Flapping-wing air vehicle cascade control switching method based on disturbance quantization and PID (Proportion Integration Differentiation) architecture

Abstract

The invention discloses an ornithopter cascade control switching method based on disturbance quantization and PID architecture, and belongs to the technical field of ornithopter control. According to the invention, a three-section foldable bionic flapping-wing aircraft is selected as a controlled object, classic PID control is used as an outer-ring main loop, and PID control, nonlinear PID control and sliding-mode control are used as a cascade control framework of an inner-ring auxiliary loop; a basis is provided for calculation and disturbance compensation through an extended state observer ESO, and based on a calculation result, a self-adaptive switching logic weight coefficient is designed, a master control instruction is generated, and construction of the method is completed. According to the method, low, medium and high disturbance intervals are covered through three sub-strategies, the calculation efficiency is guaranteed by PID-PID under low disturbance, the balance of precision and smoothness is realized by PID-NLPID under medium disturbance, and extreme disturbance is inhibited by PID-SMC under high disturbance; the RMSE of trajectory tracking under high disturbance is reduced by 5%-8% compared with pure PID, and the recovery time is shortened to be within 5 s; and under low disturbance, the control input fluctuation amplitude is reduced by more than 60% compared with pure SMC, and buffeting loss of an executing mechanism is avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of flapping wing aircraft control, and in particular to a flapping wing aircraft cascade control switching method based on disturbance quantification and PID architecture. BACKGROUND

[0002] Bionic flapping wing aircraft has wide application potential in the fields of military reconnaissance and environmental monitoring due to its low aerodynamic noise, flexible maneuvering and high energy efficiency. However, its precise control still faces three major challenges: first, strong nonlinearity and multi-channel coupling problem, the unsteady aerodynamic force under low Reynolds number conditions leads to the coupling of attitude and position control, making traditional linear control methods unsuitable; second, difficulty in suppressing complex aerodynamic disturbances, time-varying disturbances such as wind speed changes are easy to cause instability, and existing control strategies have limited disturbance rejection capability; third, it is difficult to balance performance and engineering feasibility, single control strategies such as PID have insufficient robustness, sliding mode control has chattering, and nonlinear PID parameter tuning is complex, making it difficult to cover all disturbance conditions.

[0003] Although existing technologies attempt to combine PID and sliding mode control or introduce ESO disturbance observation, there are still the following bottlenecks: lack of objective quantification standard for disturbances, relying on empirical threshold, unable to achieve accurate disturbance evaluation; control strategy coverage is limited, it is difficult to adapt to the full range from low disturbance to high disturbance, which may cause resource waste or control failure; strategy switching in engineering implementation is easy to produce control jump, parameter debugging is complex, and there is a lack of clear coordination logic between modules, affecting system real-time performance. Therefore, the existing technology still has obvious deficiencies in full disturbance adaptation, disturbance quantification standardization, module coordination and engineering implementability. SUMMARY

[0004] To solve the above technical problems, the present application provides a flapping wing aircraft cascade control switching method based on disturbance quantification and PID architecture.

[0005] To achieve the above technical scheme, the steps include: S1, selecting a three-section foldable bionic flapping wing aircraft as the controlled object.

