A Quadrotor Trajectory Tracking Control Method Based on an Improved Event Triggering Mechanism

Abstract

This invention discloses a quadrotor trajectory tracking control method based on an improved event-triggered mechanism. In the quadrotor trajectory tracking task, firstly, an anti-saturation auxiliary system is designed to address the actuator input saturation problem of the quadrotor. By modeling the saturation phenomenon and performing pre-compensation, the impact of this problem on the system control performance is reduced. Secondly, by combining an adaptive gain term with a fuzzy controller, the coefficients of the hyperbolic tangent function of the sliding mode reaching law are softened, further suppressing chattering in sliding mode control and improving control performance. Finally, an improved event-triggered mechanism is designed at the actuator end, introducing an adaptive trigger threshold to avoid redundant transmission, enhance dynamic adaptability, and further save communication resources. The method proposed in this invention improves system control performance while meeting the requirements of green flight control.

Description

Technical Field

[0001] This invention belongs to the field of aircraft control, and more specifically, relates to a quadcopter trajectory tracking control method based on an improved event triggering mechanism. Background Technology

[0002] In recent years, unmanned aerial vehicles (UAVs) have been widely used in both military and civilian fields. As a common type of aircraft, quadcopters are small, lightweight, and easy to operate; however, they also face challenges such as underactuation, strong coupling, and time-varying characteristics. Currently, a more comprehensive and integrated analysis is lacking for many of these complex situations.

[0003] With the increasing demands of flight missions, quadcopter control faces numerous challenges. Trajectory tracking control, as a common control task, has attracted considerable attention from scholars, leading to the development of many practical control methods, such as PID control, sliding mode control, predictive control, and adaptive control. Among these, sliding mode control has been widely applied due to its fast response and strong robustness against external disturbances. However, current research has failed to effectively suppress chattering and lacks attention to conserving communication resources.

[0004] Due to continuous innovation in communication engineering and control technology, the large amounts of data generated need to be transmitted in real time through efficient communication networks, which typically leads to high energy consumption and a heavy communication burden. Traditional time-triggered mechanisms are inadequate in complex situations. When the sampling period is short, a large amount of redundant data is released into the network with limited bandwidth; when the sampling period is long, control lag occurs, reducing system stability. Event-triggered mechanisms can reduce the number of control tasks executed, further reducing network resource waste while ensuring certain system performance. However, this efficient data transmission mechanism has not been fully applied in the quadcopter field, and its dynamic adaptability needs further improvement.

[0005] In conclusion, to ensure the stability and accuracy of trajectory tracking, further research should be conducted on trajectory tracking in complex scenarios, while minimizing communication resources. Summary of the Invention

[0006] The purpose of this invention is to propose a trajectory tracking control method for quadrotor aircraft based on an improved event-triggered mechanism. This addresses the complexities of trajectory tracking in practice, including external interference, strong time-varying characteristics, and actuator input saturation. Using sliding mode control as the core method, it integrates fuzzy control and adaptive gain terms to reduce chattering and improve control performance. An anti-saturation auxiliary system is introduced to reduce the impact of saturation on the system through advance compensation. The improved event-triggered mechanism achieves an optimal balance between ensuring control performance and conserving network resources.

[0007] To achieve the above objectives, the present invention provides a quadcopter trajectory tracking control method based on an improved event triggering mechanism, the specific steps of which include:

[0008] Step 1: Establish a mathematical model of aircraft dynamics based on Euler's equations and Newton's second law;

[0009] Step 2: Construct an anti-saturation auxiliary system, model the saturation phenomenon at the actuator input, and feed the compensation term back to the control system to adjust the control input in advance;

[0010] Step 3: Divide the control system into inner and outer dual-loop control, used to design the attitude control subsystem and position subsystem respectively. Using sliding mode control as the core method, combine a fuzzy controller with an adaptive gain term;

[0011] Step 4: Introduce an event triggering mechanism and design a new adaptive threshold triggering condition. When the condition is met, the control law is updated; when the condition is not met, the control law at the previous triggering moment is maintained.

