Resource-limited quadrotor unmanned aerial vehicle pre-defined time trajectory tracking control method

Abstract

This invention proposes a predefined time trajectory tracking control method for quadrotor UAVs with limited communication resources. The steps are as follows: First, establish a position-attitude dual-loop dynamic model of the quadrotor UAV, expanding external disturbances into new system states. Second, design a PTESO to estimate the total disturbance within a specified time. Then, design a dual-loop predefined time sliding mode controller to perform feedforward compensation using the disturbance estimate, ensuring that the position and attitude tracking errors converge within the predefined time. Next, introduce an event-triggered mechanism in the position loop, updating the control signal only when the error exceeds a threshold, avoiding Zeno behavior. Finally, convert the virtual control quantity of the position loop into the desired attitude and thrust to drive the UAV to complete trajectory tracking. This method combines high precision and strong disturbance rejection, reduces communication and computing resource consumption, and is suitable for communication-constrained and complex, highly interference-prone scenarios.

Description

Technical Field

[0001] This invention relates to the technical field of unmanned aerial vehicle (UAV) control, and in particular to a trajectory tracking control method for a quadcopter UAV under conditions of limited communication resources, external disturbances, and model uncertainties. Background Technology

[0002] Quadrotor drones, with their advantages of simple structure and high flexibility, have been widely used in military reconnaissance, aerial photography, and power line inspection. Trajectory tracking control is a core prerequisite for drones to perform various tasks. However, quadrotor drones possess nonlinear, underactuated, and strongly coupled characteristics, making them susceptible to uncertainties such as external disturbances and parameter perturbations during flight. Furthermore, limited by communication bandwidth, effectively reducing communication load while ensuring high-performance control has become a critical issue that urgently needs to be addressed in engineering applications.

[0003] Traditional PID control experiences a significant drop in accuracy when dealing with strong nonlinearity and complex disturbances. Sliding Mode Control (SMC) offers strong robustness but only guarantees asymptotic convergence and suffers from chattering. Finite-time control achieves finite-time convergence, but its upper bound on the convergence time depends on the system's initial conditions. Fixed-time control decouples the initial state, but the upper bound on the convergence time is implicitly determined by the controller parameters, making it difficult to set flexibly. Therefore, a control method that allows direct specification of the convergence time is urgently needed. Predefined time control allows designers to directly set the convergence time according to task requirements, independent of the system's initial state. Existing research mostly employs adaptive laws or neural networks for passive disturbance compensation, failing to address the real-time accurate estimation problem of complex, unknown dynamic disturbances. Extended State Observers (ESOs) can estimate disturbances in real time, but traditional ESOs rely on high gain, easily leading to peak phenomena, and their convergence time is difficult to pre-set. In scenarios with limited communication resources, event-triggered control can effectively reduce control frequency and communication load. However, most existing results are based on asymptotic or finite-time control frameworks, and their integration with predefined time control theory is still insufficient. For quadcopter UAV systems, how to achieve integrated collaborative design of predefined time control, predefined time-extended state observers, and event triggering mechanisms remains a subject of limited technical reporting. Summary of the Invention

[0004] To address the technical problems of existing quadrotor UAV trajectory tracking control methods, such as the inability to explicitly set the convergence time, low disturbance estimation accuracy and inability to predefine the time, weak anti-disturbance capability, and high communication resource consumption, this invention proposes a predefined time trajectory tracking control method for quadrotor UAVs with limited communication resources. This method achieves accurate estimation of the total disturbance predefined time and convergence of the tracking error predefined time, while significantly reducing the control update frequency and ensuring stable and high-precision flight in communication-constrained scenarios.

[0005] To achieve the above objectives, the technical solution of the present invention is implemented as follows:

[0006] A predefined time trajectory tracking control method for a quadrotor UAV with limited communication resources, comprising the following steps:

[0007] S1. Treat the quadcopter UAV as a symmetrical rigid body, establish position dynamics and attitude dynamics models based on the Newton-Euler equations, and equate external wind disturbance, unmodeled dynamics, and parameter perturbations to the total disturbance, and expand the total disturbance to the new state of the system; transform the trajectory tracking problem into the stabilization problem of position tracking error and attitude tracking error.

