CN115877717A - Aircraft fault-tolerant control structure and control method based on active disturbance rejection control - Google Patents
Aircraft fault-tolerant control structure and control method based on active disturbance rejection control Download PDFInfo
- Publication number
- CN115877717A CN115877717A CN202211673317.2A CN202211673317A CN115877717A CN 115877717 A CN115877717 A CN 115877717A CN 202211673317 A CN202211673317 A CN 202211673317A CN 115877717 A CN115877717 A CN 115877717A
- Authority
- CN
- China
- Prior art keywords
- model
- fault
- kalman filtering
- coefficient
- control
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
Landscapes
- Feedback Control In General (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention provides an aircraft fault-tolerant control structure and method based on active disturbance rejection control, and belongs to the technical field of aircraft fault-tolerant control. The problem of four rotor unmanned vehicles because the flight that actuating mechanism trouble and sensor measurement noise lead to is unstable or flight accident is solved. The technical scheme is as follows: the control structure comprises a dynamic model with an execution mechanism fault description, an improved Kalman filtering model, an observation model and a master control model; the control method comprises the following steps: by designing and improving a Kalman filtering model to be used as a pre-filtering stage, the fault coefficient and the flight state of an actuating mechanism are estimated in a measurement noise environment. The beneficial effects of the invention are as follows: the four-rotor unmanned aerial vehicle can quickly and accurately estimate the fault coefficient and realize fault-tolerant control under the condition of measuring noise, and the safety and stability of a flight task are ensured.
Description
Technical Field
The invention relates to the field of fault-tolerant control of flight systems, in particular to an aircraft fault-tolerant control structure and control method based on active disturbance rejection control.
Background
In recent years, the four-rotor unmanned aerial vehicle has very wide application in scientific research and production and life due to the characteristics of relatively simple structure, easy control and the like. The researchers have conducted a great deal of research on them, such as: the method comprises the following steps of four-rotor state estimation, attitude control of the four-rotor unmanned aerial vehicle, trajectory tracking control of the four-rotor unmanned aerial vehicle and the like. With the extensive use of four rotors, the safety and reliability of the control method has attracted great attention.
The aircraft can be timely adjusted and controlled by monitoring faults in time, and personnel injuries and property losses caused by faults of the aircraft are avoided. In order to discover the faults of the four-rotor unmanned aerial vehicle in time and ensure the safety and the reliability of the four-rotor unmanned aerial vehicle after the faults occur, a large amount of research is conducted by related experts in the fields of fault diagnosis, fault-tolerant control and the like of the four-rotor unmanned aerial vehicle, and the problem is the hotspot problem of the flight control direction.
At present, the flight control of part of domestic four-rotor unmanned aerial vehicles adopts an active disturbance rejection control algorithm, and the method has the advantages that the uncertainty and the external disturbance of a model can be effectively estimated, the parameter adjusting process is simple, and the flight effect is good. The active disturbance rejection control realizes accurate estimation of disturbance through an extended state observer. However, the observer-based control method is sensitive to measurement noise, and the bandwidth of the extended state observer is limited due to the magnitude of the measurement noise, thereby causing inaccurate disturbance estimation. Therefore, how to provide a fault-tolerant control method for a quadrotor unmanned aerial vehicle insensitive to measurement noise is a problem to be solved urgently by a flight controller based on active disturbance rejection control.
Disclosure of Invention
The invention provides an aircraft fault-tolerant control structure and a control method based on active disturbance rejection control, and aims to perform fault diagnosis and fault-tolerant control on an aircraft and improve the disturbance rejection capability of a flight controller in a noise environment.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that: an aircraft fault-tolerant control structure based on active disturbance rejection control, comprising:
constructing a dynamic model with an execution mechanism fault description, an improved Kalman filtering model, an observation model and a master control model according to the aircraft;
an improved Kalman filtering model is built according to a four-rotor unmanned aerial vehicle dynamic model with an actuator fault description, the improved Kalman filtering model is used as a prefiltering stage, state signals needing to be used in an observation model are calculated, and a fault coefficient and a system state are quickly and accurately estimated;
inputting a fault coefficient estimated by the improved Kalman filtering model into the observation model, observing the fault estimated by the improved Kalman filtering model at the current moment by an observer, and outputting a disturbance estimation value according to an observation result;
system states estimated using the improved Kalman filtering model
And failure coefficient b k The actual output value and the control input gain in the traditional extended state observer are replaced, so that the estimation accuracy of the observation model in the environments of actuator faults and measurement noise is improved;
estimating a fault coefficient b according to the improved Kalman filtering model k And system state
And observing the model and observing the output design the total control model, wherein the total control model has six loops: three position loops of X, Y and Z and three attitude loops of pitch angle, yaw angle and roll angle, wherein each loop adopts an active disturbance rejection controller according to a fault coefficient b k Adjusting parameter b in active disturbance rejection controller in real time 0 ；
The master control model outputs a fault coefficient estimated value b according to the observation model and the improved Kalman filtering model k And system state estimation
And calculating the flight control command U, inputting the flight control command U to the four-rotor unmanned aerial vehicle dynamic model with the executing mechanism fault description to realize a set flight task, ensuring fault-tolerant control in time after a fault occurs and ensuring stable flight of the four-rotor unmanned aerial vehicle.