[0012] Furthermore, a mathematical model of aircraft dynamics is established based on Euler's equations and Newton's second law;

[0013] 1) Define the ground coordinate system [x b ,y b ,z b ] and body coordinate system [x g ,y g ,z g Assuming that the angular velocity of the aircraft rotating around its center of mass in the body coordinate system is equal to the Euler angular velocity in the ground coordinate system, the transformation relationship between the two coordinate systems is derived. Then, by Newton's laws, the linear acceleration equation and attitude angular acceleration dynamic equation of the quadcopter in the inertial coordinate system can be obtained.

[0014] 2) Define four control inputs for the quadcopter to control the quadcopter to perform vertical, roll, pitch, and yaw motions respectively.

[0015] Furthermore, an anti-saturation auxiliary system is constructed based on the dynamic mathematical model of the quadcopter, which models the input saturation phenomenon at the actuator end and feeds the compensation term back to the control system to adjust the control input in advance.

[0016] 1) Construct a saturation constraint function of the following form:

[0017]

[0018] Among them, U i For controller output, U imax The actuator input limit is determined; a hyperbolic tangent function is introduced to approximate the limit function, where a is the smoothness coefficient of the hyperbolic tangent function;

[0019] 2) Design and construct an anti-saturation auxiliary system in the following form:

[0020]

[0021] in, ΔU i =sat(U i )-U i g i >0 represents the parameter to be designed, ρ i U is the auxiliary variable for the constructed anti-saturation auxiliary system. i For controller output, sat(U) i ) is the control input after being processed by the anti-saturation auxiliary system.

[0022] Furthermore, the control system is divided into inner and outer dual-loop control, which are used to design the attitude control subsystem and the position subsystem, respectively. Sliding mode control is used as the core method, and the fuzzy controller is combined with the adaptive gain term.

[0023] 1) Based on the aforementioned anti-saturation auxiliary system, design a controller based on adaptive sliding mode; first, define the tracking error;

[0024] 2) Define the sliding surface based on the tracking error described above;

[0025] 3) The design of a new sliding mode reaching law is improved by replacing the sign function in the original exponential reaching law with the hyperbolic tangent function;

[0026] 4) Combine fuzzy control and sliding mode control, and design a fuzzy controller by fuzzifying the gain coefficients before the hyperbolic tangent function in the sliding mode approach law of roll angle and pitch angle;

[0027] The input variables are the sliding surface S and the sliding surface convergence law. The output variable is the gain coefficient c before the hyperbolic tangent function of the sliding mode reaching law; the input variable S is defined as [-6, 6]. Defined as [-15, 15], and the universe of discourse of the output variable c is defined as [-3, 3], the fuzzy set is divided into seven levels; the values ​​of the quantization factor and the scaling factor are determined through debugging and verification; then, the triangular membership function is selected to construct the membership function distribution; the Mamdani algorithm is applied, and the fuzzy implication relationship is expressed using the if A and B then U statement to establish the fuzzy rule table; finally, the centroid method is used to perform defuzzification operation, and the absolute value of the output variable c is taken.

[0028] 5) Before resolving the desired roll and pitch angle signals from the position subsystem, define three virtual control variables; then input the desired signals together with the pre-given yaw angle desired signal into the attitude subsystem.

[0029] 6) Order The control laws for the attitude subsystem and the position subsystem can then be derived.

[0030] Furthermore, an event triggering mechanism is introduced, and a new adaptive threshold triggering condition is designed. When the condition is met, the control law is updated, and when the condition is not met, the control law at the previous triggering moment is maintained.

[0031] 1) First, the control law is transformed as follows:

[0032]

[0033] Where, m i ,η i A constant greater than 0;

[0034] 2) Then design event triggers in the following form:

[0035] e j (t)=w i (t)-W i (t k ),i=1,2,3,4;j=z,φ,θ,ψ (4)

[0036]

[0037] Where, k∈N + ,e j (t) represents the control law w at the current moment. i (t) and the control law W mentioned at the last trigger time i (t k The difference between the control law and the control law received by the actuator when the system does not meet the triggering condition is always the control law at the last triggering time; and the triggering condition for updating the control law is:

[0038]

[0039] Where, σ i (t) is the dynamic adaptive trigger threshold, which is based on the following rule:

[0040] σ i (t)=min{max[σ si ,λ i σ i (th)],σ bi}, i = 1, 2, 3, 4 (7)

[0041]

[0042] Where h is the sampling step size, σ bi >σ si >0, σ si σ is the lower bound of the trigger threshold. bi α is the upper bound of the trigger threshold; i ,β i ,μ i All are non-negative constants.