[0008] S2. Based on the predefined time stability theory, a PrescribedTime Extended State Observer (PTESO) is designed to estimate the total disturbance of the position loop and attitude loop of the quadcopter UAV in real time. By combining piecewise switching gain with time-varying gain, the observation error is made to converge to the zero neighborhood within the predefined time and remain in the zero neighborhood after the predefined time, providing accurate disturbance feedforward compensation for the controller.

[0009] S3. Based on the predefined time sliding surface, a dual-loop predefined time sliding controller is designed, including a position loop predefined time sliding controller and an attitude loop predefined time sliding controller. The disturbance estimate obtained by PTESO is introduced into the control law for feedforward compensation to ensure that the UAV position tracking error and attitude tracking error converge to the zero neighborhood within a predefined time.

[0010] S4. Introduce an event triggering mechanism based on error threshold in the position loop of the quadcopter UAV, construct rules for determining event triggering conditions and triggering times, update the control signal only when the triggering conditions are met, keep the control quantity constant within the triggering interval, reduce the control signal update frequency, and strictly eliminate Zeno behavior through theoretical derivation, thereby significantly reducing the control update frequency and communication resource consumption.

[0011] S5. The position loop virtual control quantity output by the event triggering mechanism is converted into the desired roll angle, desired pitch angle and desired total thrust through the attitude calculation module, and then input to the attitude loop controller to drive the UAV to complete the predefined time trajectory tracking.

[0012] Preferably, the position dynamics model is:

[0013] ;

[0014] The attitude dynamics model is as follows:

[0015] ;

[0016] in, , , These represent the position coordinates of the quadcopter UAV in the inertial coordinate system; , , These are respectively represented as quadcopter UAVs in inertial coordinate system. , , Linear velocity in three axes; , , These are respectively represented as quadcopter UAVs in inertial coordinate system. , , Linear acceleration in three axes; These are respectively expressed as the roll angle, pitch angle, and yaw angle of the UAV; , , These represent the three-axis angular velocities corresponding to the roll angle, pitch angle, and yaw angle of the UAV, respectively. , , These represent the three-axis angular accelerations corresponding to the roll angle, pitch angle, and yaw angle of the UAV, respectively. For the quality of the drone; It is the acceleration due to gravity; This is the drag coefficient; , , , , , External disturbances include unmodeled dynamics, perturbations, and wind disturbances. The total thrust generated by the four rotors; , , Respectively around an inertial coordinate system Control torque of the shaft; , , These are the UAVs orbiting in an inertial coordinate system. Moment of inertia of the shaft.

[0017] Preferably, the predefined time-extended state observer (PTESO) takes the form of:

[0018] ;

[0019] Among them, the switching function Time-varying parameters ; Predefined time for perturbation observation; , , These are the estimated values ​​of the system's position, velocity, and acceleration, respectively. , , These represent the state estimates in the observer. , , Each of their first derivatives with respect to time; For observation error, Indicates the system status position; , , , , , , , , All of these are observer design parameters; It is a nonlinear operator. , It is a symbolic function; To control the input amount; This represents the known nominal dynamics term of the quadcopter system excluding control inputs and total disturbances; Represents the system state variables of the controlled system; Indicates system state The corresponding first derivative.

[0020] Preferably, the predefined time-extended state observer (PTESO) is divided into a position loop observer and an attitude loop observer:

[0021] The position loop observer is:

[0022] ;

[0023] in, Corresponding to position loops in inertial coordinate systems axis, axis, The axis has three independent control channels, namely Pick represent To the position channel observer, Pick represent To the position channel observer, Pick represent Towards the location channel observer; For the corresponding Estimated lumped disturbance of the channel location loop; For position loop control input;

[0024] The attitude loop observer is:

[0025] ;

[0026] in These correspond to three independent control channels in the body coordinate system: roll angle, pitch angle, and yaw angle. Pick Represents the roll channel observer. Pick Represents the pitch channel observer. Pick Represents a yaw channel observer; For the corresponding The inherent coupling nonlinearity of the model in channel attitude dynamics; For the corresponding The lumped perturbation estimate of the channel attitude loop; This is the torque input for the attitude loop control.