In order to better achieve the above object, the present invention further provides a control method of an aircraft fault-tolerant control structure based on active disturbance rejection control, which specifically includes the following steps:
step S1: for the position motion and attitude motion system of the four-rotor unmanned aerial vehicle, according to the newton-euler formula, assuming that the four-rotor unmanned aerial vehicle is in a hovering state and in a non-yawing state, a simplified dynamic model of the four-rotor unmanned aerial vehicle can be obtained as follows:
in formula (1), X, Y and Z are coordinates of the center of mass of the aircraft under the earth frame, J 1 Moment of inertia in the Y direction, J 2 Moment of inertia in the X direction, J 3 Is the moment of inertia in the Z direction, and m is the fuselage mass of the aircraft;
constructing a discrete linear time-varying state-space equation to describe the four-rotor unmanned aerial vehicle system according to the simplified dynamic model:
in the formula (2), x k 、u k 、y k Respectively a state variable, a control input variable, an output variable, A k 、B k 、C k Then the corresponding matrix of adjustable coefficients is used,
and v k+1 Is uncorrelated white Gaussian noise;
modeling actuator faults as control performance loss and fault coefficients:
in the formula (3), b k I.e. a matrix of failure coefficients, when b ik =0 or b ik If =1, it represents that the i-th actuator has no problem at all or has a fault and is completely damaged, and in order to facilitate parameter estimation, the state space equation of the above equation can be written as:
in formula (4), U k ＝diag[u 1k u 2k u 3k u 4k ]；
Step S2: the method comprises the steps that an improved Kalman filtering model is built according to a dynamic model with actuator fault description, compared with a common Kalman filtering model, the improved Kalman filtering model adopts a two-stage Kalman filtering algorithm as a prefiltering stage and is used for estimating actuator fault coefficients and aircraft state information;
kalman filter equations fall into two categories: the method comprises the following steps that a time updating equation and a measurement updating equation are used, the time updating equation advances the state and the error covariance by one step in the time domain to obtain a priori estimation, the measurement updating equation feeds the measurement back to the priori estimation to obtain a posteriori estimation, and the whole prediction correction process is used for estimating the state which is as close to the true value of the prediction correction process as possible, so the specific steps of constructing the improved Kalman filtering model comprise:
step S21: the improved Kalman filtering model is a two-stage Kalman filtering model. On the basis of a Kalman filtering model, a bias term is ignored, and a non-bias filter is constructed, wherein the specific formula is as follows:
in the formula (5), the first and second groups of the chemical reaction materials are selected from the group consisting of,
for ignoring the state estimate at time k when the bias is ignored, <' >>
To predict the state estimate at time k +1 while ignoring the offset,
to ignore the state estimate at time k +1 when the offset is applied, V k+1|k Predicting the offset compensation factor for the k +1 th instant and calculating it from the coupling equation>
For the filtering gain->
The estimated value of the fault coefficient at the kth moment is obtained; />
To ignore the error covariance at time k +1 at the time k when the bias is ignored, <' >>
Error covariance at the k +1 th moment when bias is ignored;
step S22: further constructing a bias filter, calculating the residual state and the output of the unbiased filter and reconstructing the original system state, wherein the formula of the bias filter is as follows:
in the formula (6), the first and second groups of the compound,
for compensation at the (k + 1) th timeA filter gain->
Predicting the covariance of the compensated filter at time k +1 for time k k+1|k For the conversion matrix from the current state into a measurement and calculated from the coupling equation, <' >>
For compensating the filter covariance at the k +1 th time, R k+1 Is the noise covariance->
The estimated value of the fault coefficient at the k +1 th moment;
step S23: and carrying out linear combination according to the unbiased filter and the biased filter to obtain an optimal state estimation value, wherein an original state estimation value and an error covariance matrix can be obtained through compensation, and the formula is as follows:
in the formula (7), the first and second groups,
is the state estimate at time k +1, V k+1|k+1 Is the offset compensation coefficient at the k +1 th moment and is calculated by a coupling equation, P k+1|k+1 Error covariance for time k +1, based on>
Is the fault coefficient estimate at the time k +1>
Error covariance at the k +1 th moment when bias is ignored;
and step S3: the observation model estimates a fault coefficient b according to the improved Kalman filtering model k And system state
The total disturbance is estimated and the observation model is shown as follows:
in the formula (8), b k To improve the estimated fault coefficients of the kalman filter model,