[0043] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0044] 1. This invention takes into account actuator input saturation, constructs an anti-saturation auxiliary system, and simultaneously constructs an auxiliary state vector, which is introduced into the tracking error to compensate for the influence of input limitations.

[0045] 2. This invention takes into account external interference and proposes a novel control method for trajectory tracking of quadcopters. The controller design combines fuzzy control, adaptive gain term, and sliding mode control, which improves control performance while further reducing chattering in sliding mode control.

[0046] 3. This invention designs a novel adaptive event triggering mechanism at the actuator end of a quadcopter, and designs adaptive update conditions for the trigger threshold, eliminating unnecessary loop operations, thereby reducing resource consumption, helping to improve system performance and improve the overall efficiency of the system, while meeting the requirements of green flight control. Attached Figure Description

[0047] Figure 1 This is a control structure framework diagram proposed in an embodiment of the present invention.

[0048] Figure 2 This is a flowchart illustrating the design of a fuzzy controller according to an embodiment of this invention.

[0049] Figure 3 This is a schematic diagram of the membership function of input and output variables proposed in an embodiment of this invention application;

[0050] Figure 4 This is a schematic diagram of trajectory tracking proposed in an embodiment of the present invention.

[0051] Figure 5 This is a schematic diagram of attitude angle tracking proposed in an embodiment of this invention application;

[0052] Figure 6 This is a schematic diagram illustrating the triggering time of the conventional algorithm proposed in an embodiment of this invention.

[0053] Figure 7 This is a schematic diagram of the improved triggering time proposed in the embodiments of this invention application. Detailed Implementation

[0054] 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.

[0055] The purpose of this invention is to study trajectory tracking in the field of quadcopter control. The specific implementation process is as follows: Figure 1 As shown, firstly, a dynamic model of the quadcopter is established based on Newton's second law and Euler's equations. Then, considering the saturation phenomenon in the actuator input, an anti-saturation auxiliary system is established, constructing an auxiliary state vector to compensate for the saturated input. The quadcopter is divided into inner and outer loop control subsystems, and adaptive sliding mode controllers are designed for the position subsystem and attitude angle subsystem respectively. Considering that the expected values ​​of roll and pitch angles are obtained from the inverse solution of the position subsystem, to further suppress chattering, fuzzy control is combined with adaptive sliding mode control in the attitude subsystem, softening the gain coefficient before the sliding mode reaching law. Finally, to avoid wasting communication resources, an event triggering mechanism is introduced at the quadcopter actuator end, and a new adaptive update condition is designed for the trigger threshold to improve the dynamic adaptability of the triggering condition. To illustrate the feasibility of this invention, the following embodiments will be used for further explanation.

[0056] Example

[0057] Further, please see Figure 1-7 The specific implementation process of this application embodiment is as follows:

[0058] (1) In the embodiments of this application, the present invention establishes a mathematical model of aircraft dynamics based on Euler's equations and Newton's second law;

[0059] 1) Define the ground coordinate system [x b ,y b ,z b ] and body coordinate system [x g ,y g ,z g Assuming the angular velocity of the machine's rotation around its center of mass in the body coordinate system is equal to the Euler angular velocity in the ground coordinate system, the transformation relationship between the two coordinate systems is derived as follows:

[0060]

[0061] 2) Let x, y, and z represent the position coordinates of the quadcopter in the inertial frame; use Let φ represent the linear velocity of the quadcopter in the inertial frame; φ, θ, and ψ represent the roll, pitch, and yaw angles, respectively; and p, q, and r represent the angular velocities of rotation in the body coordinate system, respectively. From Newton's laws, the equation for the linear acceleration of the quadcopter in the inertial frame is:

[0062]

[0063] in, ω is the total lift generated by the four propellers. i k represents the rotational speed of the four motors of the quadcopter. t c is the lift coefficient of the propeller. γ =cosγ,s γ =sinγ,c γ =tanγ, γ = φ, θ, ψ; g is the acceleration due to gravity; m is the mass of the quadcopter.