[0006] S2, based on the controlled object, a cascade control framework is constructed with classic PID control as the outer loop main circuit and PID control, nonlinear PID (NLPID) control and sliding mode control as the inner loop secondary circuit; The control equation expression of the outer loop PID main circuit is as follows: In the formula, represents the desired roll angle; represents the desired pitch angle; represents the desired yaw angle; represents The direction position error is expressed as: , The direction position error is expressed as: The direction position error is expressed as: , The direction position error is expressed as: The direction position error is expressed as: wherein, , and represent the reference position, represent the actual direction; , and respectively represent the position loop proportional gain in the direction, set ; , and respectively represent the position loop integral gain in the direction, set ; , and , set ; In the present application, for the outer loop control part, the parameters of the channels in three directions are adjusted, and the parameters are as follows: , , ; , , ; , , ; The attitude loop control expression of the inner loop PID control is as follows: In the formula, represents the roll channel control moment; represents the pitch channel control moment; represents the yaw channel control moment; represents the roll angle error, expressed as , represents the pitch angle error, expressed as: ; represents the yaw angle error, expressed as: wherein, , and respectively represent the expected roll angle, the expected pitch angle and the expected yaw angle,​ represents the roll angle, represents the pitch angle, represents the yaw angle; , and represent the roll angle, pitch angle and yaw angle attitude loop proportional gain, respectively; , and represent the roll angle, pitch angle and yaw angle attitude loop integral gain, respectively; , and represent the roll angle, pitch angle and yaw angle attitude loop derivative gain, respectively; a multi-channel coupling compensation term is introduced, represented as: wherein, represents the original desired roll angle, 0.3 and 0.2 are the coupling coefficients set by the laboratory, and the coupling compensation y / z position error in the direction interferes with x channel; In the present application, the inner loop PID control parameters are set as follows: x and z direction channels are set with the same control parameters, and the control parameters are: x and z The parameter range of the direction channel is: , , , , , ; y The control parameters of the direction channel are: y the attitude loop proportional gain of the direction channel , y the attitude loop integral gain of the direction channel , y the attitude loop derivative gain of the direction channel , set , and ; The design expression of the inner loop nonlinear PID (NLPID) control includes: The inner channel (pitch) uses the control law of the inner loop PID control loop to ensure stability; According to the inner channel (roll) control law, a nonlinear control torque is obtained, and the expression is as follows: In the formula, represents the roll angle channel nonlinear control torque;​ This indicates the nonlinear roll angle proportional gain; Indicates roll angle error; This represents the nonlinear roll angle integral gain; This represents the nonlinear saturation term of the integral term; Indicates the parameters of the saturation function; This represents the linear roll angle differential gain; Indicates the nonlinear correction coefficient; The nonlinear control torque is obtained based on the inner channel (yaw) control law: In the formula, This indicates the nonlinear control torque of the yaw angle channel; This represents the nonlinear yaw angle proportional gain; Indicates yaw angle error; This represents the nonlinear yaw angle integral gain; This represents the nonlinear saturation term of the integral term; Indicates the parameters of the saturation function; This represents the nonlinear yaw angle differential gain; Indicates the nonlinear correction coefficient; The nonlinear control torque is obtained by introducing the ESO disturbance compensation term: In the formula, This represents the basic moment of roll angle calculated by nonlinear PID control. This represents the aerodynamic disturbance of the roll angle estimated by ESO; The nonlinear control torque of the yaw angle channel can be derived as follows: In the formula, This represents the basic moment of yaw angle calculated by nonlinear PID control. This represents the yaw angle aerodynamic disturbance estimated by ESO; In this invention, a nonlinear correction coefficient is set. exist x and z The value is 0.8 in the directional channel; a nonlinear correction coefficient is set. exist y Directional channel The gain is 0.75; the nonlinear proportional gain of the roll and yaw angle channels in the inner-loop nonlinear PID (NLPID) control is nonlinear. Both are set to 0.75, with nonlinear integral gain for roll and yaw channels. Both are set to 4, with nonlinear differential gain for roll and yaw angle channels. All are taken as 0.75; In the inner channel (pitch) of the inner-loop nonlinear PID (NLPID) control, the differential term gain of the inner-loop control is... It is not related to the inner loop PID. The values ​​are exactly the same, but they are based on cascaded PIDs. Using this as a basic reference value, and based on the strength of the aerodynamic nonlinearity of the three channels, the differential term gain is adjusted or corrected accordingly, so that the differential action is... y In scenarios with significant channel aerodynamic nonlinearity, it can more effectively suppress disturbances, avoid differential saturation or high-frequency noise amplification, and ultimately enhance the control stability of the three channels; The inner-loop SMC control design includes: The synovial surface is defined as: This accelerates error convergence; among them, Represents the sliding surface function of the roll channel; Indicates roll angle error; Indicates the parameters of the synovial surface; The exponential law of convergence is defined as: ,in, Indicates the rate of change of the sliding surface; Represents the law of convergence; This represents the coefficient of the convergence law exponent. The expression for the control torque obtained from the control law is as follows: In the formula, Indicates the roll channel control torque; Indicates the moment of inertia of the roll axis; Indicates the desired roll rate; Indicates the parameters of the synovial surface; Indicates roll angle error; Indicates the reaching law switching gain; Represents the sliding surface function of the roll channel; Indicates the boundary layer thickness; This represents the coefficient of the convergence law exponent. Represents the angular velocity vector; Represents the moment of inertia matrix; Represents the angular velocity vector matrix; Indicates the estimated disturbance value; Energy Coupling Correction: To ensure multi-channel energy coupling correction, considering flapping wing energy consumption and control parameters, a dynamic weighting coefficient is introduced, and the control torque is corrected as follows: ， ,in, Indicates the SMC basic control torque. Dynamic weight coefficient; flapping frequency ∈ ; In the application, the inner ring SMC control parameter is set as follows: x and z The same control parameters are set for the channel, and the sliding mode surface parameters , the reaching law , the reaching law index term coefficient , the boundary layer thickness , and the chattering is suppressed; The control parameters of the y channel, the sliding mode surface parameters , the reaching law , the reaching law index term coefficient , the boundary layer 2.

[0007] S3, an extended state observer ESO is constructed to estimate the controlled object in real time, for providing the basis for disturbance compensation calculation; The expression of the extended state observer ESO is as follows: In the formula, represents the estimated value of the included angle ; represents the estimated value of the included angle rate of change ; represents the estimated value of the aerodynamic force disturbance ; 、 and respectively represent the first observer gain, the second observer gain and the third observer gain; represents the sensor feedback signal; represents the control matrix; represents the control input; The ESO disturbance observer is designed based on the modified Kirchhoff equation to estimate the angle of attack , the angle of attack rate of change and the aerodynamic force disturbance in real time, for providing the basis for disturbance compensation calculation; The expression of the estimated aerodynamic force disturbance is as follows: ; The ESO observer gain is 100, 2000 and 10000, which is derived based on the modified Kirchhoff equation, and ensures that the estimation error of the aerodynamic force disturbance is ≤5%, and the value calculation accuracy is ≤3%. definition The physical meaning of is the ratio of airflow disturbance energy to the rated aerodynamic power of the flapping wing, based on the ESO output. Combined with control cycle With rated flapping wing aerodynamic power Through formula calculate The values ​​are then used to divide the disturbance into three intervals: low disturbance interval ∈ [1,3] (wind speed < 1m / s), medium disturbance interval ∈ [4,12] (wind speed 1-3m / s), and high disturbance interval ∈ [13,24] (wind speed > 3m / s); To achieve closed-loop control, each step is verified through graphical correlation to ensure consistency between theoretical logic and experimental operation. The specific operation is as follows: Initialize the parameters of each module: load the tuning parameters of the outer loop PID, inner loop sub-strategy, and ESO, set the control cycle to Ts=0.02s, and start the hardware self-test of the sensor, control module, and actuator; Hardware startup: Sequentially activate the sensor modules (MPU6050, VL53L0X), microcontroller (load outer loop PID and inner loop sub-strategy programs), and actuators (brushless motor pre-starts to 15Hz flapping frequency, servo motor returns to center). (torsion angle) Initial state configuration: Set the initial attitude (roll) , up and down ,yaw 1. Initial position (x=0m, y=0m, z=0m), reference trajectory set to figure eight (x=1.0sin(0.5), y=0.5sin(t)), (z=5sin(0.5t)), start cascade closed loop, that is, the outer loop PID runs first, and the inner loop activates the PID sub-strategy by default; Disturbance injection and Real-time value quantization: The system simulates three typical disturbance scenarios in actual flight and quantifies their intensity in real time using a disturbance estimation module, providing an objective basis for switching strategies. Set a low-disturbance scenario corresponding to a steady airflow with a wind speed of 0.5 m / s: sensor set to 50H Z Attitude and position signals are acquired, transmitted to the ESO processing unit, substituted into the ESO state equation, and calculated. ≈2; The disturbance scenario is a periodic gust of wind with a speed of 2 m / s and a period of 5 seconds: the disturbance injection device outputs airflow disturbances according to a preset period, and the ESO tracks the disturbance changes in real time. It exhibits periodic fluctuations, as calculated. ≈7; High disturbance scenario is chaotic airflow with wind speed 4 m / s, fluctuation amplitude ±1 m / s: disturbance injection device generates random airflow, ESO quickly responds to sudden disturbance, Wave is violent, calculated ≈18; Value transmission: send value to policy switching module every 0.02 s to ensure that switching judgment is synchronized with disturbance change.

