Position-attitude layered composite adaptive sliding mode control method for six-rotor unmanned aerial vehicle

Abstract

The invention provides a six-rotor unmanned aerial vehicle position-attitude layered composite adaptive sliding-mode control method, which comprises the steps of introducing a terminal sliding-mode robust controller into a position loop to realize finite time convergence of a system state, and performing real-time compensation on unknown disturbance and modeling uncertainty through a sliding-mode adaptive rate. The quick response and anti-interference capability of a complex track are improved; superspiral sliding mode control is adopted in an attitude ring, sliding mode gain is adjusted in real time through an adaptive law, a controller is made to automatically match flight state changes, and attitude stability and robustness are enhanced; and meanwhile, a position signal output by the position controller is converted into a specific attitude angle reference by utilizing an attitude resolving module, so that hierarchical cooperative control of the position and the attitude is realized. Compared with traditional PID and SMC controllers, the method is faster in convergence and higher in tracking precision, has stronger adaptivity and robustness to external disturbance and parameter uncertainty, and can significantly improve the motion control performance of the six-rotor unmanned aerial vehicle in complex environments and tasks.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicle (UAV) control, and in particular relates to a position and attitude control method for a six-rotor UAV based on finite-time hierarchical composite adaptive sliding mode control. Background Art

[0002] With the continuous advancement of multi-rotor drone technology, hexacopter drones (UAVs) have significant advantages in complex environment missions due to their redundant propulsion systems and high payload capacity. However, as nonlinear, underactuated systems, hexacopter drones are susceptible to various factors, including external disturbances, parameter uncertainty, and modeling errors, making flight control stability and accuracy difficult to ensure. Currently, mainstream control methods include PID, sliding mode control, model reference adaptive control (MRAC), neural network compensation, and model predictive control (MPC). These methods have achieved certain success in specific scenarios. However, when faced with strong uncertain disturbances, high-precision tracking, and real-time adaptation requirements, they often suffer from overshoot, oscillation, slow response, and steady-state errors, failing to meet the high-performance motion control requirements of hexacopter drones in complex environments. Therefore, developing a robust, responsive, and adaptive hexacopter control method for complex disturbances is of great theoretical and engineering significance. Summary of the Invention

[0003] The purpose of the present invention is to overcome the defects of the prior art and propose a position-attitude hierarchical composite adaptive sliding mode control method for a six-rotor UAV, so as to realize high-precision, strong robustness and finite-time convergence motion control of the six-rotor UAV in complex environments.

[0004] In order to achieve the above-mentioned purpose, the technical solutions provided by the present invention are as follows: Step 1: Establish a dynamic model for the three-dimensional spatial position (x-axis, y-axis, z-axis) and attitude (roll, pitch, yaw) of the hexacopter, assuming that it is a rigid body, that the lift is proportional to the square of the rotational speed, that air resistance is ignored, and that the mass is uniformly distributed. Determine the position model differential equation: Where: P = [xyz] T represents the position matrix of the UAV, ν=[ν x ν y ν z ] T represents the velocity matrix of the drone, m represents the mass of the drone, g represents the acceleration of gravity, and e=[0 0 1] T The unit vector representing the direction of gravity, F = [F x F y F z ] Trepresents the force of the position channel, d v Indicates external interference; Determine the attitude model differential equation: where η = [φ θ ψ] T represents the roll, pitch, and yaw matrices of the drone, ω = [pqr] T The angular velocity matrix of the roll angle, pitch angle and yaw angle of the drone, I = diag{I xx I yy I zz} represents the diagonal matrix of moment of inertia, L = diag{ll 1} represents the diagonal matrix of the distance from the center of mass of the drone to the center of mass of the motor, τ = [τ φ τ θ τ φ ] T represents the aerodynamic moment matrix of the UAV, d ω Represents the external interference on the attitude angle, R represents the three-axis transformation matrix between the angular velocity vector and the Euler angle vector; Step 2: By defining the position error, designing the terminal sliding mode surface and finite-time robust control law, and introducing an adaptive rate, online compensation for external disturbances and model uncertainties is performed. Finally, a terminal adaptive sliding mode robust controller is established in the position loop to achieve finite-time precise tracking of the UAV's three-axis position. Step 3: The desired acceleration and thrust signals output by the position loop are converted into desired pitch, roll, and yaw angle reference values ​​in real time through the attitude calculation module, completing the position-attitude calculation and command mapping. Step 4: To address the nonlinearity and external disturbances of the UAV attitude control, a super-helical sliding mode control law was designed. By introducing an adaptive law to dynamically adjust the sliding mode gain, high-frequency chattering was reduced. Finally, a gain-adaptive super-helical sliding mode controller was designed to achieve finite-time robust convergence of the attitude angle. Step 5: Based on the Lyapunov stability principle, Lyapunov functions are designed for position and attitude respectively to verify the stability of the designed system and whether it can converge within a finite time. Step 6: The obtained attitude torque signal is transformed into six sets of motor speed commands through dynamic inverse solution and combined with the hexacopter dynamics model. The motors are driven by PWM modulation to complete the position-attitude hierarchical composite adaptive sliding mode control of the hexacopter UAV and achieve precise control of the six-degree-of-freedom flight state. The motor allocation matrix is ​​expressed as:

[0005] Furthermore, in step 1, the mathematical model of position and posture is established, and its workflow includes the following steps: Step 1.1: According to Newton's second law, we can get: in: Integrating all the position formulas in step 1 yields: Step 1.2: Since the attitude angle of the UAV changes slightly during flight, in order to optimize the mathematical model of the UAV, the transformation matrix R can be approximated as the unit matrix: Integrating all the posture formulas in step 1, we can get:

[0006] Furthermore, in step 2, the terminal adaptive sliding mode robust controller has the following steps: Step 2.1: Define the error between the actual position of the drone and the expected position as: e P =PP d Where: P d =[x d y d z d ] T represents the desired position of the six-rotor drone, e P =[e Px e Py e Pz ] T Represents the position tracking error of the UAV; Step 2.2: Using the sign function to accelerate the convergence in finite time and effectively suppress the uncertainty of the system and the properties of external disturbances, define the following terminal sliding surface: Where: c1=diag(c 1x ,c 1y ,c 1z )>0,c2=diag(c 2x ,c 2y ,c 2z )>0 is a diagonal gain matrix, are the sign functions representing the position errors of the x, y, and z axes respectively; Step 2.3: To counteract external interference and accelerate the convergence of the sliding surface, the following robust control rate is designed: Where: a represents the diagonal sliding gain matrix, is the external disturbance d v estimated value of; Step 2.4: In order to achieve progressive tracking of external disturbances, the sign function of the sliding surface is introduced and the following adaptive law is designed to estimate the unknown disturbance in real time: Where: k>0 is the adaptive gain.

[0007] Furthermore, in step 3, the position-attitude solution and instruction mapping process includes the following steps: Step 3.1: In the z-axis direction, establish that the resultant force F and the z-axis thrust T satisfy the following relationship: Step 3.2: Calculate the modulus of the total thrust T: Step 3.3: Calculate the vertical balance equation of the drone when it moves in space: F z + mg = T cos θ cos φ Step 3.4: The control quantity F of channel x and channel y obtained by the position controller x 、F y Converted into the expected signal φ of the attitude path d ,θ d :

[0008] Furthermore, in step 4, the gain adaptive super-helical synovial control has a workflow including the following steps: Step 4.1: Define the error between the actual attitude angle and the target attitude angle; e η =η-η d Where: η d =[φ d θ d ψ d ] T represents the target attitude angle of the six-rotor drone, e η =[e ηφ e ηθ e ηψ ] T Represents the attitude tracking error of the UAV; Step 4.2: Calculate the first-order derivative and second-order derivative of the defined error to obtain the attitude angular velocity error and attitude angular acceleration error, and make the following substitutions: Step 4.3: Based on the characteristics of super-helical sliding film control, define the following sliding surface: Where: c3 represents the diagonal gain matrix; Step 4.4: After determining the sliding surface, take the derivative of the sliding surface function to obtain the relationship between the controlled parameter τ and the sliding derivative. Available control rate: Step 4.5: Establish the adaptive rate of gain in the supercoil synaptosome control rate: Where: γ1 and γ2 represent the adaptation rate parameters.