[0064] 3) The quadcopter has a strictly symmetrical structure; therefore, defining the moment of inertia of the airframe, we can obtain the equation of angular motion as follows:

[0065]

[0066] in, ω i J represents the rotational speed of the four motors of the quadcopter; J represents the moment of inertia of a single motor about its axis; I represents the rotational speed of the four motors of the quadcopter. x I y and I z These are the moments of inertia of the machine body along the x-axis, y-axis, and z-axis, respectively.

[0067] 4) Define the four control inputs of the quadcopter to control the quadcopter's vertical, roll, pitch, and yaw motions, respectively, specifically expressed as follows:

[0068]

[0069] Where, ω i k represents the rotational speed of the four motors of the quadcopter. d Let L be the rotation coefficient, and L be the distance from the motor of the quadcopter to its center of gravity.

[0070] (2) In the embodiments of this application, the present invention constructs an anti-saturation auxiliary system, models the input saturation phenomenon at the actuator end, feeds back the compensation term to the control system, and adjusts the control input in advance;

[0071] 1) Actuator saturation problem is caused by its inherent characteristics. Systems operating in a saturated state are unlikely to respond as expected, leading to reduced control performance. First, define the saturation constraint function:

[0072]

[0073] Among them, U i For controller output, U imax The actuator input limit is used; to ensure nonlinear characteristics, a hyperbolic tangent function is introduced to approximate the limit function, and the smoothness of the saturation characteristic curve is adjusted by parameter a.

[0074] 2) In order to reduce the impact of the actuator input on the system control effect while being constrained by the saturation constraint function, an anti-saturation auxiliary system of the following form is designed and constructed.

[0075]

[0076] in, ΔU i =sat(U i )-U i g i >0 represents the parameter to be designed, ρ i U is the auxiliary variable for the constructed anti-saturation auxiliary system. i For controller output, sat(U) i The control input is the result of processing by the anti-saturation auxiliary system.

[0077] (3) In the embodiments of this application, the present invention divides the control system into inner and outer dual-loop control, which are used to design the attitude control subsystem and the position subsystem, respectively. Taking sliding mode control as the core method, the fuzzy controller is combined with an adaptive gain term to suppress the chattering phenomenon generated in sliding mode control;

[0078] 1) To accurately track the desired signals in position and attitude directions and ensure that the tracking error converges within the effective time, an adaptive sliding mode controller is designed based on the aforementioned anti-saturation auxiliary system to meet control performance requirements. First, the tracking error is defined as follows:

[0079]

[0080] Where, x d ,y d ,z d ,φ d ,θ d ,ψ dThe desired signals represent the position and attitude directions; x, y, z represent the position and orientation state information of the quadcopter, respectively; φ, θ, ψ represent the attitude and orientation state information of the quadcopter, respectively.

[0081] 2) Define the sliding surface based on the tracking error:

[0082]

[0083] Among them, b i All are greater than 0 and are constants, e i The tracking error is defined in both the position direction and the attitude angle direction.

[0084] 3) The sign function in the original exponential reaching law is replaced by a hyperbolic tangent function, and the controller performance is enhanced based on an adaptive gain term; this ensures finite convergence in the reaching phase and also suppresses chattering to some extent; the improved sliding mode reaching law is designed as follows:

[0085]

[0086] Where, k i All are greater than 0 and are constants, where a is the smoothing coefficient in the hyperbolic tangent function that guarantees nonlinearity, and ε i An adaptive law of the following form:

[0087]

[0088] Where, ω i >0, c i >0, δ i >0 represents the parameter to be designed;

[0089] 4) Since the expected signals of roll and pitch angles in attitude angles are calculated from the position subsystem, they are more susceptible to the influence of the position subsystem itself. The gain before the hyperbolic function term is the main cause of chattering and will destroy the control performance of the system. In this embodiment, fuzzy control and sliding mode control are combined. By fuzzifying the gain coefficient before the hyperbolic tangent function in the sliding mode reaching law of roll and pitch angles, a fuzzy controller is designed to reduce chattering and improve control accuracy.