[0008] S4, based on calculation results, design adaptive switching logic weight coefficient; Introduce dynamic weight coefficient 、 、 Corresponding to PID, nonlinear PID (NLPID), SMC sub-strategy, meet ; The weight coefficient calculation expression of inner loop PID is: ; The weight coefficient calculation expression of nonlinear PID (NLPID) is: ; The weight coefficient calculation expression of SMC is: - .

[0009] S5, based on adaptive switching logic weight coefficient, generate total control instruction; The expression of total control instruction is as follows: In the formula, represent the control output of outer loop inner loop ; represent the control output of outer loop inner loop ; represent the control output of outer loop inner loop ; 、 and respectively represent the weight coefficients corresponding to PID, nonlinear PID (NLPID) and SMC sub-strategy; Put into the flapping wing aircraft actuator, combine the attitude and position signals fed back by the sensor for real-time iteration, realize attitude stabilization and trajectory tracking under different disturbance scenarios; To ensure the stability in the switching process, the system stability is monitored in real time by Lyapunov function, and the expression is as follows: In the formula, The total Lyapunov function of the whole cascade system is represented; The Lyapunov function of the outer loop position tracking error is represented; Two items are analyzed by inner product operation to analyze the motion state, wherein, The error state vector of the attitude loop is represented, The sliding mode control (SMC) sliding mode surface vector is represented, and the superscript The transpose is represented; It is stipulated that If an unstable trend occurs, temporarily increase the current sub-strategy weight by 10%, until the system recovers to stable.

[0010] Advantages of the present application The present application quantifies the disturbance intensity by Three sub-strategies cover low, medium and high disturbance intervals, solving the limitations of a single strategy. PID-PID ensures calculation efficiency under low disturbance, PID-NLPID realizes accuracy and smoothness balance under medium disturbance, and PID-SMC suppresses extreme disturbance under high disturbance; The present application combines ESO real-time disturbance estimation and strong robustness of sliding mode control, and the trajectory tracking RMSE under high disturbance is reduced by 5%-8% compared with pure PID, and the recovery time is shortened to within 5s; the control input fluctuation amplitude under low disturbance is reduced by more than 60% compared with pure SMC, avoiding the loss of actuator chattering; The outer loop PID parameters of the present application are fixed, and only the inner loop sub-strategy parameters need to be adjusted, reducing the parameter adjustment complexity; the control framework is compatible with MWORKS.Sysplorer physical modeling and MATLAB / Simulink simulation, and can be directly connected to the motor drive and sensor system of the actual flapping wing aircraft; The present application verifies the asymptotic stability of the switching point by Lyapunov function, realizes strategy disturbance-free transition by dynamic weight coefficient, and controls the overshoot to be less than 5% to avoid attitude fluctuation in the switching process. BRIEF DESCRIPTION OF DRAWINGS

[0011] Figure 1 The present application is an eight-shaped flight control closed-loop structure diagram; Figure 2 The present application is a flapping wing aircraft aerodynamic model diagram; Figure 3 The present application is an adaptive cascade control strategy switching logic schematic diagram; Figure 4This is a comparison chart of the dynamic position errors of different control algorithms of the present invention; wherein, Figure 4 (a) represents the position error in the x-direction; Figure 4 (b) represents the position error in the y-direction; Figure 4 (c) represents the position error in the z-direction; Figure 4 (d) represents the total position error; Figure 5 This is a diagram illustrating the control input characteristics of the present invention. Figure 6 This is the phase space and phase trajectory diagram for access disturbance quantization according to the present invention; wherein, Figure 6 (a) represents the three-dimensional phase space when K*=1; Figure 6 (b) shows the phase trajectory diagram when K*=1; Figure 6 (c) represents the three-dimensional phase space when K*=10; Figure 6 (d) shows the phase trajectory diagram when K*=10; Figure 6 (e) represents the three-dimensional phase space when K*=20; Figure 6 (f) represents the phase trajectory diagram when K*=20. Detailed Implementation

[0012] The present invention will be further described in detail below with reference to specific embodiments.

