Four-rotor unmanned aerial vehicle anti-interference tracking control method based on double-dynamic event triggering mechanism

Abstract

The invention discloses a four-rotor unmanned aerial vehicle anti-interference tracking control method based on a double-dynamic event triggering mechanism in the technical field of unmanned aerial vehicle control, and the method comprises the following steps: 1), building a kinematic equation of a four-rotor unmanned aerial vehicle, and carrying out the modeling of an unmanned aerial vehicle model; 2) combining state information with a proportional-integral structure, carrying out augmentation and expansion on the system, and designing a composite dynamic tracking controller; 3) based on a disturbance observer control strategy, designing a dual-dynamic event triggering mechanism of a system feedback state and disturbance estimation, and realizing compensation of the unmanned aerial vehicle system on disturbance and reducing data transmission between the unmanned aerial vehicle system and a ground station; and 4) solving the linear matrix inequality equation through a Lyapunov stability analysis method and a convex optimization algorithm to obtain the controller gain, and reducing the data transmission and bandwidth waste of the input end of the unmanned aerial vehicle on the basis of solving the design of the anti-interference observer, and ensuring that the controlled system has good synchronism and system stability.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle control, and in particular to an anti-interference tracking control method for a quad-rotor unmanned aerial vehicle. Background Art

[0002] As a flexible and versatile flight platform, quadrotor drones have been widely used in military, agriculture, rescue and environmental monitoring fields in recent years. However, due to the unique structure of quadrotor drones, they often face complex interference from both inside and outside during flight. These interferences include changes in the external environment, such as uncertain factors such as wind speed and airflow, as well as nonlinear and coupled dynamic terms of the internal system, such as fluctuations in motor output and harmonic interference caused by vibration. These factors will seriously affect the flight stability and control accuracy of drones, making their application in long-term, high-precision tasks challenging. Therefore, it is particularly important to study how to improve the anti-interference ability of quadrotor drones. Disturbance observer-based control (DOBC) is a typical anti-interference control method with the advantages of fast response and easy implementation. The basic idea of ​​the DOBC method is to design a disturbance observer to estimate the external disturbance, and further suppress the disturbance by combining the feedforward compensator with the observer output information. This method is easy to combine with other feedback control methods to form a composite control method. However, under the framework of DOBC theory, the introduction of disturbance estimation will inevitably bring more data redundancy and transmission pressure to the control port. Therefore, it is urgent to study anti-interference algorithms with low data transmission and low computational cost.

[0003] With the rapid development of modern communication technology, the demand for data and information transmission is increasing, but the computing resources of the system are limited. When a quadcopter is flying at high altitude, a communication network is required to realize data transmission between the ground station and the drone. Continuous communication will inevitably waste limited network resources. Usually, the transmission of data signals adopts a time-triggered mechanism, that is, the signal is transmitted at a fixed period. This communication method is bound to cause a waste of communication resources. In order to reduce resource waste, the event trigger mechanism has received widespread attention. The event trigger mechanism controls the transmission of data according to reasonable events, and only updates the data when the event does not meet the threshold condition. In order to further reduce the communication burden in the control system, the dynamic event trigger mechanism has received widespread attention and discussion. By adding internal dynamic variables to the static event trigger mechanism, a dynamic event trigger mechanism is introduced. The dynamic event trigger mechanism can ensure that the time interval between two execution moments is longer than the time interval using the static event trigger mechanism, and maintain good control performance of the system. However, most existing works are based on a single event trigger. When multiple trigger events exist at the same time, how to design a multi-trigger control method to ensure the synchronization and stability of the controlled system deserves further attention.

[0004] Therefore, under the theoretical framework of DOBC, the present invention designs dynamic event trigger mechanisms corresponding to the system status and interference estimation of the quad-rotor drone. According to the principle of "first come, same trigger", the two dynamic event trigger conditions compete with each other. Only when one of the trigger conditions is met, the controller will be updated, that is, when the trigger condition is met, the two events are triggered at the same time. Based on this dual dynamic event trigger mechanism, a quad-rotor drone anti-interference controller is designed. In the following detailed introduction of the invention, the control method also gives a corresponding solution. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides an anti-interference tracking control method for a quad-rotor UAV based on a dual dynamic event trigger mechanism, which greatly reduces unnecessary data transmission, effectively optimizes bandwidth utilization, reduces data redundancy and transmission pressure at the control input end, obtains good tracking effect, and improves the anti-interference ability of the system.

