at the K+1 moment. Thus, the final control effect is achieved.
The control algorithm provided by the invention carries out numerical simulation in Matlab, and sets the disturbance in the x and y directions as random disturbance, and the input constraint is set within 10 r/s.
Taking the spiral descending reference track as an example, the spiral descending reference track, the simulation result and the input are shown in fig. 4-7.
From the tracking effect of the spiral-down reference trajectory set as above, the dotted line is the actual state, and the solid line is the reference state. It can be seen from fig. 5 that by the algorithm of the present patent, a good tracking of the spiral descent trajectory in space can be achieved. And random disturbance is added in the propeller thrust, as can be seen from fig. 6, errors of X, Y and Z three axes all generate convergence within 3s, and the tracking error of the final three axes can be kept within 0.01 m.
Secondly, as can be seen from the control input curve of fig. 7, the designed controller can control the rotating speed of the propeller to be within 10r/s, so that the condition that the four rotors are out of control due to the overlarge rotating speed of the four-rotor motor is avoided.
Taking the horizontal 8-word reference track as an example, the horizontal 8-word reference track, simulation results and inputs are shown in fig. 8-11.
From the tracking effect of the above-set planar "8-shaped" reference track, the dotted line is the actual state, and the solid line is the reference state. It can be seen from fig. 9 that a good tracking of the "8-word" trajectory in space can be achieved by the algorithm of the present patent. And random disturbance is added in the propeller thrust, as can be seen from fig. 10, errors of X, Y and Z three axes all generate convergence within 2s, and finally the tracking error of the three axes can be kept within 0.1 m.
Secondly, as can be seen from the control input curve of fig. 11, the designed controller can control the rotating speed of the propeller to be within 10r/s, so that the condition that the four rotors are out of control due to the overlarge rotating speed of the four-rotor motor is avoided.
Referring to fig. 12, fig. 12 is a schematic device configuration diagram of a hardware running environment according to an embodiment of the present application.
As shown in fig. 12, the method and the device for controlling path tracking of the four-rotor unmanned aerial vehicle based on nonlinear model prediction may include: a processor 1201, such as a CPU, memory 1205, and a communication bus 1202. Wherein a communication bus 1202 is used to enable communication among the processor 1201 and a memory 1205. The memory 1205 may be a high-speed RAM memory or a stable memory (non-volatilememory), such as a disk memory. The memory 1205 may alternatively be a storage device separate from the processor 1201 described above.
Optionally, the four-rotor unmanned aerial vehicle path tracking control method based on nonlinear model prediction may further include a rectangular user interface, a network interface, a camera, an RF (Radio Frequency) circuit, a sensor, an audio circuit, a WiFi module, and the like. The rectangular user interface may include a Display screen (Display), an input sub-module such as a Keyboard (Keyboard), and the optional rectangular user interface may also include a standard wired interface, a wireless interface. The network interface may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface).
Those skilled in the art will appreciate that the configuration of the four-rotor unmanned aerial vehicle path tracking control method device based on nonlinear model prediction shown in fig. 12 does not constitute a limitation on the four-rotor unmanned aerial vehicle path tracking control method device based on nonlinear model prediction, and may include more or fewer components than shown, or may combine certain components, or may have a different arrangement of components.
As shown in fig. 12, an operating system, a network communication module, and a four-rotor unmanned aerial vehicle path tracking control method program based on nonlinear model prediction may be included in a memory 1205 as a kind of computer storage medium. The operating system is a program for managing and controlling the hardware and software resources of the four-rotor unmanned aerial vehicle path tracking control method based on nonlinear model prediction, and supports the four-rotor unmanned aerial vehicle path tracking control method based on nonlinear model prediction and the running of other software and/or programs. The network communication module is used for realizing communication among components in the memory 1205 and communication among other hardware and software in the four-rotor unmanned aerial vehicle path tracking control method system based on nonlinear model prediction.
In the four-rotor unmanned aerial vehicle path tracking control method apparatus based on nonlinear model prediction shown in fig. 12, the processor 1201 is configured to execute a four-rotor unmanned aerial vehicle path tracking control method program based on nonlinear model prediction stored in the memory 1205, to implement the steps of the four-rotor unmanned aerial vehicle path tracking control method based on nonlinear model prediction described in any one of the above.
The specific implementation mode of the four-rotor unmanned aerial vehicle path tracking control method based on nonlinear model prediction is basically the same as the above embodiments of the four-rotor unmanned aerial vehicle path tracking control method based on nonlinear model prediction, and is not repeated here.
The foregoing disclosure is illustrative of the preferred embodiments of the present invention, and is not to be construed as limiting the scope of the invention, as it is understood by those skilled in the art that all or part of the above-described embodiments may be practiced with equivalents thereof, which fall within the scope of the invention as defined by the appended claims.