[0013] See 1 to Figure 6 A cascaded control switching method for flapping-wing aircraft based on disturbance quantization and PID architecture includes the following steps: S1. Select a three-section foldable bionic flapping-wing aircraft as the controlled object; like Figure 1 and Figure 2 As shown, a six-degree-of-freedom nonlinear dynamic model of a three-segment foldable biomimetic flapping-wing aircraft is used to describe the mathematical characteristics of the controlled object, and the expression is as follows: In the formula, Indicates the overall weight of the machine; Represents a three-dimensional acceleration vector. ,in, express Rate of change of velocity in the direction, express Rate of change of velocity in the direction, express Rate of change of velocity in the direction; Represents the gravity vector. , Represents gravity. Indicates the transpose operation; Represents the angular velocity vector; denotes the flight velocity; denotes the Coriolis term; denotes the total aerodynamic force, which is calculated based on the modified Kirchhoff equation, expressed as: where, denotes the air density; denotes the wing area; , and denote the aerodynamic coefficients in the directions of , and respectively; The rotational motion model of the three-segment foldable biomimetic flapping-wing aircraft is used to describe the dynamic relationship between the attitude angular velocity and the moment, expressed as: where, denotes the moment of inertia matrix, where, denotes the moments of inertia in the directions of , and respectively; denotes the angular acceleration vector; denotes the centrifugal inertia term; denotes the collective disturbance moment; denotes the aerodynamic moment, expressed as: where, denotes the average chord length; , and denote the moment coefficients in the directions of roll, pitch, and yaw respectively; The attitude kinematics model of the three-segment foldable biomimetic flapping-wing aircraft is used to describe the conversion relationship between the attitude angle and the angular velocity, expressed as: where, denotes the rate of change of Euler angles; denotes the Euler angle vector, where, denotes the roll angle, i.e., the angle of rotation around the axis, denotes the pitch angle, i.e., the angle of rotation around the axis, denotes the yaw angle, i.e., the angle of rotation around the z axis; denotes the transformation matrix, i.e., when the pitch angle is denoted as: ; In this embodiment, the setting of the basic parameters includes: the total mass of the machine = 5.6 g, the wing span R = 53 mm, the average chord length = 50 mm, the wing area S = 2650 , the three-axis rotational inertia around , and directions is represented as = 575 g· , = 576 g· and = 991 g· ; the setting of the aerodynamic parameters includes: the flapping frequency is fixed as the flapping frequency , the maximum left-right torsion angle of the wing = = 30° (according to the kinematics equation of the wing = wherein, represents the correction amount of the torsion angle, represents the instantaneous time), the maximum left-right flapping angle , the air density = 1.225 kg / m³ (standard atmospheric conditions, used in the lift formula wherein, represents the lift coefficient; the thrust formula is calculated, wherein, represents the thrust coefficient), the flight attack angle = 10°; The sensors selected are commercial mature hardware, which ensures the real-time nature of signal transmission and control accuracy, including: Attitude sensor: MPU6050 six-axis gyroscope is adopted, the sampling frequency is 50 Hz, and the attitude angle measurement accuracy is ±0.1°; Position sensor: VL53L0X laser ranging sensor is adopted, the ranging range is 0-2 m, and the accuracy is ±1 mm; The specific layout position of the sensor is: the attitude sensor and the attitude sensor are fixed on the middle axis in the abdomen of the fuselage; the attitude sensing unit (MPU6050) is rigidly fixed through a silica gel damping pad, which ensures accurate alignment with the machine coordinate system and real-time output of the roll angle, pitch angle, yaw angle and angular velocity, supports the inner ring sub-strategy and disturbance estimation; the position sensing unit (VL53L0X) is installed in front of the attitude sensing unit, measures a 5° forward inclination to avoid the wing obstruction, collects three-dimensional coordinates, supports the outer ring position control, and meets the control requirements; ​Data transmission: serial communication (baud rate 115200bps) is adopted to ensure that sensor data is synchronously transmitted to the control module, with a delay of ≤10ms; An STM32H743 microcontroller (clock frequency 480MHz, floating point operation performance ≥1GFLOPS) is selected to realize real-time operation of outer loop PID, three kinds of inner loop sub-strategies (PID, NLPID, SMC) and ESO disturbance estimation, and meet the real-time requirements of K* value calculation and weight adjustment; For the motor part (actuator): Wing drive: 2204 brushless DC motor (rated speed 3000rpm, output torque ≥0.015N m); Torsion angle adjustment: SG90 servo (angle range -30°~30°, response time ≤0.1s, used for instantaneous torsion angle correction of the wing, to realize real-time adjustment of the correction amount of the flapping angle); Overload protection: built-in current sensor in the actuator (measurement range 0-3A, accuracy ±5mA), overload feedback is triggered when the current is >1.5A (rated current 1.2A), to avoid structural damage caused by overloading of the wing aerodynamic moment; The present application takes disturbance intensity quantification parameter as the core trigger condition, takes PID-PID, PID-NLPID and PID-sliding mode (PID-SMC) three fusion strategies as switchable modules, realizes smooth switching of the strategies through real-time disturbance estimation, and the core logic is as shown in Figure 3 .