[0027] Preferably, the predefined time sliding surface is:

[0028] ;

[0029] in, ; This is the error between the actual state quantity and the expected state quantity, i.e., the position or attitude tracking error; , To predefine an upper bound on the convergence time for tracking errors, It is a symbolic function.

[0030] Preferably, the position loop predefined time sliding mode controller is:

[0031] ;

[0032] The attitude loop predefined time sliding mode controller is:

[0033] ;

[0034] in, ; For the desired trajectory; This is the estimated value of the disturbance; For predefined time; For robustness gain; These are design parameters.

[0035] Preferably, the triggering condition for the event triggering mechanism in step S4 is:

[0036] ;

[0037] The trigger time determination rule is as follows:

[0038] ;

[0039] in, To track the change in error, express Real-time trajectory tracking error of the time-position loop; Indicates the first Next trigger time Position loop tracking error of sampling latch; Relative threshold parameter; This is the offset parameter.

[0040] Preferably, the event triggering mechanism has a strictly positive minimum triggering interval. ,satisfy It can completely eliminate Zeno behavior and ensure stable system operation.

[0041] Preferably, the attitude calculation formula of the attitude calculation module in step S5 is:

[0042] ;

[0043] in, The desired attitude angle; The expected total thrust; This is a virtual control variable for the position loop.

[0044] A control system for a quadrotor UAV with limited communication resources, employing a predefined time trajectory tracking control method, includes:

[0045] Model building module: used to build position dynamics and attitude dynamics models of quadcopter UAVs;

[0046] Disturbance observation module: Employs a predefined time-dilated state observer (PTESO) to estimate the total disturbance of the position and attitude loops in real time;

[0047] Trajectory tracking control module: Employs a dual-loop predefined time sliding mode controller to achieve predefined time convergence tracking of position and attitude;

[0048] Event triggering module: Employs an event triggering mechanism to control the update frequency of the position loop control signal;

[0049] Attitude calculation module: The attitude calculation method is used to convert the virtual control variables of the position loop into the desired attitude angle and the desired total thrust.

[0050] The beneficial effects of this invention are:

[0051] 1) Predefined time disturbance estimation: The designed predefined time extended state observer can accurately estimate the total system disturbance within an explicitly set predefined time. The observation error quickly converges to the zero neighborhood, providing reliable feedforward compensation for the controller and significantly improving the system's disturbance rejection capability.

[0052] 2) Predefined time tracking convergence: The dual-loop predefined time sliding mode controller allows for explicit setting of the convergence time, and its tracking accuracy and response speed are superior to traditional PID, sliding mode control, and finite time / fixed time control.

[0053] 3) Efficient utilization of communication resources: The location loop event triggering mechanism adopts an on-demand update strategy, which can reduce the number of control updates by about 87%, significantly reduce communication bandwidth occupation and computational overhead, and is suitable for communication-constrained scenarios.

[0054] 4) Strictly Zeno-free behavior: The event triggering mechanism is theoretically proven to have a minimum triggering interval, completely eliminating Zeno behavior, ensuring stable system operation, and having strong engineering feasibility.

[0055] 5) Strong robustness: It has a strong ability to suppress uncertainties such as external wind disturbance, parameter perturbation, and unmodeled dynamics, and is suitable for UAV trajectory tracking tasks in complex and harsh environments. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 A schematic diagram defining the coordinate system for a quadcopter UAV.

[0058] Figure 2 This is a block diagram of the control system structure for a quadcopter unmanned aerial vehicle (UAV).

[0059] Figure 3 Convergence curves for different predefined time position errors.