[0009] Furthermore, in step 5, the stability and finite time convergence of the closed-loop system are verified, and the workflow includes the following steps: Step 5.1: Construct the Lyapunov functions of the position loop and attitude loop respectively: in: represents the error estimate, k1 * 、k2 * is the ideal gain; Step 5.2: Calculate the time derivative According to the Lyapunov stability judgment principle: when V>0 and V(0)=0, Determine whether the closed-loop system is stable; Step 5.3: According to the finite-time convergence theorem, find n and β in the constructed Lyapunov function to satisfy: Step 5.4: Calculate the convergence time of the system through step 5.3 to ensure that the designed system can converge to a stable state within a limited time.

[0010] The present invention proposes a finite-time hierarchical composite adaptive sliding mode control method for a six-rotor UAV. Compared with the existing technology, its beneficial effects include the following: 1. High robustness: Adaptive sliding mode gain and online interference estimation enable real-time compensation for external disturbances and modeling errors, effectively enhancing the anti-interference capability of the hexacopter system. 2. Strong adaptability: Gain parameters can be dynamically adjusted according to system status, eliminating the need for trial and error, reducing reliance on manual parameter adjustment, and adapting to multi-task and multi-environment changes; 3. Finite-time convergence: Through terminal sliding mode and super-helical sliding mode design, the system state converges within a finite time, improving task response speed and dynamic performance; 4. High tracking accuracy: Both the position and attitude channels significantly reduce steady-state error and overshoot, ensuring high-precision position and attitude control of the UAV, especially when dealing with complex environments or disturbances. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 This is a flow chart of the position-attitude hierarchical composite adaptive sliding mode control method for the six-rotor UAV proposed in the present invention. Figure 2 This is the structure diagram of the position-attitude hierarchical composite adaptive sliding mode control system of the six-rotor UAV; Figure 3 is the X-axis trajectory tracking curve in Experimental Example 1; Figure 4 It is the Y-axis trajectory tracking curve in Experimental Example 1; Figure 5 It is the Z-axis trajectory tracking curve in Experimental Example 1; Figure 6 is the X-axis tracking error curve in Experimental Example 1; Figure 7 is the Y-axis tracking error curve in Experiment 1; Figure 8 is the Z-axis tracking error curve in Experiment 1; Figure 9 is the roll angle tracking curve in Experimental Example 1; Figure 10 is the pitch angle tracking curve in Experimental Example 1; Figure 11 is the yaw angle tracking curve in Experimental Example 1; Figure 12 is the roll angle tracking error curve in Experimental Example 1; Figure 13 is the pitch angle tracking error curve in Experimental Example 1; Figure 14 is the yaw angle tracking error curve in Experimental Example 1; Figure 15 This is the spiral trajectory tracking comparison curve in Experimental Example 1; Figure 16 This is the X-axis tracking error curve of the layered composite controlled spiral trajectory in Experimental Example 1; Figure 17 This is the Y-axis tracking error curve of the layered composite controlled spiral trajectory in Experimental Example 1; Figure 18 This is the Z-axis tracking error curve of the layered composite controlled spiral trajectory in Experimental Example 1; Figure 19 This is the roll angle tracking error curve of the spiral trajectory under layered composite control in Experimental Example 1; Figure 20 This is the pitch angle tracking error curve of the layered composite control spiral trajectory in Experimental Example 1; Figure 21 The yaw angle tracking error curve of the layered composite control spiral trajectory in Experimental Example 1; Figure 22 This is the output curve of the spiral trajectory position control in the layered composite control in Experimental Example 1; Figure 23 This is the output curve of the spiral trajectory attitude control of the layered composite control in Experimental Example 1. DETAILED DESCRIPTION