system state, beta, estimated for the improved Kalman Filter model 1 Is an adjustable observation gain one, beta of the observation model 2 Is an adjustable observation gain of the observation model 3 An adjustable observation gain of three, z for the observation model 1 Is paired with>
An observed value of z 2 Is paired with>
Observed value of derivative, z 3 Is an observed value of the total disturbance;
the observation model differs from a conventional extended state observer in the system state estimated using the improved Kalman filtering model
And failure coefficient b k The actual output value and the control input gain in the traditional extended state observer are replaced, so that the estimation accuracy of the observation model in the environments of actuator faults and measurement noise is improved;
and step S4: the fault coefficient b estimated according to the improved Kalman filtering model k And system state
And z observed by the observation model 1 ，z 2 And z 3 Designing the master control model, wherein the master control model has six loops: three position loops of X, Y and Z and three attitude loops of pitch angle, yaw angle and roll angle, wherein each loop adopts an active disturbance rejection controller according to a fault coefficient b k Adjusting parameter b in active disturbance rejection controller in real time 0 ；
Step S5: z from observation model output 1 ，z 2 And z 3 And improving the fault coefficient b estimated by the Kalman filtering model k Calculating a flight control command U, wherein the specific formula is as follows:
in the formula (9), u 0 V is a set flight track signal, k is a flight control command before total disturbance compensation p Is a proportionality coefficient, k d Is a differential coefficient, k p 、k d Can be calculated by the following equations: k is a radical of p ＝ω c 2 ，k d ＝2ω c ；
And a flight control instruction U is input to a four-rotor unmanned aerial vehicle dynamic model with an execution mechanism fault description so as to realize a set flight task, ensure that fault-tolerant control is carried out in time after a fault occurs, and ensure the stable flight of the four-rotor unmanned aerial vehicle.
Compared with the prior art, the invention has the beneficial effects that: when the unmanned aerial vehicle executing mechanism has faults and the system has measurement noise, the control structure and the control method provided by the invention can accurately estimate the fault coefficient of the executing mechanism and the current flight state of the unmanned aerial vehicle by using the improved Kalman filtering model, and reduce the influence of the faults and the measurement noise on the observation model. Furthermore, according to the fault coefficient and the estimated current flight state, the observation model and the master control model can accurately estimate the total disturbance of the system and compensate in real time, so that the aircraft can realize stable flight when an execution mechanism fails, and can still effectively resist external disturbance.
Drawings
For the purpose of more clearly illustrating the methods in the practice of the present application and for the convenience of understanding, some brief descriptions will be given below to some drawings in the practice, which together with the embodiments of the present invention serve to explain the present invention and not to limit the present invention.
Fig. 1 is a block diagram of a fault-tolerant control structure of a four-rotor unmanned aerial vehicle according to embodiment 1 of the present invention;
fig. 2 is a schematic flowchart of a fault-tolerant control method for a four-rotor unmanned aerial vehicle according to embodiment 1 of the present invention;
fig. 3 is a structural block diagram of an improved kalman filter model in a fault-tolerant control method for a quad-rotor unmanned aerial vehicle according to embodiment 1 of the present invention;
fig. 4 is a structural block diagram of a master control model in a fault-tolerant control method for a quad-rotor unmanned aerial vehicle according to embodiment 1 of the present invention;
FIG. 5 is a schematic diagram of a fault detection result of an improved Kalman filtering model provided in embodiment 2 of the present invention;
fig. 6 is a schematic diagram of a trajectory tracking effect of a quad-rotor unmanned aerial vehicle provided in embodiment 3 of the present invention.
Wherein the reference numerals are: 11. a kinetic model; 12. improving a Kalman filtering model; 13. observing the model; 14. and (4) a master control model.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort belong to the protection scope of the present application.
The flowcharts shown in the figures are illustrative only and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations or steps may be decomposed, combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
It is to be understood that the terminology used in the description of the present application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments described below and the features of the embodiments can be combined with each other without conflict.
Example 1
The structure and the method aim at timely detecting faults of the aircraft and performing fault-tolerant control, ensure that the aircraft can keep relatively stable flight, and improve the safety and the reliability of the aircraft.
