CN-121806515-B - Deformable wing flutter inhibition stabilization method based on adaptive control

CN121806515BCN 121806515 BCN121806515 BCN 121806515BCN-121806515-B

Abstract

The invention discloses a method for inhibiting and stabilizing flutter of a deformable wing based on self-adaptive control, which relates to the technical field of aeroelastic active control of aircrafts, and the invention comprises the steps of constructing a fault-tolerant control system for real-time diagnosis, online reconstruction and dynamic redistribution, continuously monitoring health of actuator clusters, actively diagnosing the running state of each actuator, identifying abnormal states such as saturation, partial failure or complete failure of the actuator, starting an online reconstruction mechanism, the fault information is integrated into the real-time calculation of the control law, the feedback gain matrix of the controller is dynamically adjusted, the virtual control quantity calculated by the reconstructed control law is used as an integral instruction, the integral instruction is re-analyzed and distributed to all healthy actuators, and the functions of the original fault actuators are equivalently replaced in the pneumatic effect through the cooperative modes such as differential combination and the like, so that the functional integrity is maintained, and the stable control of the flutter inhibition under the fault of the actuators is realized.

Inventors

JIA ZHAOHU
CHENG LONGFEI
CUI JIE
XU JINJIN
FENG YAJING
GAO YIDI
WANG XUAN
WANG JINGYU
GUO YAZHOU

Assignees

西北工业大学

Dates

Publication Date: 20260508
Application Date: 20260309

Claims (6)

1. A method for inhibiting and stabilizing flutter of a deformable wing based on self-adaptive control is characterized by comprising the following specific steps: S1, real-time monitoring and health diagnosis, namely continuously monitoring the operation parameters of each deformation actuator on the wing, and diagnosing the health state of each actuator in real time The health status includes normal, saturated, partially disabled, and fully disabled; S2, fault triggering and online reconstruction, namely once any actuator is diagnosed to be in any one of three abnormal health states of saturation, partial failure and complete failure, reconstructing a control efficiency matrix of the system, and immediately triggering an online reconstruction process of the self-adaptive control law; Control efficiency matrix of the reconstruction system The specific method of (a) is as follows: based on an initial control efficiency matrix Wherein Is the number of modes of the system, Is the total number of actuators, Is defined in each column of (1) Represents the first The control efficiency of the actuators to different modes; health status identification obtained according to S1 Reconstruction is performed according to the following rules : When (when) Then reserve the column, i.e ; When (when) Then the column element is zeroed out, i.e ; When (when) Then multiply the column element by an attenuation factor I.e. Wherein And Respectively the first The output displacement and preset displacement of the actuators; In the S2 fault triggering and online reconstruction, the specific steps of the online reconstruction process are as follows: Based on the system matrix A in the current flight state and the reconstructed health state according to the diagnosis of S1 Solving Lyapunov equation Obtaining positive definite matrix ; Using matrices obtained by solution And generating a reconstructed self-adaptive control law, wherein the output virtual control quantity is as follows: , wherein, The virtual control amount generated after the reconstruction, Is the state vector of the actuator, comprising the bending displacement, the torsion displacement and the change rate of the wing, Is a control efficiency matrix after reconstruction, The transpose operation of the matrix is represented, Is a positive control weight matrix used for balancing control effect and energy consumption; s3, dynamically reassigning the control quantity, namely assigning the expected virtual control quantity generated in the S2 to all the remaining healthy actuators by utilizing a reassignment algorithm; S4, fault-tolerant cooperative execution, namely, the rest healthy actuators receive the control instruction redistributed in the S3 and perform cooperative action by changing the deformation mode of the rest healthy actuators so as to generate equivalent flutter inhibition force; And S5, stably maintaining, namely feeding back the body response after vibration suppression through a vibration sensor on the wing, and comparing with a vibration-free state.
2. An adaptive control-based method for stabilizing flutter suppression of a deformable wing according to claim 1, wherein said S1 real-time monitoring and health diagnosis is performed during each control cycle Collect in real time the first The operating parameters of the actuators include input command voltages Output displacement Drive current Preset displacement Calculating the absolute value of the displacement residual error And diagnosing the health state of the actuator in real time based on the operation parameters of the actuator: Normal: and absolute value of drive current Wherein For a preset first displacement residual threshold, Is the rated maximum current of the actuator; saturation: Or (b) At the same time Wherein And Is the positive and negative physical travel limit of the actuator, A current threshold that is saturated; Partial failure: and the actuator is not determined to be in a saturated state, wherein Is a second displacement residual threshold value for distinguishing partial failure from complete failure and ; Complete failure: or at a command voltage In the normal case of the device, 、 ; Based on the determination result, for each actuator Giving a control period at the present time Internally unique health status identification And is also provided with 。
3. The adaptive control-based variable wing flutter suppression stabilization method according to claim 1, wherein in the S3 control amount dynamic redistribution, a redistribution algorithm is used for mapping a virtual control amount to an actuator space with a normal health state, and an optimization problem targeting on distribution precision and actuator constraint is constructed: The object is: ; Constraint: ; Wherein, the For the normal actuator command vector to be solved, And Upper and lower limits for physical output; solving the optimization problem by adopting a numerical optimization algorithm to obtain an allocated instruction vector 。
4. A method of adaptive control-based variable wing flutter suppression stabilization according to claim 3, wherein the numerical optimization algorithm is implemented by Solving the optimization problem, wherein, Is a post-reconstruction control efficiency matrix Pseudo-inverse of (2) 。
5. A method for stabilizing flutter suppression of a deformable wing based on adaptive control according to claim 3, wherein the specific steps of S4 fault-tolerant co-execution are as follows: post-allocation instruction vector outputting S3 Analyzing the control signals into control signals with time sequences of each normal actuator; All normal actuators synchronously act according to the analyzed signals, namely when the actuators fail, the adjacent actuators jointly synthesize the required control moment through a differential combination mode, wherein the differential combination mode is used for driving the adjacent healthy state of the main actuators of the wing to be the normal actuators after the main actuators of the wing fail, so that the actuators generate deflection with different sizes and different directions, and the asymmetric local aerodynamic force caused by the deflection is synthesized into an equivalent total control force and total control moment acting on the wing to replace the function of the original failed actuators.
6. The adaptive control-based variable wing flutter suppression stabilization method according to claim 1, wherein the specific step of S5 stable maintenance is: a plurality of vibration sensors deployed on the wing, continuously collecting dynamic response signals of the wing after S4 fault tolerance cooperative execution, wherein the vibration sensors comprise strain gauges and accelerometers, and the dynamic response signals directly represent state vectors of the wing ; The collected state vector Comparing the state error vector with the vibration-free state, and calculating to obtain a state error vector; and taking the state error vector as the input of the self-adaptive control law after S2 reconstruction to carry out dynamic control quantity redistribution and fault tolerance cooperative execution until the state error vector converges to a preset stable state.