Claims (7)
1. A four-rotor unmanned aerial vehicle path tracking control method based on nonlinear model prediction is characterized in that the control object is an X-type four-rotor unmanned aerial vehicle, and the method comprises the following steps:
step S1, setting a reference path, wherein the reference path is a desired state quantity set;
step S2, establishing a four-rotor unmanned aerial vehicle motion mathematical model;
s3, obtaining the rotating speeds of 4 propeller motors, and inputting the rotating speeds into the four-rotor unmanned aerial vehicle motion mathematical model to predict the state quantity at the next moment;
Step S4, carrying out feedback compensation on the predicted state quantity according to the current state quantity of the quadrotor unmanned aerial vehicle to obtain a corrected state quantity;
S5, acquiring an expected state quantity corresponding to the next moment in the reference path, substituting the expected state quantity and the correction state quantity into an objective function, and solving a control quantity of the four-rotor unmanned aerial vehicle at the next moment by using the objective function;
and S6, applying the control quantity at the next moment to the quadrotor unmanned aerial vehicle.
2. The method for controlling path tracking of the four-rotor unmanned aerial vehicle based on nonlinear model prediction according to claim 1, wherein the input of the mathematical model of the four-rotor unmanned aerial vehicle motion is the rotational speeds [ omega 1,ω2,ω3,ω4 ] of 4 propeller motors, the output is a predicted state quantity X P,X, y and z are three directional speeds respectively,Θ, ψ are respectively expressed as pitch angle, yaw angle, roll angle; in the four-rotor unmanned aerial vehicle motion mathematical model:
Under the machine body coordinate system, the four-rotor unmanned aerial vehicle generates a rotation moment [ M x,My,Mz ] around the machine body three axes through high-speed rotation of four propellers, and the rotation moment is expressed as:
In the operation process of the unmanned aerial vehicle, the total pulling force along the vertical direction is as follows:
Wherein, L a＝Lb = L is the length of the horn, α is the thrust coefficient of the unmanned aerial vehicle, and β is the rotor anti-torque coefficient;
The nonlinear dynamics model of the four-rotor unmanned aerial vehicle is as follows:
Wherein m is the mass of the four-rotor unmanned aerial vehicle; g is gravity acceleration; i x,Iy,Iz is the triaxial moment of inertia of the quadrotor unmanned aerial vehicle respectively; representing the positional acceleration of the quadrotor unmanned, Representing the angular acceleration of the quadrotor unmanned aerial vehicle; by aligningAndX P is obtained by twice integration.
3. The method for controlling path tracking of a four-rotor unmanned aerial vehicle based on nonlinear model prediction according to claim 2, wherein the step S4 comprises:
step S41, obtaining a predicted state quantity X p (k+1) obtained by predicting the k+1 time in the step S3;
Step S42, calculating an error e (k) according to the current actual state quantity Y (k) of the quadrotor unmanned aerial vehicle:
e(k)＝Y(k)-Xp(k+1)
Y(k)＝X(k)
in step S43, the correction state quantity X b (k+1) at time k+1 is calculated:
Xb(k+1)＝Xp(k+1)+Kb·e(k)
where K b is the feedback coefficient.
4. The method for controlling path tracking of a four-rotor unmanned aerial vehicle based on nonlinear model prediction according to claim 3, wherein the step S5 comprises:
step S51, obtaining an expected state quantity X r (k+1) corresponding to the k+1 time in the reference path;
Step S52, substituting the desired state quantity and the correction state quantity into an objective function J, and solving an optimal control sequence { U (k+i) } using the objective function: wherein the discrete four rotor nonlinear system satisfies:
The objective function J is expressed as:
Wherein Q and R are respectively a state weight matrix and an input weight matrix; u (k+i) is the input of the system at time k+i;
wherein, the constraint condition of the solving process comprises:
Wherein U min is the minimum input matrix, and U max is the maximum input matrix; x min is the minimum state matrix of the four-rotor unmanned aerial vehicle, and X max is the maximum state matrix of the four-rotor unmanned aerial vehicle;
and step S53, taking the first element U (k+1) in the optimal control sequence { U (k+i) } as the control quantity of the four-rotor unmanned aerial vehicle at the next moment.
5. The method for controlling path tracking of a quadrotor unmanned aerial vehicle based on nonlinear model prediction according to claim 4, wherein the control amount U of the quadrotor unmanned aerial vehicle is represented as:
6. A four-rotor unmanned aerial vehicle path tracking control method and equipment based on nonlinear model prediction are characterized by comprising the following steps: the system comprises a memory, a processor and a program stored on the memory, wherein the program is used for realizing a four-rotor unmanned aerial vehicle path tracking control method based on nonlinear model prediction;
The processor is configured to execute a program for implementing a method for controlling path tracking of a quadrotor unmanned aerial vehicle based on nonlinear model prediction, so as to implement the steps of the method for controlling path tracking of a quadrotor unmanned aerial vehicle based on nonlinear model prediction as set forth in any one of claims 1 to 5.
7. A readable storage medium, wherein a program for implementing a method for controlling path tracking of a four-rotor unmanned aerial vehicle based on nonlinear model prediction is stored on the readable storage medium, and the program for implementing the method for controlling path tracking of a four-rotor unmanned aerial vehicle based on nonlinear model prediction is executed by a processor to implement the steps of the method for controlling path tracking of a four-rotor unmanned aerial vehicle based on nonlinear model prediction as set forth in any one of claims 1 to 5.
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