[0012] The present application is described in detail below with reference to specific embodiments. The following embodiments will help those skilled in the art to further understand the present application, but are not intended to limit the present application in any form. It should be noted that, for those skilled in the art, several variations and improvements can be made without departing from the concept of the present application. These fall within the scope of protection of the present application. Example 1 The present application discloses a hexacopter position-attitude hierarchical composite adaptive sliding mode control scheme, comprising the following steps: Step 1: Establish a dynamic model for the three-dimensional spatial position (x-axis, y-axis, z-axis) and attitude (roll, pitch, yaw) of the hexacopter, assuming that it is a rigid body, that the lift is proportional to the square of the rotational speed, that air resistance is ignored, and that the mass is uniformly distributed. Determine the position model differential equation: Where: P = [xyz] T represents the position matrix of the UAV, ν=[ν x ν y ν z ] T represents the velocity matrix of the drone, m represents the mass of the drone, g represents the acceleration of gravity, and e=[0 0 1] T The unit vector representing the direction of gravity, F = [F x F y F z ] T represents the force of the position channel, d v Indicates external interference. Determine the attitude model differential equation: where η = [φ θ ψ] Trepresents the roll, pitch, and yaw matrices of the drone, ω = [pqr] T The angular velocity matrix of the roll angle, pitch angle and yaw angle of the drone, I = diag{I xx I yy I zz} represents the diagonal matrix of moment of inertia, L = diag{ll 1} represents the diagonal matrix of the distance from the center of mass of the drone to the center of mass of the motor, τ = [τ φ τ θ τ φ ] T represents the aerodynamic torque of the UAV, d ω Represents the external interference on the attitude angle, and R represents the three-axis transformation matrix between the angular velocity vector and the Euler angle vector. Step 2: By defining the position error, designing the terminal sliding mode surface and finite-time robust control law, and introducing an adaptive rate, external disturbances and model uncertainties are compensated online. Finally, a terminal adaptive sliding mode robust controller is designed for the position loop to achieve finite-time precise tracking of the UAV's three-axis position. Step 3: The desired acceleration and thrust signals output by the position loop are converted into desired pitch, roll, and yaw angle reference values ​​in real time through the attitude calculation module, completing the position-attitude calculation and command mapping. Step 4: To address the nonlinearity and external disturbances of the UAV attitude control, a super-helical sliding mode control law was designed. By introducing an adaptive law to dynamically adjust the sliding mode gain, high-frequency chattering was reduced. Finally, a gain-adaptive super-helical sliding mode controller was designed to achieve finite-time robust convergence of the attitude angle. Step 5: Based on the Lyapunov stability principle, Lyapunov functions are designed for position and attitude respectively to verify the stability of the designed system and whether it can converge within a finite time; Step 6: System closed-loop coordination and control implementation: The obtained attitude torque signal is transformed into six sets of motor speed commands through dynamic inverse solution and combined with the hexacopter dynamics model. The motors are driven by PWM modulation to complete the position-attitude hierarchical composite adaptive sliding mode control of the hexacopter UAV, achieving precise control of the six-degree-of-freedom flight state. The motor allocation matrix is ​​expressed as: Where: c t represents the lift coefficient, c m Represents the counter-torque coefficient.

[0013] Furthermore, in step 1, the mathematical model of position and posture is established, and its workflow includes the following steps: Step 1.1: According to Newton's second law, we can get: in: Integrating all the position formulas in step 1 yields: Step 1.2: Since the attitude angle of the UAV changes slightly during flight, in order to optimize the mathematical model of the UAV, the transformation matrix R can be approximated as the unit matrix: Integrating all the posture formulas in step 1, we can get:

[0014] Furthermore, in step 2, the terminal adaptive sliding mode robust controller controller has the following steps: Step 2.1: Define the error between the actual position of the drone and the expected position as: e P =PP d Where: P d =[x d y d z d ] T represents the desired position of the six-rotor drone, e P =[e Px e Py e Pz ] T Represents the position tracking error of the UAV; Step 2.2: Using the sign function to accelerate the convergence in finite time and effectively suppress the uncertainty of the system and the properties of external disturbances, define the following terminal sliding surface: Where: c1=diag(c 1x ,c 1y ,c 1z )>0,c2=diag(c 2x ,c 2y ,c 2z )>0 is a diagonal gain matrix, are the sign functions representing the position errors of the x, y, and z axes respectively; Step 2.3: To counteract external interference and accelerate the convergence of the sliding surface, the following robust control rate is designed: Where: a represents the diagonal sliding gain matrix, is the external disturbance d v estimated value of; Step 2.4: In order to achieve progressive tracking of external disturbances, the sign function of the sliding surface is introduced and the following adaptive law is designed to estimate the unknown disturbance in real time: Where: k>0 is the adaptive gain.