[0014] S2, based on the controlled object, a cascade control framework is constructed with classic PID control as the outer loop main circuit and PID control, nonlinear PID (NLPID) control and sliding mode control as the inner loop sub-circuit; The control equation expression of the outer loop PID main circuit is as follows: In the formula, represents the expected roll angle; represents the expected pitch angle; represents the expected yaw angle; represents the direction position error, which is expressed as: , represents the direction position error, which is expressed as: , represents the direction position error, which is expressed as: wherein, , and represents the reference position, represents the actual direction; , and respectively represent the position loop proportional gain in the direction, set ; , and respectively represent the position loop integral gain in the direction, set ; , and respectively represent the position loop differential gain in the direction, set ; In the present application, for the outer loop control part, the parameters of the channels in three directions are regulated, and the parameters are as follows: , , ; , , ; , , ; The expression of the inner loop PID control attitude loop control is as follows: In the formula, represents the roll channel control moment; represents the pitch channel control moment; represents the yaw channel control moment; represents the roll angle error, expressed as , represents the pitch angle error, expressed as: ; represents the yaw angle error, expressed as: wherein, , and respectively represent the expected roll angle, the expected pitch angle and the expected yaw angle, represents the roll angle, represents the pitch angle, represents the yaw angle; , and respectively represent the roll angle, the pitch angle and the yaw angle attitude loop proportional gain; , and ​These represent the attitude loop integral gains for roll angle, pitch angle, and yaw angle, respectively. , and These represent the attitude loop differential gains for roll, pitch, and yaw angles, respectively; a multi-channel coupling compensation term is introduced, expressed as: ,in, This represents the original desired roll angle, with 0.3 and 0.2 being the laboratory-tuned coupling coefficients for compensation. y / z Position error in direction x Channel interference; In this invention, the inner loop PID control parameters are set as follows: x and z The directional channels are tuned with the same control parameters, which are: set up x and z The parameter range for the directional channel is: , , , , , ; y The control parameters for the directional channel are: y Attitude loop proportional gain on the directional channel , y Attitude loop integral gain on the directional channel , y Attitude-to-differential gain on the directional channel ,set up , and ; The design expression for inner-loop nonlinear PID (NLPID) control includes: The inner channel (pitch) uses the control law of the inner loop PID control loop to ensure stability; The nonlinear control torque is obtained based on the inner channel (roll) control law, and its expression is as follows: In the formula, This indicates the nonlinear control torque of the roll angle channel; This indicates the nonlinear roll angle proportional gain; Indicates roll angle error; This represents the nonlinear roll angle integral gain; This represents the nonlinear saturation term of the integral term; Indicates the parameters of the saturation function; This represents the linear roll angle differential gain; Indicates the nonlinear correction coefficient; The nonlinear control torque is obtained based on the inner channel (yaw) control law: In the formula, This indicates the nonlinear control torque of the yaw angle channel; This represents the nonlinear yaw angle proportional gain; Indicates yaw angle error; This represents the nonlinear yaw angle integral gain; This represents the nonlinear saturation term of the integral term; Indicates the parameters of the saturation function; This represents the nonlinear yaw angle differential gain; Indicates the nonlinear correction coefficient; The nonlinear control torque is obtained by introducing the ESO disturbance compensation term: In the formula, This represents the basic moment of roll angle calculated by nonlinear PID control. This represents the aerodynamic disturbance of the roll angle estimated by ESO; In the formula, This represents the basic moment of yaw angle calculated by nonlinear PID control. This represents the yaw angle aerodynamic disturbance estimated by ESO; In this invention, a nonlinear correction coefficient is set. exist x and z The value is 0.8 in the directional channel; a nonlinear correction coefficient is set. exist y Directional channel The gain is 0.75; the nonlinear proportional gain of the roll and yaw angle channels in the inner-loop nonlinear PID (NLPID) control is nonlinear. Both are set to 0.75, with nonlinear integral gain for roll and yaw channels. Both are set to 4, with nonlinear differential gain for roll and yaw angle channels. All are taken as 0.75; In the inner channel (pitch) of the inner-loop nonlinear PID (NLPID) control, the differential term gain of the inner-loop control is... It is not related to the inner loop PID. The values ​​are exactly the same, but they are based on cascaded PIDs. Using this as a basic reference value, and based on the strength of the aerodynamic nonlinearity of the three channels, the differential term gain is adjusted or corrected accordingly, so that the differential action is... yIn the scenario of significant aerodynamic nonlinearities of the channels, the disturbance is suppressed more effectively, the differential saturation or high-frequency noise amplification is avoided, and finally the control stability of the three channels is strengthened; The inner-loop SMC control design includes: The sliding film surface is defined as: The acceleration error converges; wherein, The roll channel sliding mode surface function is represented as: The roll angle error is represented as: The sliding film surface parameter is represented as: The exponential approach law is defined as: Wherein, The sliding mode surface change rate is represented as: The approach law is represented as: The approach law exponential term coefficient is represented as: The control torque expression obtained by the control law is as follows: In the formula, The roll channel control torque is represented as: The roll axis moment of inertia is represented as: The desired roll angular velocity is represented as: The sliding film surface parameter is represented as: The roll angle error is represented as: The approach law is represented as: The roll channel sliding mode surface function is represented as: The boundary layer thickness is represented as: The approach law exponential term coefficient is represented as: The angular velocity vector is represented as: The moment of inertia matrix is represented as: The angular velocity vector matrix is represented as: The disturbance estimation value is represented as: The hyperbolic tangent function is represented as: Energy coupling correction: in order to ensure the energy coupling correction of multiple channels, considering the energy consumption and control quantity of the flapping wing, a dynamic weight coefficient is introduced, and the control torque correction is: , Wherein, The SMC basic control torque is represented as: The dynamic weight coefficient is represented as: flapping frequency ∈ ; In the present application, the inner-loop SMC control parameter is set as follows: x And z The control parameters of the channels are set the same, and the sliding film surface parameters , the approach law , the approach law exponential term coefficient Boundary layer thickness Chattering suppression y Channel control parameters, membrane surface parameters Approach law Approach law index term coefficient Boundary layer 2 All inner loop control parameters are summarized in Table 1. Table 1: Switchable inner loop control parameters PID-NLPID Nonlinear Correction: Introduce the differential link of the roll and yaw channel ( is the correction coefficient, is the difference between the expected attitude angle and the actual attitude angle), and the differential suppression ability is enhanced when the error increases, adapting to the nonlinear characteristics under disturbance; PID-SMC Chattering Suppression: Replace the traditional sgn function with a tanh boundary layer, with a boundary layer thickness , combined with energy coupling correction , to balance the anti-disturbance and actuator fatigue loss.