[0060] Figure 4 The curves represent the X, Y, and Z axis trajectory tracking curves under undisturbed conditions.

[0061] Figure 5 Roll angle under undisturbed conditions Pitch angle Yaw angle Tracking curve.

[0062] Figure 6 This is the 3D trajectory tracking curve under undisturbed conditions.

[0063] Figure 7 This is the curve for estimating the position loop perturbation of a predefined time observer.

[0064] Figure 8 This is the predefined time observer attitude loop perturbation estimation curve.

[0065] Figure 9 The curves for tracking the X, Y, and Z axes under disturbance conditions.

[0066] Figure 10 Roll angle under disturbance conditions Pitch angle Yaw angle Tracking curve.

[0067] Figure 11 This is a curve showing the cumulative number of times the event was triggered.

[0068] Figure 12 This is a curve showing the event trigger interval. Detailed Implementation

[0069] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0070] Example 1, such as Figure 2 As shown, a predefined time trajectory tracking control method for a quadcopter UAV with limited communication resources includes the following steps:

[0071] S1. System Modeling and Problem Description:

[0072] S1.1 Model Assumptions

[0073] Assumption 1: The drone is a symmetrical rigid body with constant mass and moment of inertia, and its geometric center coincides with its center of mass.

[0074] Assumption 2: The external disturbance is continuous and bounded, and the derivative of the disturbance satisfies the bounded condition.

[0075] S1.2 Unmanned Aerial Vehicle System Modeling

[0076] Establish an inertial coordinate system and a body coordinate system, such as Figure 1 As shown. The dynamics of a quadcopter UAV and the Newton-Euler equations can be decomposed into two coupled parts: position dynamics and attitude dynamics. The position dynamics equations satisfy:

[0077] (1);

[0078] The attitude dynamics model satisfies:

[0079] (2);

[0080] in, , , These represent the position coordinates of the quadcopter UAV in the inertial coordinate system; , , These are respectively represented as quadcopter UAVs in inertial coordinate system. , , Linear velocity in three axes; , , These are respectively represented as quadcopter UAVs in inertial coordinate system. , , Linear acceleration in three axes; These are respectively expressed as the roll angle, pitch angle, and yaw angle of the UAV; , , These represent the three-axis angular velocities corresponding to the roll angle, pitch angle, and yaw angle of the UAV, respectively. , , These represent the three-axis angular accelerations corresponding to the roll angle, pitch angle, and yaw angle of the UAV, respectively. For the quality of the drone; It is the acceleration due to gravity; This is the drag coefficient; , , , , , External disturbances include unmodeled dynamics, perturbations, and wind disturbances. The total thrust generated by the four rotors; , , These are the coordinate systems around the body. Control torque of the shaft; , , respectively drones orbit Moment of inertia of the shaft.

[0081] S1.3, Virtual control quantity

[0082] To facilitate controller design, three virtual control variables are selected from the position dynamics equation (1). The specific definition is as follows:

[0083] (3)

[0084] S1.4 Attitude Calculation

[0085] Based on (4), the desired attitude angle can be derived. , and expected lift The expression is shown in the following formula:

[0086] (4)

[0087] S1.5 The control problem is transformed into an error stabilization problem.

[0088] The position loop tracking error of a quadcopter UAV is defined as:

[0089] (5)

[0090] in, , , They are respectively , , The desired trajectory of the axis.

[0091] By combining equations (1), (3), and (5), the dynamic equation for the position tracking error of the UAV is derived:

[0092] (6)

[0093] To simplify the writing, the attitude dynamics equation (2) is rearranged into a compact matrix form:

[0094] (7)

[0095] in for The antisymmetric matrix operator, with each matrix defined as follows:

[0096] (8)

[0097] Define attitude angle tracking error:

[0098] (9)

[0099] in, Let be the desired attitude angle vector of the UAV. Accordingly, according to equation (7), the attitude angle tracking error satisfies:

[0100] (10)

[0101] in, and Desired attitude angle The first and second derivatives, For the lumped disturbance of the attitude loop, the following conditions must be met:

[0102] (11)

[0103] For the attitude loop tracking error (Equation 10), the following variables are defined:

[0104] (12)

[0105] Equation (10) can be rewritten as:

[0106] (13)

[0107] Through the above coordinate transformation and error definition, the trajectory tracking problem of a quadcopter UAV can be transformed into a position tracking error system (6). , , In the attitude tracking error system (13) , , The issue of composure.