[0015] Furthermore, in step 3, the position-attitude solution and instruction mapping process includes the following steps: Step 3.1: In the z-axis direction, establish that the resultant force F and the z-axis thrust T satisfy the following relationship: Step 3.2: Calculate the modulus of the total thrust T: Step 3.3: Calculate the vertical balance equation of the drone when it moves in space: F z + mg = T cos θ cos φ Step 3.4: The control quantity F of channel x and channel y obtained by the position controller x 、F y Converted into the expected signal φ of the attitude path d ,θ d :

[0016] Furthermore, in step 4, the gain adaptive super-helical synovial control has a workflow including the following steps: Step 4.1: Define the error between the actual attitude angle and the target attitude angle; e η =η-η d Where: η d =[φ d θ d ψ d ] T represents the target attitude angle of the six-rotor drone, e η =[e ηφ e ηθ e ηψ ] T Represents the attitude tracking error of the UAV; Step 4.2: Calculate the first-order derivative and second-order derivative of the defined error to obtain the attitude angular velocity error and attitude angular acceleration error, and make the following substitutions: Step 4.3: Based on the characteristics of super-helical sliding film control, define the following sliding surface: Where: c3 represents the diagonal gain matrix; Step 4.4: After determining the sliding surface, the relationship between the controlled parameter τ and the sliding mode derivative can be obtained by taking the derivative of the sliding surface function. Available control rate: Step 4.5: Establish the adaptive rate of gain in the supercoil synaptosome control rate: Where: γ1 and γ2 represent the adaptation rate parameters.

[0017] Furthermore, in step 5, the stability and finite time convergence of the closed-loop system are verified, and the workflow includes the following steps: Step 5.1: Construct the Lyapunov functions of the position loop and attitude loop respectively: in: represents the error estimate, k1 * 、k2 * is the ideal gain; Step 5.2: Calculate the time derivative According to the Lyapunov stability judgment principle: when V>0 and V(0)=0, Determine whether the closed-loop system is stable; Step 5.3: According to the finite-time convergence theorem, find n and β in the constructed Lyapunov function to satisfy: Step 5.4: Calculate the convergence time of the system through step 5.3 to ensure that the designed system can converge to a stable state within a limited time.

[0018] The following simulation is performed on a position-attitude hierarchical composite adaptive sliding mode control method for a six-rotor UAV disclosed in this application to demonstrate the effectiveness and feasibility of the control strategy, as follows: The design of the various parameters of the UAV in the simulation experiment is listed as follows: the mass of the six-rotor UAV is m = 1.298 kg, the arm of the six-rotor UAV is l = 0.53 m, the moment of inertia of the x-axis and y-axis is I xx / I yy =0.035kg·m 2 , the moment of inertia about the z axis I zz =0.06kg·m 2 , lift coefficient c t =1.1×10-4N / (rpm2 ), counter torque coefficient c m =3.5×10-6N\cdotpm / (rpm 2 ). The controller parameter designs in the simulation experiment are listed as follows: diagonal gain matrix c1 = diag{4 4 5}, c2 = diag{4 5 1}, Hurwitz matrix c3 = diag{5 5 7}, diagonal sliding mode gain matrix a = diag{6 3 2}, adaptive gain k = 2, adaptive parameters γ1 = 4, γ2 = 6.

[0019] The simulation results are as follows: The results of the position simulation experiment are as follows Figures 3 to 8 shown. Figures 3 to 5 The figure shows the position tracking curves of the x-axis, y-axis, and z-axis. It can be seen from the figure that the tracking speed of the traditional sliding mode control is lower than that of its terminal sliding mode robust control. There is a certain range of oscillation in the PID control. There is a tracking delay of 0.33s in the tracking of the z-axis. The terminal sliding mode robust controller curve fits the desired trajectory significantly better than the sliding mode controller and PID controller. Figures 6 to 8 It represents the position tracking error of the x-axis, y-axis and z-axis. It can be seen from the figure that the end sliding mode robust control has overshoots with peak values ​​of 0.12m and 0.26m in the tracking of the x-axis and y-axis, but the controller can quickly recover to a stable state within 0.16s, and the stability error is always stable within ±0.05m; the traditional sliding film control has a rise time of 1.25s and 2.91s for the tracking of the x-axis and y-axis respectively, and its response speed is much lower than the end sliding mode robust control; the PID control not only has a long rise time, but also has a tracking error of ±0.8m for the three axes, and its tracking accuracy is lower than the other two controls.