[0090] The input variables are the sliding surface S and the sliding surface convergence law. The output variable is the gain coefficient c before the hyperbolic tangent function of the sliding mode reaching law; considering the control difficulty and accuracy of the attitude subsystem, the universe of discourse of the input variable S is defined as [-6, 6]. Defined as [-15, 15], and the universe of discourse of the output variable c as [-3, 3], the fuzzy set is divided into seven levels. After multiple debugging and verifications, the values ​​of the quantization factor and scaling factor are determined. Then, the triangle membership function with the fastest fuzzification and defuzzification is selected to quickly suppress chattering. The widely used Mamdani algorithm is applied, and the if A and B then U statement is used to express the fuzzy implication relationship. Taking into account both response speed and chattering suppression, a fuzzy rule table is established. Finally, the centroid method is used for defuzzification, and the absolute value of the output variable c is taken.

[0091]

[0092] Among them, A(c i ) is the membership function on the universe of discourse of the output variable;

[0093] 5) Before the desired roll and pitch angle signals can be derived from the position subsystem, three virtual control variables are defined:

[0094]

[0095] The obtained desired signal is then fed into the attitude subsystem along with the pre-given desired yaw angle signal.

[0096]

[0097] 6) Order The sliding mode control law can then be derived as follows:

[0098]

[0099] (4) In the embodiments of this application, the present invention introduces an event triggering mechanism and designs a new adaptive threshold triggering condition. When the condition is met, the control law is updated, and when the condition is not met, the control law at the previous triggering moment is maintained.

[0100] 1) First, the control law undergoes a certain formal transformation:

[0101]

[0102] Where, m i ,η i It is a constant greater than 0.

[0103] 2) Then design the following type of event trigger:

[0104] e j (t)=w i (t)-W i (t k ),i=1,2,3,4; j=z,φ,θ,ψ (24)

[0105]

[0106] Where, k∈N + e j (t) represents the control law w at the current time. i (t) and the control law W at the last trigger time i (t k The difference between the two values; when the system does not meet the triggering condition, the control law received by the actuator is always the control law at the last triggering time; and the system control law update triggering condition is:

[0107]

[0108] Where, σ i (t) is the dynamic adaptive trigger threshold, which is based on the following rule:

[0109] σ i (t)=min{max[σ si , λ i σ i (th)],σ bi}, i = 1, 2, 3, 4 (27)

[0110]

[0111] Where h is the sampling step size, σ bi >σ si >0, σ si σ is the lower bound of the trigger threshold. bi α is the upper bound of the trigger threshold; i ,β i μ i All are non-negative constants. According to the dynamic change of the trigger threshold under this mechanism, an optimal balance is achieved between ensuring certain system performance and saving communication resources.

[0112] Experimental test:

[0113] The physical parameters of the quadcopter aircraft were tested using a system platform developed by a team led by Professor Quan Quan of Beijing University of Aeronautics and Astronautics. The entire framework was built in Matlab 2022b and Simulink, and the physical parameters are shown in Table 1.

[0114] physical parameters numerical values m 2 <![CDATA[k t ]]> <![CDATA[1.276×10 -5 ]]> <![CDATA[k d ]]> <![CDATA[1.747×10 -5 ]]> <![CDATA[k x ,k y ,k z ]]> <![CDATA[8.268×10 -2 ]]> L 0.25 <![CDATA[I x ,I y ]]> <![CDATA[3.009×10 -2 ]]> <![CDATA[I z ]]> <![CDATA[5.603×10 -2 ]]> J <![CDATA[1.34×10 -4 ]]>

[0115] Table 1. Physical parameters of the quadcopter.

[0116] The initial state of the quadcopter's position is [0, 0, 0], the initial state of its attitude angle is [0, 0, 0], the initial state of its linear velocity is [0, 0, 0], and the initial state of its angular velocity is [0, 0, 0]. The simulation duration is 20 seconds. The desired signal for the position direction is [2, 2, 0.1t], and the desired signal for the yaw angle is... Furthermore, considering the complex dynamic environment in reality, external disturbances are introduced into the position loop and attitude loop respectively. The disturbance value of the position loop is [0.25sint, 0.25sint, 0.25sint], and the disturbance value of the attitude loop is [0.2sint, 0.3sint, 0.31sint].