[0108] S2. Design of a predefined time-extended state observer

[0109] Based on the quadcopter UAV models (1) and (2), its dynamic model can be expressed as:

[0110] (14)

[0111] in, The controlled quantity; For control input; For the nonlinear terms of the system; The total disturbance includes external disturbances, model uncertainties, coupling terms, etc. For ease of observer design, the total disturbance is... Expanding into a new state of the system And define the extended state vector. ,in Then system (14) can be written in the following extended state-space form:

[0112] (15)

[0113] in, The perturbation derivative satisfies the boundedness condition of Assumption 2.

[0114] S2.2 Design of Predefined Time-Expanded State Observer

[0115] For system (15), the following predefined time-extended state observer is designed:

[0116] (16)

[0117] in, , , These are the estimated values ​​of the system's position, velocity, and acceleration, respectively. , , These represent the state estimates in the observer. , , Each of their first derivatives with respect to time; This represents the observation error. , , and The parameters to be designed. Define time-varying parameters. ,in and . , , and The parameters to be designed, and satisfy... , , , Define nonlinear operators , , It is a symbolic function. Switching function for a predefined time-extended state observer , For a predefined time, System time; This represents the known nominal dynamics of the quadrotor system without control input and total disturbance (model-inherent nonlinear dynamics). Represents the system state variables of the controlled system (quadrotor position / attitude generalized coordinates); Indicates system state The corresponding first derivative (generalized velocity).

[0118] S2.3 Design of Predefined Time-Location Loop Expansion State Observer

[0119] (17);

[0120] in, Corresponding to position loops in inertial coordinate systems axis, axis, The axis has three independent control channels, namely Pick represent To the position channel observer, Pick represent To the position channel observer, Pick represent Towards the location channel observer; For the corresponding Estimated lumped disturbance of the channel location loop; For position loop control input;

[0121] S2.4 Design of Predefined Time and Attitude Loop Extended State Observer

[0122] (18);

[0123] in These correspond to three independent control channels in the body coordinate system: roll angle, pitch angle, and yaw angle. Pick Represents the roll channel observer. Pick Represents the pitch channel observer. Pick Represents a yaw channel observer; For the corresponding The inherent coupling nonlinearity of the model in channel attitude dynamics; For the corresponding The lumped perturbation estimate of the channel attitude loop; This is the torque input for the attitude loop control.

[0124] S3, Predefined Time Sliding Mode Controller Design

[0125] S3.1 Predefined Time Sliding Surface Design

[0126] (19);

[0127] in, ; This is the error between the actual state quantity and the expected state quantity, i.e., the position or attitude tracking error; , A predefined upper bound for the convergence time is provided for tracking error. For symbolic functions, the definition is as follows:

[0128] (20)

[0129] S3.2, The position loop predefined time sliding mode controller is:

[0130] (twenty one);

[0131] S3.3, The attitude loop predefined time sliding mode controller is:

[0132] (twenty two);

[0133] in, ; For the desired trajectory; This is the estimated value of the disturbance; The convergence time is predefined; For robustness gain; These are design parameters.

[0134] S4. Event Triggering Mechanism Design

[0135] S4.1 Triggering conditions for event triggering mechanism

[0136] (twenty three);

[0137] S4.2, The triggering time determination rule is as follows:

[0138] (twenty four);

[0139] in, To track the change in error, express Real-time trajectory tracking error of the time-position loop; Indicates the first Next trigger time Position loop tracking error of sampling latch; Relative threshold parameter; This is an offset parameter used to avoid excessive triggering frequency when the system approaches the equilibrium point.