[0015] S3, Construct an extended state observer ESO to estimate the controlled object in real time, to provide the basis for calculation and disturbance compensation; The expression of the extended state observer ESO is as follows: In the formula, represents the estimated value of the common angle ; represents the estimated value of the common angle rate of change ; represents the estimated value of the aerodynamic disturbance ; , and respectively represent the first observer gain, the second observer gain and the third observer gain; represents the sensor feedback signal; represents the control matrix; represents the control input; The ESO disturbance observer is designed based on the modified Kirchhoff equation to estimate the angle of attack , the rate of change of the angle of attack and the aerodynamic disturbance in real time, to provide the basis for calculation and disturbance compensation; The expression for estimating the aerodynamic disturbance is as follows: ; The ESO observer gain is set to 100, 2000, and 10000, based on the modified Kirchhoff equation, to ensure aerodynamic disturbances. The estimation error is ≤5%. Value calculation accuracy ≤3%; definition The physical meaning of is the ratio of airflow disturbance energy to the rated aerodynamic power of the flapping wing, based on the ESO output. Combined with control cycle With rated flapping wing aerodynamic power Through formula calculate The values ​​are then used to divide the disturbance into three intervals: low disturbance interval ∈ [1,3] (wind speed < 1m / s), medium disturbance interval ∈ [4,12] (wind speed 1-3m / s), and high disturbance interval ∈ [13,24] (wind speed > 3m / s); To achieve closed-loop control, each step is verified through graphical correlation to ensure consistency between theoretical logic and experimental operation. The specific operation is as follows: Initialize the parameters of each module: load the tuning parameters of the outer loop PID, inner loop sub-strategy, and ESO, set the control period to Ts=0.02s, and start the hardware self-test of the sensor, control module, and actuator; Hardware startup: Sequentially activate the sensor modules (MPU6050, VL53L0X), microcontroller (load outer loop PID and inner loop sub-strategy programs), and actuators (brushless motor pre-starts to 15Hz flapping frequency, servo motor returns to center). (torsion angle) Initial state configuration: Set the initial attitude (roll) , up and down ,yaw 1. Initial position (x=0m, y=0m, z=0m), reference trajectory set to figure eight (x=1.0sin(0.5), y=0.5sin(t)), (z=5sin(0.5t)), start cascade closed loop, that is, the outer loop PID runs first, and the inner loop activates the PID sub-strategy by default; Disturbance injection and Real-time value quantization: The system simulates three typical disturbance scenarios in actual flight and quantifies their intensity in real time using a disturbance estimation module, providing an objective basis for switching strategies. Set a low-disturbance scenario corresponding to a steady airflow with a wind speed of 0.5 m / s: sensor set to 50H Z Attitude and position signals are acquired, transmitted to the ESO processing unit, substituted into the ESO state equation, and calculated. ≈2; The medium disturbance scenario is a wind speed of 2 m / s periodic gust with a period of 5 s: the disturbance injection device outputs airflow disturbance at a preset period, and the ESO tracks the disturbance change in real time, periodic fluctuations, and the calculation result is ≈7; The high disturbance scenario is a wind speed of 4 m / s chaotic airflow with a fluctuation amplitude of ±1 m / s: the disturbance injection device generates random airflow, and the ESO quickly responds to sudden disturbance, fluctuations are severe, and the calculation result is ≈18; Value transmission: every 0.02 s, the value is sent to the strategy switching module to ensure that the switching judgment is synchronized with the disturbance change.

[0016] S4, based on the calculation result, the adaptive switching logic weight coefficient is designed; The dynamic weight coefficient , , corresponds to the PID, nonlinear PID (NLPID), and SMC sub-strategies respectively, and satisfies ; The weight coefficient calculation expression of the inner loop PID is: ; The weight coefficient calculation expression of the nonlinear PID (NLPID) is: ; The weight coefficient calculation expression of the SMC is: - .

[0017] S5, based on the adaptive switching logic weight coefficient, the total control instruction is generated; The expression of the total control instruction is as follows: In the formula, denotes the control output of the outer loop inner loop ; denotes the control output of the outer loop inner loop ; denotes the control output of the outer loop inner loop ; , and denote the weight coefficients corresponding to the PID, nonlinear PID (NLPID), and SMC sub-strategies respectively; The The input flapping-wing aircraft actuator combines the real-time iteration of the attitude and position signals fed back by the sensor to realize the attitude stability and trajectory tracking under different disturbance scenarios. In order to ensure the stability during the switching process, the system stability is monitored in real time by Lyapunov function, and the expression is as follows: In the formula, represents the total Lyapunov function of the whole cascade system; represents the Lyapunov function of the outer loop position tracking error; The two terms are analyzed by inner product operation to analyze the motion state, wherein, represents the error state vector of the attitude loop, represents the sliding mode control (SMC) sliding mode surface vector, and the superscript represents the transpose; It is stipulated that If an unstable trend occurs, temporarily increase the current sub-strategy weight by 10% until the system recovers to stable.

[0018] Perform feedback and cascade closed-loop iteration: Actuator drive: total control command Convert to PWM signal (frequency 50HZ), drive brushless motor to maintain 15HZ flapping frequency, and adjust the wing twist angle (correction , ) according to the command of the rudder to realize attitude and position adjustment; State feedback: the sensor collects the adjusted attitude and position signals in real time, and feeds back to the outer loop PID to calculate the position error 、 , and update the disturbance estimation module and k value, form the cascade closed loop of outer loop adjustment, inner loop switching and execution feedback; Overload protection linkage: the actuator current sensor feeds back the load current in real time, if the current >1.5A, immediately send overload signal to the strategy switching module, temporarily reduce the current sub-strategy control amount by 10%, while preferentially maintaining the stability of the outer loop PID, to avoid the collapse of the cascade architecture due to local overload.

[0019] After deep processing based on actual test data, the statistical characteristics of the three control methods are obtained, as shown in Table 2. In terms of root mean square error (RMSE), different control strategies have different numerical values in different axial directions. From the table, the steady-state accuracy, disturbance rejection ability, and recovery speed of different control strategies under pulse, step, and sinusoidal disturbances can be seen: PID-SMC has the best disturbance rejection ability (0.9, 0.95) and recovery speed (0.95, 0.7) under pulse and step disturbances, but the control smoothness (0.4, 0.45) is the worst due to the sliding mode chattering problem, which reflects its strong inhibition ability to transient disturbances and inherent characteristics; the steady-state accuracy (0.9) of PID-NLPID is the best under various disturbances, and the disturbance rejection and parameter robustness (0.8) are more balanced, which shows that the nonlinear correction of the inner loop effectively improves the steady-state performance and generalization; the control smoothness (0.7-0.85) of PID-PID is the best, but the disturbance rejection significantly decays with periodicity (0.45 under sinusoidal disturbance), which reflects the limitations of the pure linear cascade structure in adapting to complex nonlinear and periodic disturbances.