[0117] The red dashed line represents the desired signal of the state, the blue solid line (FASMC+AET+AIS) represents the algorithm in this paper, and the green dotted line (SMC+FET) represents the traditional fixed threshold sliding mode control algorithm. Figures 4 to 7 As can be seen, the algorithm proposed in this invention has a significantly faster convergence and response speed than traditional algorithms, further reducing communication resource consumption while maintaining a certain level of control accuracy. Furthermore, the adaptive gain and fuzzy control concepts introduced in this invention significantly suppress chattering phenomena that occur in sliding mode control. Based on the improved event triggering mechanism of this invention, communication resource consumption and waste can be further reduced while maintaining a certain control effect, improving overall system efficiency and achieving the goal of green flight control. In addition, the algorithm without the event triggering mechanism averages 4001 communication transmissions; while the traditional fixed threshold event triggering mechanism averages 2411 communication transmissions, reducing communication resource consumption by 39.74%; and the adaptive threshold event triggering mechanism of this invention averages 1207 communication transmissions, reducing communication resource consumption by 69.80%.

[0118] This patent proposes a highly efficient trajectory tracking control method for quadrotor aircraft. By constructing an anti-saturation auxiliary strategy, combining fuzzy control with adaptive gain terms and sliding mode control, and improving the event triggering mechanism, an optimal balance between control performance and network resource conservation is achieved, meeting the requirements of green flight control. Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A quadcopter trajectory tracking control method based on an improved event-triggered mechanism, characterized in that, include: Step 1: Establish a dynamic mathematical model of the quadcopter based on Euler's equations and Newton's second law; Step 2: Construct an anti-saturation auxiliary system based on the dynamic mathematical model of the quadcopter, model the input saturation phenomenon at the actuator end, and feed the compensation term back to the control system to adjust the control input in advance; Step 3: Divide the control system into inner and outer dual-loop control, which are used to design the attitude control subsystem and the position subsystem respectively. Using sliding mode control as the core method, the fuzzy controller is combined with the adaptive gain term. Step 4: Introduce an event triggering mechanism and design an adaptive threshold triggering condition. When the condition is met, update the control law; when the condition is not met, maintain the control law from the previous triggering moment. The implementation process of step (4) is as follows: 1) First, the control law is transformed as follows: Where, m i ,η i A constant greater than 0; 2) Then design event triggers in the following form: e j (t)=w i (t)-W i (t k ),i=1,2,3,4;j=z,φ,θ,ψ Where, k∈N + ,e j (t) represents the control law w at the current moment. i (t) and the control law W mentioned at the last trigger time i (t k The difference between the control law and the control law received by the actuator when the system does not meet the triggering condition is always the control law at the last triggering time; and the triggering condition for updating the control law is: Where, σ i (t) is the dynamic adaptive trigger threshold, which is based on the following rule: s i (t)=min{max[σ si ,l i s i (th)],s bi },i=1,2,3,4 Where h is the sampling step size, σ bi >σ si >0, σ si σ is the lower bound of the trigger threshold. bi α is the upper bound of the trigger threshold; i ,β i ,μ i All are non-negative constants.