[0140] S4.3 Control and Maintenance Strategy

[0141] (25)

[0142] Triggering conditions are defined based on position tracking errors, and threshold parameters are set; the control input remains constant within the trigger interval; theoretical proof demonstrates that the event triggering mechanism has a strictly positive minimum trigger interval. ,satisfy It can completely eliminate Zeno behavior and ensure stable system operation.

[0143] S5. Attitude Calculation and Closed-Loop Control

[0144] The virtual control quantity of the position loop output from the event-triggered mechanism is input into the attitude calculation module. This quantity is then transformed into the desired roll angle, desired pitch angle, and desired total thrust using attitude calculation formulas, and input as the desired command to the attitude loop controller. The attitude loop controller, combined with the disturbance estimate from the predefined time-spreading state observer, outputs a control torque to drive the quadcopter UAV's actuators, forming a position-attitude dual-loop closed-loop control. Ultimately, this achieves predefined time trajectory tracking under conditions of limited communication resources.

[0145] The attitude calculation formula for the attitude calculation module is as follows:

[0146] (26);

[0147] in, The desired attitude angle; The expected total thrust; This is a virtual control variable for the position loop.

[0148] Example 2: A predefined time trajectory tracking control system for a quadcopter UAV with limited communication resources, used to implement a predefined time trajectory tracking control method for a quadcopter UAV with limited communication resources; including:

[0149] Model building module: used to build position dynamics and attitude dynamics models of quadcopter UAVs;

[0150] Disturbance observation module: Employs a predefined time-dilated state observer (PTESO) to estimate the total disturbance of the position and attitude loops in real time;

[0151] Trajectory tracking control module: Employs a dual-loop predefined time sliding mode controller to achieve predefined time convergence tracking of position and attitude;

[0152] Event triggering module: Employs an event triggering mechanism to control the update frequency of the position loop control signal;

[0153] Attitude calculation module: The attitude calculation method is used to convert the virtual control variables of the position loop into the desired attitude angle and the desired total thrust.

[0154] The modules work together to achieve predefined time trajectory tracking and communication resource optimization.

[0155] Simulation examples and results

[0156] To verify the effectiveness of the method of this invention, a simulation model was built on the MATLAB / Simulink platform, and UAV parameters, predefined time, event triggering parameters, and disturbance conditions were set. The simulation parameter settings are as follows:

[0157]

[0158] The desired trajectory is set as follows:

[0159]

[0160] The disturbance settings are as follows:

[0161]

[0162] The simulation duration was set to 15 seconds. The simulation results show that the predefined time trajectory tracking control method for quadrotor UAVs with limited communication resources proposed in this invention can achieve high-precision, fast-response, and robust trajectory tracking effects.

[0163] Figure 3 shows the position error convergence curves under different predefined times. The results show that the position tracking error can be quickly converged to the steady-state range within the predefined time specified by the designer. The convergence speed and accuracy can be flexibly adjusted, which verifies the effectiveness of the predefined time control.

[0164] Figure 4 shows the X, Y, and Z axis trajectory tracking curves under undisturbed conditions, and Figure 5 shows the roll, pitch, and yaw angle tracking curves under undisturbed conditions. The results show that under undisturbed conditions, the tracking curves of each channel are smooth, without overshoot or oscillation, and the actual trajectory can quickly and accurately follow the desired trajectory, with a stable and coordinated attitude response. Figure 6 shows the three-dimensional trajectory tracking curve under undisturbed conditions; the actual flight trajectory of the UAV highly overlaps with the desired trajectory, indicating that the present invention possesses excellent trajectory tracking performance under ideal conditions.

[0165] Figure 7 shows the predefined time observer position loop perturbation estimation curve, and Figure 8 shows the predefined time observer attitude loop perturbation estimation curve. Figure 8 It is evident that the predefined time-spread state observer can quickly and accurately track and estimate the total disturbance of each channel within a predefined time period, with high estimation accuracy and fast response speed, providing reliable disturbance compensation for the controller.