Referring to fig. 1, fig. 1 is a block diagram of a fault-tolerant control structure of a quad-rotor unmanned aerial vehicle according to embodiment 1 of the present disclosure;
as shown in fig. 1, a dynamics model 11 with an actuator fault description, an improved kalman filter model 12, an observation model 13 and a master control model 14 are constructed according to a four-rotor unmanned aerial vehicle;
the improved Kalman filtering model 12 is built according to the dynamic model 11 with the executing mechanism fault description and is used as a prefiltering stage for calculating state signals required to be used in the observation model 13. The aircraft position signals obtained from the four-rotor unmanned aircraft GPS and the theta, phi and psi signals of the three attitude angles of the aircraft obtained from the flight control gyroscope are used as the input of the improved Kalman filtering model 12. Improved Kalman filtering model 12 for estimating fault coefficient b k And system state
And input into the observation model 13, the system status->
The original actual measurement signal in the observation model 13 is replaced, and the observation model 13 outputs z according to the observation result 1 ，z 2 And z 3 . Fault coefficient b estimated according to improved kalman filtering algorithm k And observation model 1And 3, a feedback controller is designed according to the output of the controller, and a flight control command U is calculated and input to a four-rotor unmanned aerial vehicle dynamic model 11 with an execution mechanism fault description so as to realize a set flight task, so that fault-tolerant control is timely performed after a fault occurs, and stable flight of the four-rotor unmanned aerial vehicle is ensured.
As shown in fig. 2, fig. 2 is a schematic flowchart of a fault-tolerant control method for a four-rotor unmanned aerial vehicle according to embodiment 1 of the present invention;
the four-rotor unmanned aerial vehicle fault-tolerant control method specifically comprises the steps S1 to S5.
Constructing a dynamic model with an execution mechanism fault description, an improved Kalman filtering model, an observation model and a master control model according to the quad-rotor unmanned aerial vehicle;
step S1: specifically, for the position motion and attitude motion system of the quad-rotor unmanned aerial vehicle, according to the newton-euler formula, assuming that the quad-rotor unmanned aerial vehicle is in a hovering state and is not in a non-yawing state, a simplified dynamic model of the quad-rotor unmanned aerial vehicle can be obtained as follows:
in formula (1), X, Y and Z are coordinates of the center of mass of the aircraft under the earth frame, J 1 Moment of inertia in the Y direction, J 2 Moment of inertia in the X direction, J 3 Is the moment of inertia in the Z direction, and m is the fuselage mass of the aircraft;
constructing a discrete linear time-varying state-space equation to describe the four-rotor unmanned aerial vehicle system according to the simplified dynamic model:
in the above formula (2), x k 、u k 、y k Respectively a state variable, a control input variable, an output variable, A k 、B k 、C k Is correspondingThe matrix of adjustable coefficients is then used to determine,
and v k+1 Is uncorrelated white Gaussian noise;
actuator faults are modeled as control performance loss and fault coefficients:
in the formula (3), b k I.e. a matrix formed by fault coefficients, when b ik =0 or b ik If =1, it represents that the i-th actuator has no problem at all or has a fault and is completely damaged, and for the convenience of parameter estimation, it can be obviously indicated that the actuator of the quad-rotor unmanned aerial vehicle has a fault, and the state space equation of the above equation can be written as follows:
in formula (4), U k ＝diag[u 1k u 2k u 3k u 4k ]，
And v k+1 Is uncorrelated white Gaussian noise; />
As shown in fig. 3, an improved kalman filtering model is constructed, including:
step S2: the method comprises the steps that an improved Kalman filtering model is built according to a dynamic model with actuator fault description, compared with a common Kalman filtering model, the improved Kalman filtering model adopts a two-stage Kalman filtering algorithm as a prefiltering stage and is used for estimating actuator fault coefficients and aircraft state information;
the kalman filter equation falls into two categories: a time update equation and a measurement update equation, wherein the time update equation advances the state and the error covariance by one step in the time domain to obtain a prior estimate, the measurement update equation feeds back the measurement to the prior estimate to obtain a posterior estimate, and the whole prediction correction process is used for estimating a state as close to the true value as possible, and the step S2 specifically includes:
step S21: the improved Kalman filtering model is a two-stage Kalman filtering model. On the basis of a Kalman filtering model, a bias term is ignored, and a non-bias filter is constructed, wherein the specific formula is as follows:
in the formula (5), the first and second groups,
for ignoring the state estimate at time k when the bias is ignored, <' >>
To predict the state estimate at time k +1 while ignoring the offset,
to ignore the state estimate at time k +1 when the offset is applied, V k+1|k Predicting a bias compensation factor for the k +1 th instant and calculating it from a coupling equation>
For the filtering gain->
The estimated value of the fault coefficient at the kth moment is obtained; />
To ignore the error covariance at time k +1 at the time k when the bias is ignored, <' >>
Error covariance at the k +1 th moment when bias is ignored;