[0020] The results of the posture simulation experiment are as follows Figures 9 to 14 shown. Figures 9 to 11 It represents the tracking performance of roll angle, pitch angle and yaw angle. It can be seen from the figure that the gain adaptive super-helical sliding mode controller designed in this paper shows fast convergence characteristics in all three degrees of freedom, and the tracking of attitude angle reaches steady state within 1s without overshoot; its stabilization time is improved by 48% and 60% respectively compared with traditional sliding mode control and PID control. Figures 12 to 14 The figure shows the tracking errors for roll, pitch, and yaw angles. The roll error converges to within ±0.3° within 2 seconds, and the pitch error stabilizes within ±0.2° after 3 seconds. The yaw error converges 40% faster than the SMC. The SMC's inherent chattering leads to slightly higher steady-state errors (roll error approximately ±0.5°), while PID control, affected by its nonlinear characteristics, results in significant error fluctuations (pitch error up to 2.2°, yaw steady-state error ±0.6°).

[0021] The above simulation experiments show the performance comparison between the TA-SMRC-based position controller and the GA-STSMC-based attitude controller and the traditional sliding mode controller and PID controller. Through experimental comparison, the TA-SMRC position controller and the GA-STSMC attitude controller are superior to the sliding mode control and PID control in terms of response speed, tracking accuracy and stability. In order to verify the feasibility and effectiveness of the position-attitude hierarchical composite control scheme designed in this paper, a spiral trajectory tracking experiment was designed and compared with the sliding mode control and PID control for both position and attitude. The experimental simulation results are shown in Figure 2. Figure 15 shown.

[0022] Figures 16 to 18 Figure 2 shows the tracking error of the position channel in a spiral trajectory tracking experiment. The results show that our proposed method achieves rapid error convergence in all three axes, with minimal overshoot, no noticeable oscillation, and minimal steady-state error. On the x-axis, the PID error reaches a maximum of 0.23m and exhibits significant oscillation. SMC is slightly better, but still exhibits fluctuations. Our proposed strategy converges quickly and remains stable. On the y-axis, our proposed method achieves virtually no overshoot and minimal jitter. Error control in the z-axis outperforms both SMC and PID, achieving a steady-state error of less than 0.05m. Overall, this demonstrates superior tracking accuracy and control smoothness. Figures 19 to 21 The figure represents the tracking error of the attitude channel in the spiral trajectory tracking experiment. The results show that traditional PID control exhibits large periodic oscillations and high error peaks in the error response of roll, pitch, and yaw angles, with the maximum error reaching approximately ±0.4 degrees. SMC can suppress the error amplitude to a certain extent, but certain periodic fluctuations still exist. The proposed scheme can stably control the attitude error within ±0.1 degrees throughout the entire process, effectively suppressing high-frequency oscillations and error spikes. A comparison with sliding mode control shows that the proposed scheme significantly reduces the system's steady-state error and transient jitter while maintaining dynamic response speed, effectively improving the system's robustness.

[0023] Figure 22 The position control inputs of the proposed strategy and the other two methods, i.e., the accelerations of the x, y, and z axes, are shown in the figure. For a given signal, the control inputs of the TA-SMRC controller in the three position channels reach their peak values ​​within 0.23s, which are 14.6 m / s. 2 , 23.8m / s 2 and 5.2m / s 2, and quickly returns to a steady state within 0.5 seconds. Compared to SMC and PID controllers, the TA-SMRC controller achieves faster response speeds, allowing the system acceleration command to quickly converge to a steady state. Due to the introduction of adaptive rate, the TA-SMRC controller maintains excellent tracking accuracy and dynamic performance under uncertainty and external disturbances, resulting in smoother control inputs, effectively suppressing high-frequency chattering, and improving the smoothness of the actuator's movement and system robustness.