[0166] Figure 9 shows the X, Y, and Z axis trajectory tracking curves under disturbance conditions, and Figure 10 shows the roll, pitch, and yaw angle tracking curves under disturbance conditions. Under the influence of external wind disturbances and model uncertainties, the method of this invention still maintains high-precision tracking and stable attitude response, indicating that the system has strong robustness and excellent anti-interference ability.

[0167] Figure 11 shows the cumulative number of event triggers. Within a 15-second simulation cycle, the number of control updates is significantly reduced, and the consumption of communication and computing resources is greatly reduced. Figure 12 shows the event trigger interval curve. The trigger interval always remains greater than zero, and there is no infinitely high-frequency triggering phenomenon. Zeno behavior is strictly excluded, ensuring system stability, reliability, and engineering feasibility.

[0168] The predefined time trajectory tracking control method for quadrotor UAVs with limited communication resources proposed in this invention can still achieve high-precision, predefined time convergence trajectory tracking under conditions of external disturbances, model uncertainty and limited communication resources, while significantly reducing communication resource consumption, and has good engineering application value.

[0169] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A predefined time trajectory tracking and control method for a quadrotor unmanned aerial vehicle with limited communication resources, characterized in that, The steps are as follows: S1. Treat the quadcopter UAV as a symmetrical rigid body, establish position dynamics and attitude dynamics models based on the Newton-Euler equations, and equate external wind disturbance, unmodeled dynamics, and parameter perturbations to the total disturbance, and expand the total disturbance to the new state of the system. S2. Based on the predefined time stability theory, a predefined time extended state observer (PTESO) is designed to estimate the total disturbance of the position loop and attitude loop of a quadcopter UAV in real time. By combining piecewise switching gain with time-varying gain, the observation error is made to converge to the zero neighborhood within the predefined time and remain in the zero neighborhood after the predefined time. S3. Based on the predefined time sliding surface, a dual-loop predefined time sliding controller is designed, including a position loop predefined time sliding controller and an attitude loop predefined time sliding controller. The disturbance estimate obtained by PTESO is introduced into the control law for feedforward compensation to ensure that the UAV position tracking error and attitude tracking error converge to the zero neighborhood within a predefined time. S4. Introduce an event triggering mechanism based on error threshold in the position loop of a quadcopter UAV, construct rules for determining event triggering conditions and triggering times, update the control signal only when the triggering conditions are met, keep the control quantity constant within the triggering interval, and strictly eliminate Zeno behavior through theoretical derivation. S5. The position loop virtual control quantity output by the event triggering mechanism is converted into the desired roll angle, desired pitch angle and desired total thrust through the attitude calculation module, and then input to the attitude loop controller to drive the UAV to complete the predefined time trajectory tracking.

2. The predefined time trajectory tracking and control method for a quadrotor UAV with limited communication resources according to claim 1, characterized in that, The position dynamics model is as follows: ； The attitude dynamics model is as follows: ； in, , , These represent the position coordinates of the quadcopter UAV in the inertial coordinate system; , , These are respectively represented as quadcopter UAVs in inertial coordinate system. , , Linear velocity in three axes; , , These are respectively represented as quadcopter UAVs in inertial coordinate system. , , Linear acceleration in three axes; These are respectively expressed as the roll angle, pitch angle, and yaw angle of the UAV; , , These represent the three-axis angular velocities corresponding to the roll angle, pitch angle, and yaw angle of the UAV, respectively. , , These represent the three-axis angular accelerations corresponding to the roll angle, pitch angle, and yaw angle of the UAV, respectively. For the quality of the drone; It is the acceleration due to gravity; This is the drag coefficient; , , , , , External disturbances include unmodeled dynamics, perturbations, and wind disturbances. The total thrust generated by the four rotors; , , Respectively around an inertial coordinate system Control torque of the shaft; , , These are the UAVs orbiting in an inertial coordinate system. Moment of inertia of the shaft.