step S22: constructing a bias filter, generating the residual state and the output of the unbiased filter and reconstructing the original system state, the formula of the bias filter is as follows:
in the formula (6), the first and second groups,
for the compensation filter gain at the time k +1>
Predict the compensated filter covariance at time k +1 for time k>
For compensating the filtered covariance at the k +1 th time, H k+1|k For the conversion matrix from the current state to the measurement and calculated from the coupling equation, R k+1 Is the noise covariance->
The estimated value of the fault coefficient at the k +1 th moment;
step S23: the optimal state estimation is obtained by linear combination according to the unbiased filter and the biased filter, and the original state estimation value and the error covariance matrix can be obtained by compensation, wherein the formula is as follows:
in the formula (7), the first and second groups,
is the state estimate at time k + 1, V k+1|k+1 Is the offset compensation coefficient at the k +1 th moment and is calculated by a coupling equation, P k+1|k+1 For error correction at the k +1 th timeVariance,. Or>
Is the fault coefficient estimate at the time k +1>
Error covariance at the k +1 th moment when bias is ignored;
and step S3: the observation model estimates a fault coefficient b according to the improved Kalman filtering model k And system state
The total disturbance is estimated and the observation model is shown as follows:
in the formula (8), b k To improve the estimated fault coefficients of the kalman filter model,
system state, beta, estimated for the improved Kalman filter model 1 Is an adjustable observation gain one, beta of the observation model 2 Is the adjustable observation gain two, beta of the observation model 3 An adjustable observation gain of three, z for the observation model 1 Is->
Observed value of (a), z 2 Is->
Observed value of derivative, z 3 Is the observed value of the total disturbance;
the observation model differs from a conventional extended state observer in the system state estimated using the improved Kalman filtering model
And failure coefficient b k The actual output value and the control input gain in the traditional extended state observer are replaced, so that the estimation accuracy of the observation model in the environments of actuator faults and measurement noise is improved;
as shown in fig. 4, the general control model is constructed, and the specific steps include:
and step S4: a fault coefficient b estimated according to the improved Kalman filtering model k And system state
And z observed by the observation model 1 ，z 2 And z 3 Designing the master control model, wherein the master control model has six loops: three position loops of X, Y and Z and three attitude loops of pitch angle, yaw angle and roll angle, wherein each loop adopts an active disturbance rejection controller according to a fault coefficient b k Adjusting parameter b in active disturbance rejection controller in real time 0 ；
Step S5: z from observation model output 1 ，z 2 And z 3 Fault coefficient b estimated by improved Kalman filtering model k Calculating a flight control command U, wherein the specific formula is as follows:
in the formula (9), u 0 V is a set flight track signal, k is a flight control command before total disturbance compensation p Is a proportionality coefficient, k d Is a differential coefficient, k p 、k d Can be calculated by the following equations: k is a radical of p ＝ω c 2 ，k d ＝2ω c ；
And a flight control instruction U is input to a four-rotor unmanned aerial vehicle dynamic model with an execution mechanism fault description so as to realize a set flight task, ensure that fault-tolerant control is carried out in time after a fault occurs, and ensure the stable flight of the four-rotor unmanned aerial vehicle. The master control model is determined by the method, so that the response speed, robustness and stability of the master control model are improved.
Example 2
As shown in FIG. 5, in order to verify the fault coefficient b estimated by the modified Kalman filtering model k The actual values of the fault coefficients of the four executing mechanisms are all b in the simulation experiment k =0.2, estimated using the modified kalman filter model described in example 1. As can be seen from fig. 5, the estimated fault coefficient is substantially consistent with the set real fault value near the mean value 0.2 of the four estimated fault values of the execution mechanisms calculated by the improved kalman filtering model, which proves that the improved kalman filtering model can accurately estimate the fault coefficient of the execution mechanisms, and provides for the next step of accurately estimating the total disturbance by the observation model.
Example 3
As shown in fig. 6, a simulation experiment is performed on a trajectory tracking task of a quad-rotor unmanned aerial vehicle with an actuator failure by using the fault-tolerant control structure and the control method of the quad-rotor unmanned aerial vehicle according to embodiment 1. Outputting in three positions of the four-rotor unmanned aerial vehicle: the reference values of X, Y and Z are all 1, the reference values of a pitch angle and a roll angle are 0, the reference value of a yaw angle is 1, and the fault coefficients of four actuating mechanisms of the four-rotor unmanned aerial vehicle are all set to be 0.2. According to fig. 6, the four-rotor unmanned aerial vehicle realizes a good trajectory tracking effect under the condition of an actuator failure, and realizes accurate tracking of a set value at t =10s, which illustrates that the fault-tolerant control structure and the control method of the four-rotor unmanned aerial vehicle provided by embodiment 1 have a good practical effect.