[0020] Table 2 Performance indicators of different control strategies In combination Figure 4 with Figure 4 (a), Figure 4 (b), Figure 4 (c), and Figure 4 (d), the dynamic position error comparison, the position error (RMSE) test results under three disturbance scenarios are as follows: Low disturbance (k≈2, PID-PID dominant): x / y / z direction RMSE is 0.08m, 0.06m, 0.10m respectively, the adjustment time is 3.2s, and the error converges to ±2% of the steady-state value, compared with pure PID, the tracking accuracy is improved by 8%, and there is no overshoot; Medium disturbance (k≈7, PID-NLPID dominant): x direction RMSE reduces to 0.068m, y / z direction error fluctuation amplitude ≤0.05m, which adapts to the disturbance characteristics of periodic gust, and the steady-state accuracy is 0.9; High disturbance (k≈18, PID-SMC dominant): z direction RMSE is 0.078m, the maximum attitude deviation under disturbance reduces from to , the recovery time is 4.5s, which is shortened by 35% compared with PID-PID, and the disturbance rejection is 0.95.

[0021] Control smoothness: combined with the control input characteristics analysis of Figure 4 , the test control input amplitude and chattering are as follows: Low-disturbance switching: When PID-PID is dominant, the control input amplitude is ≤0.6V, and the fluctuation amplitude is reduced by 60% compared with PID-SMC. The actuator current is stable at 0.8-1.0A, with no obvious mechanical vibration and smooth control effect. High-disturbance switching: When PID-SMC is dominant, chattering is suppressed by the tanh boundary layer, and the input chattering amplitude is controlled to be ≤0.4V; During the transition phase: when switching from PID-PID to PID-NLPID, the rate of change of the control quantity is ≤0.1V / s, with no obvious impact, indicating that the control can still be smooth during the switching strategy, verifying the disturbance-free effect of dynamic weight fusion.

[0022] Combination Figure 5 middle Figure 6 (a) Figure 6 (b) Figure 6 (c) Figure 6 (d) Figure 6 (e) Figure 6 (f) (Phase space and phase trajectory under different K* values), the system stability characteristics in actual testing are as follows: Low disturbance (K*=1): The system phase space is a regular compact sphere and the phase trajectory is a dense ellipse, indicating that the state is stable and there is no attitude shift after 1 hour of continuous operation; Medium perturbation (K*=10): Phase points are locally dispersed but not chaotic; the density of phase trajectory ellipses decreases slightly; the system still maintains stable attitude error ≤ ; High disturbance (K*=20): Although the phase points are extended, the overall structure is regular, the phase trajectory profile is controllable, and the phase returns to the target attitude within 4.5 s after the disturbance disappears, without oscillation.

[0023] Referring to Figure 1, Figure 6 , Figure 3 Control input characteristics and Figure 5 Phase space and phase trajectory; the essential breakthrough of this invention lies in: abandoning the limitation of traditional flapping-wing aircraft's single strategy being difficult to adapt to all scenarios, and quantifying the disturbance intensity with the K* value ( Figure 6 Figure 6 The K* values ​​of 1, 10, and 20 correspond to low, medium, and high disturbances, respectively. This replaces the traditional empirical classification and fundamentally solves the core contradiction of wasted resources for low disturbances and insufficient anti-disturbance for high disturbances by using a new mode with a fixed outer loop PID main loop and dynamic switching of three sub-strategies in the inner loop.

[0024] In summary, this invention provides core technical support for the stable operation of micro flapping-wing aircraft in multiple scenarios, and can also provide a reusable cascaded architecture paradigm for the control design of highly nonlinear and multi-disturbance systems, possessing significant theoretical innovation value and practical application prospects.

Claims

1. A flapping wing vehicle cascade control switching method based on disturbance quantification and PID architecture, characterized in that, The method comprises the following steps: S1, selecting a three-section foldable bionic flapping-wing aircraft as a controlled object; S2, based on the controlled object, a cascade control framework is constructed, in which a classic PID control is used as an outer ring main loop, and PID control, nonlinear PID control and sliding mode control are used as inner ring sub-loops; S3, construct an extended state observer ESO to estimate the controlled object in real time, for calculate the basis for disturbance compensation; S4、based on The adaptive switching logic weight coefficient is designed according to the calculation result. S5, based on the adaptive switching logic weight coefficient, a total control instruction is generated, and the construction of the flapping-wing aircraft cascade control switching method is completed.

2. The flapping wing vehicle cascade control switching method based on disturbance quantification and PID architecture according to claim 1, characterized in that, The control equation expression of the classic PID control as the outer ring main loop is as follows: In the formula, represents a desired roll angle; represents a desired pitch angle; represents a desired yaw angle; represents a directional position error, expressed as: , represents a directional position error, expressed as: , represents a directional position error, expressed as: wherein, ， and represent a reference position, represents an actual direction; , and respectively represent a position loop proportional gain in the direction, set the position loop proportional gain ; , and respectively represent a position loop integral gain in the direction, set the position loop integral gain ; , and respectively represent a position loop derivative gain in the direction, set the position loop derivative gain ; For the outer loop, i.e. the loop control part of the position loop, the control parameters are set to Channel parameters in three directions are set, including: 、 、 ； 、 、 ； 、 、 。 3. The flapping wing vehicle cascade control switching method based on disturbance quantification and PID architecture according to claim 1, characterized in that, In the cascade control framework with PID control, nonlinear PID control and sliding mode control as the inner ring sub-loops, the attitude ring control expression of the PID control is as follows: wherein represents a roll channel control moment; represents a pitch channel control moment; represents a yaw channel control moment; represents a roll angle error, expressed as , represents a pitch angle error, expressed as: ; represents a yaw angle error, expressed as: wherein , and represent a desired roll angle, a desired pitch angle and a desired yaw angle, respectively, represents a roll angle, represents a pitch angle, represents a yaw angle; , and represent roll angle, pitch angle and yaw angle attitude loop proportional gains, respectively; , and represent roll angle, pitch angle and yaw angle attitude loop integral gains, respectively; , and represent roll angle, pitch angle and yaw angle attitude loop derivative gains, respectively; A multi-channel coupling compensation term is introduced, denoted as: wherein, denotes the original desired roll angle, 0.3 and 0.2 are prescribed coupling coefficients, and y z the position error in the x direction interferes with the channel. denotes the position error in the direction.​ The inner ring PID control parameter setting is as follows: x and z The same control parameters are set for the directional channels, the control parameters being: Settings x and z Parameter ranges on directional channels are: , , ; y Control parameters for the directional channel are: y Proportional gain for the attitude loop on the directional channel , y Integral gain for the attitude loop on the directional channel , y Rate gain for the attitude loop on the directional channel .