[0006] The object of the present invention is achieved by: a quad-rotor unmanned aerial vehicle anti-interference tracking control method based on a dual dynamic event trigger mechanism, comprising the following steps:

[0007] Step 1) establishing a kinematic equation of a quad-rotor drone and modeling the quad-rotor drone model;

[0008] Step 2) Combine the state information with the proportional-integral structure to expand the system and design a composite dynamic tracking controller;

[0009] Step 3) Based on the disturbance observer control strategy, a dual dynamic event trigger mechanism of system feedback state and disturbance estimation is designed to realize the compensation of the quadrotor UAV system for disturbance and reduce its data transmission with the ground station;

[0010] Step 4) Solve the linear matrix inequality equations to obtain the controller gains through Lyapunov stability analysis and convex optimization algorithm.

[0011] Furthermore, step 1) is specifically as follows: establishing the kinematic equation of the quad-rotor drone, modeling the drone model, and obtaining the drone nonlinear system model:

[0012] ;

[0013] In the formula, are the position coordinates of the drone in three directions in the coordinate system, where is the position coordinate of the drone along the X axis, is the position coordinate of the drone along the Y axis, is the position coordinate of the drone along the Z axis; are the Euler angles of the quadcopter, respectively, , Roll Angle , Pitch angle ; is the linear velocity vector of the UAV in three directions in the coordinate system, where is the linear velocity vector of the drone along the X axis, is the linear velocity vector of the drone along the Y axis, is the linear velocity vector of the drone along the Z axis; is the angular velocity vector of the UAV in three directions in the coordinate system, where is the angular velocity vector of the drone along the X axis, is the angular velocity vector of the drone along the Y axis, is the angular velocity vector of the drone along the Z axis; is the moment of inertia matrix, where , , They are the rotational inertia of the drone along the X-axis, Y-axis and Z-axis in the coordinate system; For the quality of quadrotor drone; is the gravity vector; and are friction coefficient and torque coefficient respectively; and For unknown interference; and are two rotation matrices, respectively:

[0014] ;

[0015] ;

[0016] and are the torque and force generated by the motor, respectively, and their relationship is as follows:

[0017]

[0018] in, is the arm length of the quadrotor drone; and are the force coefficient and moment coefficient respectively; , , , They are the rotation speeds of the four motors of the quadrotor drone;

[0019] By using the principle of small disturbance linearization, the nonlinear system model of the UAV is transformed into a linear system model. Taylor series expansion is used at the equilibrium point of the quadrotor UAV system to retain linear terms, discard high-order small terms, and write the nonlinear scalar equation into a linear state equation:

[0020]

[0021] In the formula, and is the system state matrix, Input to the system.

[0022] Furthermore, step 2) specifically includes:

[0023] Step 2-1) The linear state equation is linked with the regulated output to obtain:

[0024]

[0025] in, is the state variable of the state equation, is the system control input, is the system output, For measurable output, , , , , , is a system matrix of suitable dimension, and There are two different types of interference;

[0026] New Definition is the augmented system state variable, where is the tracking error, is the target weight vector;

[0027] Step 2-2) Combine the system state information with the proportional integral structure and substitute it into the original state space equation, expand the system state to obtain the following augmented system:

[0028] ;

[0029] in, , , , , , , .

[0030] Furthermore, step 3) specifically includes:

[0031] Step 3-1) Model the interference. The interference model is as follows:

[0032] ;

[0033] in, is the intermediate state, and is the designed dimensionality matrix;

[0034] Step 3-2) Construct a disturbance observer:

[0035]

[0036] in, and are the estimated values ​​of the interference and intermediate states, respectively, is a designed auxiliary variable, is the observer gain;

[0037] Step 3-3) Design a dynamic event triggering mechanism for the system augmentation state:

[0038]

[0039] in, is a normal number, is an additional design parameter, the system augmented state transmission error The definition is as follows:

[0040] ;

[0041] is an internal dynamic variable introduced in the dynamic event triggering mechanism, which satisfies the following equation:

[0042] ;

[0043] in, To satisfy the local Lipschitz condition function, is the parameter to be designed;