[0024] Figure 23 The figure shows the attitude control inputs of the proposed strategy and the other two methods when simulating spiral trajectory tracking, namely the acceleration of the roll angle, pitch angle, and yaw angle. As can be seen from the figure: the control inputs of the GA-STSMC controller in the three attitude channels reach their peak values ​​within 0.21s, which are 0.68rad / s 2 , 0.55rad / s 2 , 0.37rad / s 2 Compared with SMC and PID algorithms, GA-STSMC achieves faster response and smaller overshoot, with extremely smooth control input and virtually no high-frequency chattering. Because this method relies on an adaptive algorithm to adaptively optimize the sliding mode gain parameters, the system maintains high-precision tracking and strong robustness under various disturbance conditions, effectively suppressing the chattering problem common in traditional sliding mode methods.

[0025] The technical means disclosed in the solutions of the present invention are not limited to those disclosed in the above-mentioned embodiments, but also include technical solutions composed of any combination of the above-mentioned technical features. It should be noted that those skilled in the art may make various improvements and modifications without departing from the principles of the present invention, which are also considered to be within the scope of protection of the present invention.

Claims

1. A hexarotor unmanned aerial vehicle position-attitude hierarchical composite adaptive sliding mode control method is used for the flight position and attitude stability control of the unmanned aerial vehicle. The core idea is to design the position loop and attitude loop hierarchically, design a position controller and an attitude controller respectively, and convert the output of the position controller into the input of the attitude controller through the attitude solution module, finally realizing the design of the unmanned aerial vehicle closed-loop system. The position loop adopts a terminal adaptive sliding mode robust controller (TA-SMRC) to achieve rapid convergence of the position channel; the attitude loop adopts a gain adaptive super spiral mode controller (GA-STSMC) to achieve finite time convergence and high-precision tracking of the attitude channel. Through the above-mentioned hierarchical composite control structure, the method of the present invention can effectively suppress external interference in complex environments, and improve the dynamic response speed, tracking accuracy and overall robustness of the hexarotor unmanned aerial vehicle.

2. The hexacopter position-attitude hierarchical composite adaptive sliding mode control method according to claim 1 is characterized in that: The method may include the following steps: Step 1: Establish a dynamic model for the three-dimensional spatial position (x-axis, y-axis, z-axis) and attitude (roll, pitch, yaw) of the hexacopter, assuming that it is a rigid body, that the lift is proportional to the square of the rotational speed, that air resistance is ignored, and that the mass is uniformly distributed. The position model differential equation is determined as follows: Where: P = [xyz] T represents the position matrix of the UAV, ν=[ν x ν y ν z ] T represents the velocity matrix of the drone, m represents the mass of the drone, g represents the acceleration of gravity, and e = [001] T The unit vector representing the direction of gravity, F = [F x F y F z ] T represents the force of the position channel, d v Indicates external interference; The differential equation for the posture model is determined as follows: where η = [φθψ] T represents the roll, pitch, and yaw matrices of the drone, ω = [pqr] T The angular velocity matrix of the roll angle, pitch angle and yaw angle of the drone, I = diag{I xx I yy I zz } represents the diagonal matrix of moment of inertia, L = diag{ll 1} represents the diagonal matrix of the distance from the center of mass of the drone to the center of mass of the motor, τ = [τ φ τ θ τ φ ] T represents the aerodynamic moment matrix of the UAV, d ω Represents the external interference on the attitude angle, R represents the three-axis transformation matrix between the angular velocity vector and the Euler angle vector; Step 2: By defining the position error, designing the terminal sliding mode surface and finite-time robust control law, and introducing an adaptive rate, online compensation for external disturbances and model uncertainties is performed. Finally, a terminal adaptive sliding mode robust controller is established in the position loop to achieve finite-time precise tracking of the UAV's three-axis position. Step 3: The desired acceleration and thrust signals output by the position loop are converted into desired pitch, roll, and yaw angle reference values ​​in real time through the attitude calculation module, completing the position-attitude calculation and command mapping. Step 4: To address the nonlinearity and external disturbances of the UAV attitude control, a super-helical sliding mode control law is designed. By introducing an adaptive law to dynamically adjust the sliding mode gain, high-frequency chattering is reduced. Finally, a gain-adaptive super-helical sliding mode attitude controller is established to achieve finite-time robust convergence of the attitude angle. Step 5: Based on the Lyapunov stability principle, Lyapunov functions are designed for position and attitude respectively to verify the stability of the designed system and its convergence within a finite time. Step 6: The obtained attitude torque signal is transformed into six sets of motor speed commands through dynamic inverse solution and combined with the hexacopter dynamics model. The motors are driven by PWM modulation to complete the position-attitude hierarchical composite adaptive sliding mode control of the hexacopter UAV and achieve precise control of the six-degree-of-freedom flight state. The motor allocation matrix is ​​expressed as: Where: c t represents the lift coefficient, c m Represents the counter-torque coefficient.