In summary, according to the fault-tolerant control structure and control method for the four-rotor unmanned aerial vehicle provided by the embodiment, fault-tolerant control of the four-rotor unmanned aerial vehicle is realized by constructing a power model with an execution mechanism fault description, an observation model, an improved kalman filter model and a master control model. Specifically, when the four-rotor unmanned aerial vehicle executing mechanism breaks down, the improved Kalman filtering model can quickly and accurately estimate the fault coefficient and input the fault coefficient to the observation model, and the observation model can accurately estimate the system state and the total disturbance according to the fault coefficient. The parameters of the master control model are adjusted according to the fault coefficients estimated by the improved Kalman filtering model, the stability and the anti-interference performance of the system are improved, and the aircraft can keep relatively stable flight when a fault occurs, so that the fault-tolerant control effect is achieved.
The present embodiment is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention should be included in the present embodiment within the protection scope of the present invention.
Claims (2)
1. An aircraft fault-tolerant control architecture based on active disturbance rejection control, the architecture comprising:
the system comprises a dynamic model with an execution mechanism fault description, an improved Kalman filtering model, an observation model and a master control model;
the input of the dynamic model with the executing mechanism fault description is a flight control command U, and the output is the flight attitude y at the current moment;
the improved Kalman filtering model is built according to a dynamic model with an execution mechanism fault description and used as a prefiltering stage, state signals needed to be used in the observation model are calculated, and the input of the improved Kalman filtering model comprises the following steps: the output of the improved Kalman filtering model is a fault coefficient estimated value b k And system state estimation
The inputs to the observation model include: fault coefficient estimated value b calculated by improved Kalman filtering model k And system state estimation
The output of the observation model comprisesz 1 ，z 2 And z 3 Wherein: z is a radical of 1 Is->
An observed value of z 2 Is->
Observed value of derivative, z 3 Is the observed value of the total disturbance;
the total control model outputs z according to the observation model 1 ，z 2 And z 3 And a fault coefficient estimation value b output by the improved Kalman filtering model k And system state estimation
And calculating the flight control instruction U, and inputting the flight control instruction U to the four-rotor unmanned aerial vehicle dynamic model with the executing mechanism fault description to realize a set flight task, so that fault-tolerant control is ensured to be carried out in time after a fault occurs, and stable flight of the four-rotor unmanned aerial vehicle is realized.
2. The method for controlling an active disturbance rejection control based aircraft fault tolerant control architecture according to claim 1, comprising the steps of:
step S1: constructing a dynamic model with the fault description of the actuating mechanism, wherein the specific formula is as follows:
in formula (1), U k ＝diag[u 1k u 2k u 3k u 4k ]，x k 、u k 、y k Respectively a state variable, a control input variable, an output variable, A k 、B k 、C k Then it is the corresponding coefficient matrix coefficient,
and v k+1 Is uncorrelated white Gaussian noise;
step S2: building the improved Kalman filtering model according to the dynamic model with the actuator fault description, wherein the improved Kalman filtering model adopts a two-stage Kalman filtering algorithm as a prefiltering stage and is used for estimating actuator fault coefficients and aircraft state information;
the two-stage Kalman filtering algorithm specifically comprises the following steps:
step S21: first, neglecting the bias term, construct a filter without bias, as shown in the following formula:
in equation (2):
for ignoring the state estimate at time k when the bias is ignored, <' >>
For the state estimate at the predicted k +1 time when the offset is ignored, <' >>
Neglecting the state estimate of the bias for the k +1 th time, V k+1|k Predicting the offset compensation factor at the time k +1 for the kth time, calculated from the coupling equation, and->
For a filter gain, <' >>
The estimated value of the fault coefficient at the kth moment is obtained;
step S22: considering the offset, a bias filter is constructed to compensate the unbiased filter, thereby reconstructing the original system state, as shown in the following formula:
in the formula (3), the first and second groups,
for the compensation filter gain at the time k +1>
Predicting the covariance of the compensated filter at time k +1 for time k k+1|k For the conversion matrix from the current state into a measurement, calculated from the coupling equation, ->
For the compensated filter covariance at the k +1 th instant, is>
To ignore the error covariance, R, of the k +1 moment predicted at the k moment when biasing k+1 Is the noise covariance;
step S23: carrying out linear combination according to the unbiased filter and the biased filter to obtain an optimal state estimation value, and obtaining an original state estimation value and an error covariance matrix through compensation, wherein the following formula is shown:
in the formula (4), the first and second groups,
is the state estimate at time k +1, V k+1|k+1 Is the offset compensation coefficient at the k +1 th moment and is calculated by a coupling equation, P k+1|k+1 Is the error covariance at time k +1, device for selecting or keeping>
Is the fault coefficient estimate at the time k +1>
Error covariance at the k +1 th moment when bias is ignored;
and step S3: the observation model estimates a fault coefficient b according to the improved Kalman filtering model k And system state
Estimating the total disturbance of the system, wherein the observation model is described according to the following formula:
in the formula (5), b k Fault coefficients estimated for the improved kalman filter model,
for the system state, beta, estimated by the improved Kalman Filter model 1 Is an adjustable observation gain one, beta of the observation model 2 Is the adjustable observation gain two, beta of the observation model 3 An adjustable observation gain of three, z for the observation model 1 Is->
An observed value of z 2 Is->
Observed value of derivative, z 3 Is the observed value of the total disturbance;
and step S4: estimating a fault coefficient b according to the improved Kalman filtering model k And z observed by the observation model 1 ，z 2 And z 3 Designing the master control model, wherein the master control model has six loops: three position loops of X, Y and Z and three attitude loops of pitch angle, yaw angle and roll angle, wherein each loop adopts an active disturbance rejection controller according to a fault coefficient b k Adjusting parameter b in active disturbance rejection controller in real time 0 ；
Step S5: z output from observation model 1 ，z 2 And z 3 Fault coefficient b estimated by improved Kalman filtering model k Designing the active disturbance rejection controller for calculating the flight control command U, wherein a specific formula is as follows:
in the formula (6), u 0 V is a set flight track signal, k is a flight control command before total disturbance compensation p For adjustable proportionality coefficient, k d Is an adjustable differential coefficient;
and the flight control command U is input to a four-rotor unmanned aerial vehicle dynamic model with an execution mechanism fault description so as to realize a set flight task, so that fault-tolerant control is timely performed after a fault occurs, and stable flight of the four-rotor unmanned aerial vehicle is realized.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211673317.2A CN115877717B (en) | 2022-12-26 | 2022-12-26 | Aircraft fault-tolerant control structure and control method based on active disturbance rejection control |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211673317.2A CN115877717B (en) | 2022-12-26 | 2022-12-26 | Aircraft fault-tolerant control structure and control method based on active disturbance rejection control |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115877717A true CN115877717A (en) | 2023-03-31 |
| CN115877717B CN115877717B (en) | 2023-06-13 |
Family
ID=85754557
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211673317.2A Active CN115877717B (en) | 2022-12-26 | 2022-12-26 | Aircraft fault-tolerant control structure and control method based on active disturbance rejection control |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115877717B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116520698A (en) * | 2023-05-04 | 2023-08-01 | 中国人民解放军国防科技大学 | Carrier rocket executing mechanism fault-tolerant control method based on extended Kalman filter |
| CN117572779A (en) * | 2024-01-15 | 2024-02-20 | 北京航空航天大学杭州创新研究院 | Control method of electric aeroengine under blade damage |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103135553A (en) * | 2013-01-21 | 2013-06-05 | 南京航空航天大学 | Four-rotor aircraft fault-tolerant control method |
| CN105353615A (en) * | 2015-11-10 | 2016-02-24 | 南京航空航天大学 | Active fault tolerance control method of four-rotor aircraft based on sliding-mode observer |
| CN110888451A (en) * | 2019-12-20 | 2020-03-17 | 国网山西省电力公司电力科学研究院 | Fault-tolerant control method and system for multi-rotor unmanned aerial vehicle |