3. The predefined time trajectory tracking and control method for a quadrotor UAV with limited communication resources according to claim 2, characterized in that, The predefined time-extended state observer PTESO takes the following form: ； Among them, the switching function Time-varying parameters ; Predefined time for perturbation observation; , , These are the estimated values ​​of the system's position, velocity, and acceleration, respectively. , , These represent the state estimates in the observer. , , Each of their first derivatives with respect to time; For observation error, Indicates the system status position; , , , , , , , , All of these are observer design parameters; It is a nonlinear operator. , It is a symbolic function; To control the input amount; This represents the known nominal dynamics term of the quadcopter system excluding control inputs and total disturbances; Represents the system state variables of the controlled system; Represents system state variables The corresponding first derivative.

4. The predefined time trajectory tracking and control method for a quadrotor UAV with limited communication resources according to claim 3, characterized in that, The predefined time-dilated state observer (PTESO) is divided into a position loop observer and an attitude loop observer: The position loop observer is: （17）； in, Corresponding to position loops in inertial coordinate systems axis, axis, The axis has three independent control channels, namely Pick represent To the position channel observer, Pick represent To the position channel observer, Pick represent Towards the location channel observer; For the corresponding Estimated lumped disturbance of the channel location loop; For position loop control input; The attitude loop observer is: ； in These correspond to three independent control channels in the body coordinate system: roll angle, pitch angle, and yaw angle. Pick Represents the roll channel observer. Pick Represents the pitch channel observer. Pick Represents a yaw channel observer; For the corresponding The inherent coupling nonlinearity of the model in channel attitude dynamics; For the corresponding The lumped perturbation estimate of the channel attitude loop; This is the torque input for the attitude loop control.

5. The predefined time trajectory tracking and control method for a quadrotor UAV with limited communication resources according to claim 4, characterized in that, The predefined time sliding surface is: ； in, ; The error between the actual state quantity and the expected state quantity; , To predefine an upper bound on the convergence time for tracking errors, It is a symbolic function.

6. The predefined time trajectory tracking and control method for a quadrotor UAV with limited communication resources according to claim 5, characterized in that, The predefined time sliding mode controller for the position loop is: ； The attitude loop predefined time sliding mode controller is: ； in, ; For the desired trajectory; This is the estimated value of the disturbance; For predefined time; For robustness gain; These are design parameters.

7. The predefined time trajectory tracking and control method for a quadrotor UAV with limited communication resources according to claim 1, characterized in that, The triggering condition for the event triggering mechanism in step S4 is: ； The trigger time determination rule is as follows: ； in, To track the change in error, express Real-time trajectory tracking error of the time-position loop; Indicates the first Next trigger time Position loop tracking error of sampling latch; Relative threshold parameter; This is the offset parameter.

8. The predefined time trajectory tracking control method for a quadrotor UAV with limited communication resources according to claim 7, characterized in that, The event triggering mechanism has a strictly positive minimum triggering interval. ,satisfy This completely eliminates Zeno's behavior.

9. The predefined time trajectory tracking and control method for a quadrotor UAV with limited communication resources according to claim 2 or 6, characterized in that, The attitude calculation formula in step S5 is as follows: ； in, The desired attitude angle; The expected total thrust; This is a virtual control variable for the position loop.

10. A control system corresponding to the predefined time trajectory tracking control method for a quadrotor unmanned aerial vehicle with limited communication resources as described in any one of claims 1-9, characterized in that, include: Model building module: used to build position dynamics and attitude dynamics models of quadcopter UAVs; Disturbance observation module: Employs a predefined time-dilated state observer (PTESO) to estimate the total disturbance of the position and attitude loops in real time; Trajectory tracking control module: Employs a dual-loop predefined time sliding mode controller to achieve predefined time convergence tracking of position and attitude; Event triggering module: Employs an event triggering mechanism to control the update frequency of the position loop control signal; Attitude calculation module: The attitude calculation method is used to convert the virtual control variables of the position loop into the desired attitude angle and the desired total thrust.

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