[0044] Step 3-4) Design a dynamic event triggering mechanism for interference estimation:

[0045]

[0046] in, is a normal number, is an additional design parameter, disturbing the estimation error The definition is as follows:

[0047]

[0048] It is also an internal dynamic variable introduced in the dynamic event triggering mechanism, which satisfies the following equation:

[0049]

[0050] in, To satisfy the local Lipschitz condition function, is the parameter to be designed;

[0051] Step 3-5) The dual dynamic event trigger conditions based on system status and interference estimation are as follows:

[0052]

[0053] in, , and They represent the minimum trigger time lower bounds of the system augmented state and interference estimation under the dynamic event trigger mechanism respectively;

[0054] Step 3-6) Under the dual dynamic event trigger mechanism, the composite controller is designed as follows:

[0055]

[0056] in, is the gain of the designed composite controller.

[0057] Furthermore, step 4) specifically includes: according to the controlled system and the disturbance error model, assuming , , and The following matrix inequality is satisfied:

[0058]

[0059]

[0060] in, , , , , , Solving the above linear matrix inequality can prove that the augmented system is stable under the designed composite controller; the controller gain is calculated by The disturbance observer gain is obtained by To obtain.

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] (1) Under the premise of ensuring the stability of the system, the present invention designs a composite dynamic tracking controller. By combining Lyapunov stability analysis and convex optimization algorithm, it not only ensures the stability of the system, but also greatly improves the dynamic tracking performance of the quadrotor UAV system; under different target points and complex interference conditions, the controller of the present invention can achieve high-precision trajectory tracking control.

[0063] (2) The dual dynamic event trigger mechanism of the present invention adopts the "first come, same trigger" strategy, which introduces internal dynamic variables to monitor the system status and interference estimation data in real time. Data transmission is performed only when a certain event trigger condition is met. This dynamic adjustment mechanism greatly reduces unnecessary data transmission and effectively optimizes bandwidth utilization, thereby reducing the computational complexity and energy consumption of the system. It is particularly suitable for UAV systems with high requirements for endurance.

[0064] (3) The present invention designs an interference observer. Compared with the traditional anti-interference method, the interference observer can quickly and accurately estimate the interference information and compensate for the interference information in the feedforward channel, which greatly improves the anti-interference ability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0066] Figure 1 It is a flow chart of the present invention.

[0067] Figure 2 Schematic diagram of the control system of a quad-rotor UAV under the dual dynamic event triggering mechanism of the present invention. DETAILED DESCRIPTION

[0068] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0069] like Figure 1-2 The anti-interference tracking control method of a quad-rotor UAV based on a dual dynamic event trigger mechanism includes the following steps:

[0070] Step 1) Establish the kinematic equation of the Z410 quad-rotor drone and model the quad-rotor drone model:

[0071]

[0072] In the formula, is the position coordinate; is the Euler angle (roll angle, pitch angle, yaw angle) of the quadrotor drone; is the UAV linear velocity vector; is the angular velocity vector of the drone; Moment of inertia matrix; For the quality of quadrotor drone; is the gravity vector; and are friction coefficient and torque coefficient respectively; and For unknown interference; and are two rotation matrices, respectively:

[0073]

[0074] and are the torque and force generated by the motor, respectively, and their relationship is as follows:

[0075]

[0076] in, is the arm length of the quadrotor drone; and are the force coefficient and moment coefficient respectively; , , , They are the rotation speeds of the four motors of the quadrotor drone.

[0077] The relevant parameter settings in the formula are as follows:

[0078] In addition, by using the principle of small disturbance linearization, the complex nonlinear system of the UAV is converted into a linear system, which can ensure the stability of the model and enable the model to maintain robustness when the input changes slightly. Using Taylor series expansion at the equilibrium point of the quadcopter UAV system, retaining linear terms and discarding high-order small terms, the nonlinear scalar equation can be rewritten into a linear state equation:

[0079]

[0080] In the formula, and is the system state matrix, Input to the system.

[0081] Step 2) Combine the state information with the proportional-integral structure to augment the system and design a composite dynamic tracking controller.

[0082] The state space equation of the quadrotor drone system model of the present invention and the adjusted output are linked to obtain:

[0083]

[0084] in, is the state variable of the system state equation, is the system control input, is the system output, For measurable output, , , , , , is a system matrix of suitable dimension, and There are two different types of interference.