Description

Deformable wing flutter inhibition stabilization method based on adaptive control Technical Field The invention relates to the technical field of aeroelastic active control of aircrafts, in particular to a method for inhibiting and stabilizing flutter of a deformable wing based on self-adaptive control. Background The deformable wing adapts to different flight states by changing the aerodynamic shape of the deformable wing, so that the full envelope performance is improved. However, such flexible structures are more prone to aeroelastic flutter under certain flight conditions, a dynamic instability that can lead to catastrophic results, and the prior art generally employs active control methods to dampen vibrations by driving actuators disposed on the wing. However, the prior art has a key defect that the control strategy is seriously dependent on the premise that all actuators work normally, when one or more actuators are degraded, saturated or completely disabled due to electrical faults, mechanical clamping stagnation or physical damage, the conventional fixed control law not only can drastically reduce vibration suppression efficiency, but also can introduce new unstable factors due to asymmetric distribution of control force, and an effective method capable of sensing the health state of the actuators in real time in the flight process and automatically and intelligently reallocating control resources to maintain the stability of the system is lacking at present. Therefore, a flutter suppression system with an inherent fault tolerance is urgently needed, and the flutter suppression system can quickly reconstruct a control strategy when an actuator fails, so that flight safety is guaranteed. Disclosure of Invention The invention aims to make up the defects of the prior art and provides a variable wing flutter inhibition stabilization method based on self-adaptive control, which can realize continuous health monitoring on actuator clusters by constructing a fault-tolerant control system with real-time diagnosis, online reconstruction and dynamic redistribution, actively diagnose the running state of each actuator, integrate fault information into real-time calculation of a control law by identifying abnormal states such as saturation, partial failure or complete failure of the actuator, start an online reconstruction mechanism, dynamically adjust a feedback gain matrix of a controller, re-analyze and distribute virtual control quantity calculated by the reconstruction control law as an integral instruction to all healthy actuators, and equivalently replace the functions of original fault actuators on pneumatic effect through cooperative modes such as differential combination, thereby maintaining the functional integrity, realizing stable flutter inhibition control under the fault of the actuators and improving the system viability and task reliability. The invention provides a deformable wing flutter inhibition stabilization method based on self-adaptive control, which comprises the following specific steps: S1, real-time monitoring and health diagnosis, namely continuously monitoring the operation parameters of each deformation actuator on the wing, and diagnosing the health state of each actuator in real time The health status includes normal, saturated, partially disabled, and fully disabled; S2, fault triggering and online reconstruction, namely once any actuator is diagnosed to be in any one of three abnormal health states of saturation, partial failure and complete failure, reconstructing a control efficiency matrix of the system, and immediately triggering an online reconstruction process of the self-adaptive control law; s3, dynamically reassigning the control quantity, namely assigning the expected virtual control quantity generated in the S2 to all the remaining healthy actuators by utilizing a reassignment algorithm; S4, fault-tolerant cooperative execution, namely, the rest healthy actuators receive the control instruction redistributed in the S3 and perform cooperative action by changing the deformation mode of the rest healthy actuators so as to generate equivalent flutter inhibition force; And S5, stably maintaining, namely feeding back the body response after vibration suppression through a vibration sensor on the wing, and comparing with a vibration-free state. Further, in the S1 real-time monitoring and health diagnosis, in each control periodCollect in real time the firstThe operating parameters of the actuators include input command voltagesOutput displacementDrive currentPreset displacementCalculating the absolute value of the displacement residual errorAnd diagnosing the health state of the actuator in real time based on the operation parameters of the actuator: Normal: and absolute value of drive current WhereinFor a preset first displacement residual threshold,Is the rated maximum current of the actuator; saturation: Or (b) At the same timeWhereinAndIs the positive and negative