4. The flapping wing vehicle cascade control switching method based on disturbance quantification and PID architecture according to claim 3, characterized in that, In the cascade control framework with PID control, nonlinear PID control and sliding mode control as the inner ring sub-loops, the design expression of the nonlinear PID control includes: The inner channel pitch uses the inner ring PID control loop to ensure stability. According to the inner channel roll control law, a nonlinear control torque is obtained, and the expression is as follows: wherein represents a roll angle channel nonlinear control moment; represents a nonlinear roll angle proportional gain; represents a roll angle error; represents a nonlinear roll angle integral gain; represents an integral term nonlinear saturation term, represents a hyperbolic tangent function; represents a saturation function parameter; represents a fractional linear roll angle derivative gain; represents a nonlinear correction coefficient; According to the inner channel, that is, the yaw angle control law, a nonlinear control torque is obtained: wherein represents a yaw angle channel non-linear control moment; represents a non-linear yaw angle proportional gain; represents a yaw angle error; represents a non-linear yaw angle integral gain; represents an integral term non-linear saturation term; represents a saturation function parameter; represents a non-linear yaw angle derivative gain; represents a non-linear correction coefficient; Introducing ESO disturbance compensation term to get nonlinear control moment : wherein represents a roll angle base moment of the nonlinear PID calculation; represents a roll angle aerodynamic force disturbance estimated by the ESO. Set the nonlinear correction coefficient In x and z 0.8 on the directional channel; set the nonlinear correction coefficient In y the directional channel 0.75; the inner ring nonlinear PID control roll angle and yaw angle channel nonlinear proportional gain Both take 0.75, the roll angle and yaw angle channel nonlinear integral gain Both take 4, the roll angle and yaw angle channel nonlinear differential gain Both take 0.

75.

5. The flapping wing vehicle cascade control switching method based on disturbance quantification and PID architecture according to claim 1, characterized in that, In the cascade control framework with PID control, nonlinear PID control and sliding mode control as the inner ring sub-loops, the sliding mode control SMC control design includes: The slip surface is defined as: where, represents the roll channel slip surface function; represents the roll angle error; represents the slip surface parameters; The exponential reaching law is defined as: wherein, represents the rate of change of the sliding surface; represents the reaching law; represents the reaching law exponential term coefficient; The control torque expression obtained by the control law is as follows: wherein represents a roll channel control torque; represents a roll axis moment of inertia; represents a desired roll angular velocity; represents a slip surface parameter; represents a roll angle error; represents a reaching law; represents a roll channel sliding mode surface function; represents a boundary layer thickness; represents a reaching law exponential term coefficient; represents an angular velocity vector; represents a moment of inertia matrix; represents an angular velocity vector matrix; represents a disturbance estimate; represents a hyperbolic tangent function; Energy coupling correction: in order to ensure the multi-channel energy coupling correction, the dynamic weight coefficient is introduced by considering the flapping-wing energy consumption and the control amount, and the control torque correction is: , wherein, denotes the SMC base control torque, denotes the dynamic weight coefficient; flapping frequency ∈ ; The inner ring SMC control parameter setting is as follows: x and z same control parameters, sliding surface parameters , reaching law , reaching law index term coefficient , boundary layer thickness , chattering suppression y channel control parameter, synovial membrane surface parameter , approach law , approach law index term coefficient , boundary layer 2.

6. The flapping wing vehicle cascade control switching method based on disturbance quantification and PID architecture according to claim 1, characterized in that, The expression of the extended state observer ESO is as follows: In the formula, represents a public angle estimation value ; represents a public angle rate of change estimation value ; represents an aerodynamic force disturbance estimation value ; 、 and respectively represent a first observer gain, a second observer gain, and a third observer gain; represents a sensor feedback signal; represents a control matrix; represents a control input; Aerodynamic force disturbance estimate The expression for the aerodynamic force disturbance estimate is as follows: ； Setting the estimation error ≤ 5%, the value calculation accuracy ≤ 3%.

7. The flapping wing vehicle cascade control switching method based on disturbance quantification and PID architecture according to claim 1, characterized in that, The method comprises the following steps: The calculation result, the design of the adaptive switching logic weight coefficient comprises: introducing a dynamic weight coefficient 、 And Corresponding to PID, nonlinear PID and sliding mode control SMC sub-strategy respectively, meet ; The weight coefficient calculation expression of the inner loop PID is: ; The weight coefficient calculation expression of the nonlinear PID is: ; The weight coefficient calculation expression of SMC is: - .

8. The flapping wing vehicle cascade control switching method based on disturbance quantification and PID architecture according to claim 1, characterized in that, The expression of the total control instruction is as follows: wherein denotes the control output of the outer loop denotes the control output of the inner loop denotes the control output of the inner loop denotes the control output of the outer loop denotes the control output of the inner loop denotes the control output of the inner loop sliding mode control denotes the control output of the outer loop denotes the control output of the inner loop sliding mode control denotes the control output of the outer loop ； 、 and denote the weight coefficients corresponding to the PID, nonlinear PID and sliding mode control SMC sub-strategies, respectively.

9. The flapping wing vehicle cascade control switching method based on disturbance quantification and PID architecture according to claim 8, characterized in that, In the total control instruction, in order to ensure the stability in the switching process, the system stability is monitored in real time through the Lyapunov function, and the Lyapunov function is less than or equal to zero.

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