2. The quadrotor trajectory tracking control method based on an improved event triggering mechanism according to claim 1, characterized in that, The implementation process of step (1) is as follows: 1) Define the ground coordinate system [x b ,y b ,z b ] and body coordinate system [x g ,y g ,z g Assuming the angular velocity of the machine's rotation around its center of mass in the body coordinate system is equal to the Euler angular velocity in the ground coordinate system, the transformation relationship between the two coordinate systems is derived as follows: 2) Let x, y, and z represent the position coordinates of the quadcopter in the inertial frame; use and Let represent the linear velocity of the quadcopter in the inertial frame; let φ, θ, and ψ represent the roll angle, pitch angle, and yaw angle, respectively; let p, q, and r represent the angular velocities of rotation in the body coordinate system, respectively; and let the equation of linear acceleration of the quadcopter in the inertial frame be obtained from Newton's laws. in, ω is the total lift generated by the four propellers. i k represents the rotational speed of the four motors of the quadcopter. t c is the lift coefficient of the propeller. γ =cosγ,s γ =sinγ,c γ =tanγ, γ = φ, θ, ψ; g is the acceleration due to gravity; m is the mass of the quadcopter. 3) Establish the dynamic equation for attitude angular acceleration as follows: in, ω i J represents the rotational speed of the four motors of the quadcopter; J represents the moment of inertia of a single motor about its axis; I represents the rotational speed of the four motors of the quadcopter. x I y and I z These are the moments of inertia of the machine body along the x-axis, y-axis, and z-axis, respectively. 4) Define the four control inputs of the quadcopter to control the quadcopter's vertical, roll, pitch, and yaw motions, respectively, specifically expressed as follows: Where [U1,U2,U3,U4] are the control inputs of the quadcopter, k d k is the rotation coefficient. t Let L be the lift coefficient of the propeller, and L be the distance from the motor of the quadcopter to the center of gravity of the quadcopter.

3. The quadrotor trajectory tracking control method based on an improved event triggering mechanism according to claim 2, characterized in that, In step (2): 1) Construct a saturation constraint function of the following form: Among them, U i For controller output, U imax The actuator input limit is determined; a hyperbolic tangent function is introduced to approximate the limit function, where a is the smoothness coefficient of the hyperbolic tangent function; 2) Design and construct an anti-saturation auxiliary system in the following form: in, ΔU i =sat(U i )-U i g i >0 represents the parameter to be designed, ρ i U is the auxiliary variable for the constructed anti-saturation auxiliary system. i For controller output, sat(U) i ) is the control input after being processed by the anti-saturation auxiliary system.

4. The quadrotor trajectory tracking control method based on an improved event triggering mechanism according to claim 3, characterized in that, In step (3): 1) Based on the aforementioned anti-saturation auxiliary system, design a controller based on adaptive sliding mode; first, define the tracking error: Where, x d ,y d ,z d ,φ d ,θ d ,ψ d The desired signals for position and attitude directions are respectively represented by x, y, and z, which represent the position and orientation state information of the quadcopter, respectively, and φ, θ, and ψ represent the attitude and orientation state information of the quadcopter. 2) Define the sliding surface based on the tracking error described above: Among them, b i e is a constant greater than 0. i The tracking error is defined in both the position direction and the attitude angle direction. 3) By replacing the sign function in the original exponential reaching law with the hyperbolic tangent function, the improved sliding mode reaching law is designed as follows: Where, k i All are greater than 0 and are constants, where a is the smoothing coefficient in the hyperbolic tangent function that guarantees nonlinearity, and ε i An adaptive law of the following form: Where, ω i >0, c i >0, δ i >0 represents the parameter to be designed; 4) Combine fuzzy control and sliding mode control, and design a fuzzy controller by fuzzifying the gain coefficients before the hyperbolic tangent function in the sliding mode approach law of roll angle and pitch angle; The input variables are the sliding surface S and the sliding surface convergence law. The output variable is the gain coefficient c before the hyperbolic tangent function of the sliding mode reaching law; the input variable S is defined as [-6, 6]. Defined as [-15, 15], and the universe of discourse of the output variable c as [-3, 3], the fuzzy set is divided into seven levels; the values ​​of the quantization factor and the scaling factor are determined through debugging and verification; then, a triangular membership function is selected to construct the membership function distribution; the Mamdani algorithm is applied, and the fuzzy implication relationship is expressed using the if A and B then U statement to establish the fuzzy rule table; finally, the centroid method is used for defuzzification operation, and the absolute value of the output variable c is taken: Among them, A(c i ) is the membership function on the universe of discourse of the output variable; 5) Before resolving the desired roll and pitch angle signals from the position subsystem, define three virtual control variables: The desired signal is then fed into the attitude control subsystem along with the pre-given yaw angle desired signal. 6) Order The control law can then be derived as follows:

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