3. The position-attitude hierarchical composite adaptive sliding mode control method for a six-rotor UAV according to claim 2 is characterized in that: The terminal adaptive sliding mode robust controller described in step 2 has the following steps: Step 2.1: Define the error between the actual position of the drone and the expected position as: e P =P-P d , Where: P d =[x d y d z d ] T represents the desired position of the six-rotor drone, e P =[e Px e Py e Pz ] T Represents the position tracking error of the UAV; Step 2.2: Using the sign function to accelerate the convergence in finite time and effectively suppress the uncertainty of the system and the properties of external disturbances, define the following terminal sliding surface: Where: c1=diag(c 1x ,c 1y ,c 1z )>0,c2=diag(c 2x ,c 2y ,c 2z )>0 is a diagonal gain matrix, are the sign functions representing the position errors of the x, y, and z axes respectively; Step 2.3: To counteract external interference and accelerate the convergence of the sliding surface, the following robust control rate is designed: Where: a represents the diagonal sliding gain matrix, is the external disturbance d v estimated value of; Step 2.4: In order to achieve progressive tracking of external disturbances, the sign function of the sliding surface is introduced and the following adaptive law is designed to estimate the unknown disturbance in real time: Where: k>0 is the adaptive gain.

4. The position-attitude hierarchical composite adaptive sliding mode control method for a six-rotor UAV according to claim 2 is characterized in that: The workflow of position-attitude solution and command mapping described in step 3 includes the following steps: Step 3.1: In the z-axis direction, establish that the resultant force F and the z-axis thrust T satisfy the following relationship: Step 3.2: Calculate the modulus of the total thrust T: Step 3.3: Calculate the vertical balance equation of the drone when it moves in space: F z +mg=Tcosθcosφ, Step 3.4: The control quantity F of channel x and channel y obtained by the position controller x 、F y Converted into the expected signal φ of the attitude path d ,θ d :

5. The position-attitude hierarchical composite adaptive sliding mode control method for a six-rotor UAV according to claim 2, characterized in that: Step 4: Gain adaptive super-helical sliding mode controller, the workflow of which includes the following steps: Step 4.1: Define the error between the actual attitude angle and the target attitude angle; e η =th-th d , Where: η d =[φ d θ d ψ d ] T represents the target attitude angle of the six-rotor drone, e η =[e ηφ e ηθ e ηψ ] T Represents the attitude tracking error of the UAV; Step 4.2: Calculate the first-order derivative and second-order derivative of the defined error to obtain the attitude angular velocity error and attitude angular acceleration error, and make the following substitutions: Step 4.3: Based on the characteristics of super-helical sliding mode control, define the following sliding surface: Where: c3 represents the diagonal gain matrix; Step 4.4: After determining the sliding surface, take the derivative of the sliding surface function to obtain the relationship between the controlled parameter τ and the sliding derivative. Available control rate: Step 4.5: Establish the adaptive rate of the gain in the super-helical sliding mode control rate: Where: γ1 and γ2 represent the adaptation rate parameters.

6. The position-attitude hierarchical composite adaptive sliding mode control method for a six-rotor UAV according to claim 2 is characterized in that: Step 5: Verify the stability and finite time convergence of the closed-loop system. The workflow includes the following steps: Step 5.1: Construct the Lyapunov functions of the position loop and attitude loop respectively: in: represents the error estimate, k1 * 、k2 * is the ideal gain; Step 5.2: Calculate the time derivative According to the Lyapunov stability judgment principle: when V>0 and V(0)=0, Determine whether the closed-loop system is stable; Step 5.3: According to the finite-time convergence lemma, find n and β in the constructed Lyapunov function to satisfy: Step 5.4: Calculate the convergence time of the system through step 5.3 to ensure that the designed system can converge to a stable state within a limited time.

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