[0085] In order to obtain good dynamic tracking performance, is the newly defined augmented system state variable. Among them, is the tracking error, is the target weight vector.

[0086] Combining the system state information with the proportional integral structure and substituting it into the original state space equation, the system state is expanded to obtain the following augmented system:

[0087]

[0088] in, , , , , , , .

[0089] Step 3) Based on the disturbance observer control strategy, a dual dynamic event triggering mechanism of system feedback state and disturbance estimation is designed to realize the compensation of the quadrotor UAV system for disturbance and reduce its data transmission with the ground station.

[0090] First, the control method of the present invention needs to model the interference, and the interference model is as follows:

[0091]

[0092] in, is the intermediate state, and is the designed dimension matrix.

[0093] The present invention constructs the following disturbance observer:

[0094]

[0095] in, and are the estimated values ​​of the interference and intermediate states, respectively, is a designed auxiliary variable, is the observer gain.

[0096] A dual dynamic event trigger mechanism is adopted, and corresponding dynamic event trigger mechanisms are designed for the augmented state and interference estimation of the UAV system. According to the principle of "first come, same trigger", the two dynamic event trigger conditions compete with each other. Only when one of the trigger conditions is met, the controller will be updated, that is, when the trigger condition is met, the two events are triggered at the same time.

[0097] The dynamic event triggering mechanism of the system augmented state is designed as follows:

[0098]

[0099] in, is a normal number, is an additional design parameter, the system augmented state transmission error The definition is as follows:

[0100]

[0101] at the same time, is an internal dynamic variable introduced in the dynamic event triggering mechanism, which satisfies the following equation:

[0102]

[0103] in, To satisfy the local Lipschitz condition function, are the parameters to be designed.

[0104] The dynamic event triggering mechanism for interference estimation is designed as follows:

[0105]

[0106] in, is a normal number, is an additional design parameter, disturbing the estimation error The definition is as follows:

[0107]

[0108] at the same time, It is also an internal dynamic variable introduced in the dynamic event triggering mechanism, which satisfies the following equation:

[0109]

[0110] in, To satisfy the local Lipschitz condition function, are the parameters to be designed.

[0111] The dual dynamic event trigger conditions based on system status and interference estimation are as follows:

[0112]

[0113] in, , and They represent the minimum trigger time lower bounds of the system augmented state and interference estimation under the dynamic event triggering mechanism respectively.

[0114] Under the dual dynamic event trigger mechanism, the composite controller is designed as follows:

[0115]

[0116] in, is the gain of the designed composite controller. It can be seen from the above formula that the composite controller under the dual dynamic event trigger mechanism only transmits the relevant state at the triggering moment.

[0117] Step 4) Solve the linear matrix inequality equations to obtain the controller gains through Lyapunov stability analysis and convex optimization algorithm.

[0118] According to the controlled system and disturbance error model, it is assumed that , , and The following matrix inequality is satisfied:

[0119]

[0120]

[0121] in, , , , , , Solving the above linear matrix inequality can prove that the augmented system is stable under the designed composite controller. At the same time, the good dynamic tracking characteristics can also be verified. The controller gain can be obtained by The disturbance observer gain can be obtained by To obtain.

[0122] The above embodiments are only used to help understand the method and core idea of ​​the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made to the present invention without departing from the principles of the present invention, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

Claims

1. A quadrotor unmanned aerial vehicle anti-interference tracking control method based on a dual dynamic event trigger mechanism, characterized in that: The following steps are involved: Step 1) establishing a kinematic equation of a quad-rotor drone and modeling the quad-rotor drone model; Step 2) Combine the state information with the proportional-integral structure to expand the system and design a composite dynamic tracking controller; Step 3) Based on the disturbance observer control strategy, a dual dynamic event trigger mechanism of system feedback state and disturbance estimation is designed to realize the compensation of the quadrotor UAV system for disturbance and reduce its data transmission with the ground station; Step 4) Solve the linear matrix inequality equations to obtain the controller gains through Lyapunov stability analysis and convex optimization algorithm.

2. The anti-interference tracking control method for a quad-rotor drone based on a dual dynamic event trigger mechanism according to claim 1 is characterized in that: Step 1) is as follows: establish the kinematic equation of the quadcopter, model the drone model, and obtain the nonlinear system model of the drone: ； In the formula, are the position coordinates of the drone in three directions in the coordinate system, where is the position coordinate of the drone along the X axis, is the position coordinate of the drone along the Y axis, is the position coordinate of the drone along the Z axis; are the Euler angles of the quadcopter, respectively, , Roll Angle , Pitch angle ; is the linear velocity vector of the UAV in three directions in the coordinate system, where is the linear velocity vector of the drone along the X axis, is the linear velocity vector of the drone along the Y axis, is the linear velocity vector of the drone along the Z axis; is the angular velocity vector of the UAV in three directions in the coordinate system, where is the angular velocity vector of the drone along the X axis, is the angular velocity vector of the drone along the Y axis, is the angular velocity vector of the drone along the Z axis; is the moment of inertia matrix, where , , They are the rotational inertia of the drone along the X-axis, Y-axis and Z-axis in the coordinate system; For the quality of quadrotor drone; is the gravity vector; and are friction coefficient and torque coefficient respectively; and For unknown interference; and are two rotation matrices, respectively: ； ； and are the torque and force generated by the motor, respectively, and their relationship is as follows: ； in, is the arm length of the quadrotor drone; and are the force coefficient and moment coefficient respectively; , , , They are the rotation speeds of the four motors of the quadrotor drone; By using the principle of small disturbance linearization, the nonlinear system model of the UAV is transformed into a linear system model. Taylor series expansion is used at the equilibrium point of the quadrotor UAV system to retain linear terms, discard high-order small terms, and write the nonlinear scalar equation into a linear state equation: ； In the formula, and is the system state matrix, Input to the system.

3. The anti-interference tracking control method for a quad-rotor drone based on a dual dynamic event trigger mechanism according to claim 2 is characterized in that: Step 2) specifically includes: Step 2-1) The linear state equation is linked with the regulated output to obtain: ； in, is the state variable of the state equation, is the system control input, is the system output, For measurable output, , , , , , is a system matrix of suitable dimension, and There are two different types of interference; New Definition is the augmented system state variable, where is the tracking error, is the target weight vector; Step 2-2) Combine the system state information with the proportional integral structure and substitute it into the original state space equation, expand the system state to obtain the following augmented system: ； in, , , , , , , .

4. The anti-interference tracking control method for a quad-rotor drone based on a dual dynamic event trigger mechanism according to claim 3 is characterized in that: Step 3) specifically includes: Step 3-1) Model the interference. The interference model is as follows: ； in, is the intermediate state, and is the designed dimensionality matrix; Step 3-2) Construct the disturbance observer: ； in, and are the estimated values ​​of the interference and intermediate states, respectively, is a designed auxiliary variable, is the observer gain; Step 3-3) Design a dynamic event triggering mechanism for the system augmentation state: ； in, is a positive constant, is an additional design parameter, the system augmented state transmission error The definition is as follows: ； is an internal dynamic variable introduced in the dynamic event triggering mechanism, which satisfies the following equation: ； in, To satisfy the local Lipschitz condition function, is the parameter to be designed; Step 3-4) Design a dynamic event triggering mechanism for interference estimation: ； in, is a positive constant, is an additional design parameter, disturbing the estimation error The definition is as follows: ； It is also an internal dynamic variable introduced in the dynamic event triggering mechanism, which satisfies the following equation: ； in, To satisfy the local Lipschitz condition function, is the parameter to be designed; Step 3-5) The dual dynamic event trigger conditions based on system status and interference estimation are as follows: ； in, , and They represent the minimum trigger time lower bounds of the system augmented state and interference estimation under the dynamic event trigger mechanism respectively; Step 3-6) Under the dual dynamic event trigger mechanism, the composite controller is designed as follows: ； in, is the gain of the designed composite controller.

5. The anti-interference tracking control method for a quad-rotor drone based on a dual dynamic event trigger mechanism according to claim 4 is characterized in that: Step 4) specifically includes: According to the controlled system and the disturbance error model, assume , , and The following matrix inequality is satisfied: ； ； in, , , , , , Solving the above linear matrix inequality can prove that the augmented system is stable under the designed composite controller; the controller gain is calculated by The disturbance observer gain is obtained by To obtain.

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