| CN112346470A (en) * | 2020-10-29 | 2021-02-09 | 中国地质大学（武汉） | Four-rotor attitude control method based on improved active disturbance rejection control |
| CN112394739A (en) * | 2020-10-29 | 2021-02-23 | 南京航空航天大学 | Active-deformation active-disturbance-rejection flight control method for four-rotor aircraft |
| CN113204191A (en) * | 2021-04-25 | 2021-08-03 | 西安工业大学 | Active disturbance rejection fault-tolerant control method for unmanned helicopter |
-
2022
- 2022-12-26 CN CN202211673317.2A patent/CN115877717B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103135553A (en) * | 2013-01-21 | 2013-06-05 | 南京航空航天大学 | Four-rotor aircraft fault-tolerant control method |
| CN105353615A (en) * | 2015-11-10 | 2016-02-24 | 南京航空航天大学 | Active fault tolerance control method of four-rotor aircraft based on sliding-mode observer |
| CN110888451A (en) * | 2019-12-20 | 2020-03-17 | 国网山西省电力公司电力科学研究院 | Fault-tolerant control method and system for multi-rotor unmanned aerial vehicle |
| CN112346470A (en) * | 2020-10-29 | 2021-02-09 | 中国地质大学（武汉） | Four-rotor attitude control method based on improved active disturbance rejection control |
| CN112394739A (en) * | 2020-10-29 | 2021-02-23 | 南京航空航天大学 | Active-deformation active-disturbance-rejection flight control method for four-rotor aircraft |
| CN113204191A (en) * | 2021-04-25 | 2021-08-03 | 西安工业大学 | Active disturbance rejection fault-tolerant control method for unmanned helicopter |
Non-Patent Citations (5)
| Title |
|---|
| 刘栩粼;郭玉英;: "四旋翼无人机时延模糊自抗扰容错控制", 测控技术, no. 01 * |
| 姜海涛;常青;王耀力;: "改进EKF的自抗扰飞控系统设计", 电子技术应用, no. 04 * |
| 程;陈增强;孙明玮;孙青林;: "多变量逆解耦自抗扰控制及其在精馏塔过程中的应用", 自动化学报, no. 06 * |
| 谢梦雷;魏先利;王欢;: "基于自抗扰的无人飞行器舵面损伤被动容错控制", 战术导弹技术, no. 06 * |
| 陈增强;程;孙明玮;孙青林;: "线性自抗扰控制理论及工程应用的若干进展", 信息与控制, no. 03 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116520698A (en) * | 2023-05-04 | 2023-08-01 | 中国人民解放军国防科技大学 | Carrier rocket executing mechanism fault-tolerant control method based on extended Kalman filter |
| CN116520698B (en) * | 2023-05-04 | 2024-06-04 | 中国人民解放军国防科技大学 | Carrier rocket executing mechanism fault-tolerant control method based on extended Kalman filter |
| CN117572779A (en) * | 2024-01-15 | 2024-02-20 | 北京航空航天大学杭州创新研究院 | Control method of electric aeroengine under blade damage |
| CN117572779B (en) * | 2024-01-15 | 2024-03-26 | 北京航空航天大学杭州创新研究院 | Control method of electric aeroengine under blade damage |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115877717B (en) | 2023-06-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110531777B (en) | Four-rotor aircraft attitude control method and system based on active disturbance rejection control technology | |
| Liu et al. | Anti-saturation adaptive finite-time neural network based fault-tolerant tracking control for a quadrotor UAV with external disturbances | |
| Shao et al. | Robust back-stepping output feedback trajectory tracking for quadrotors via extended state observer and sigmoid tracking differentiator | |
| Liang et al. | Observer-based H∞ fault-tolerant attitude control for satellite with actuator and sensor faults | |
| CN115877717A (en) | Aircraft fault-tolerant control structure and control method based on active disturbance rejection control | |
| CN106773679B (en) | A kind of spacecraft fault tolerant control method based on angular speed observer | |
| CN105912009B (en) | Four-rotor aircraft control method based on pole allocation and fuzzy active disturbance rejection control technology | |
| CN108803317A (en) | Adaptive multivariable quadrotor drone finite time fault tolerant control method | |
| Edwards et al. | Sliding mode methods for fault detection and fault tolerant control | |
| Hu et al. | Fuzzy reliable tracking control for flexible air-breathing hypersonic vehicles | |
| Zhang et al. | Nonlinear disturbance observer based adaptive super-twisting sliding mode control for generic hypersonic vehicles with coupled multisource disturbances | |
| CN113128035B (en) | Civil aircraft flight control sensor signal reconstruction fault-tolerant control method | |
| CN111880410A (en) | Four-rotor unmanned aerial vehicle fault-tolerant control method for motor faults | |
| Shao et al. | Input-and-measurement event-triggered control for flexible air-breathing hypersonic vehicles with asymmetric partial-state constraints | |
| Gai et al. | Dynamic Event-Triggered Hᵢ/H∞ Optimization Approach to Fault Detection for Unmanned Aerial Vehicles | |
| Zhang et al. | Control surface faults neural adaptive compensation control for tailless flying wing aircraft with uncertainties | |
| CN115857529A (en) | Actuator fault reconstruction method of spacecraft attitude control system | |
| Han et al. | Quadratic-Kalman-filter-based sensor fault detection approach for unmanned aerial vehicles | |
| Liang et al. | Back-stepping fault-tolerant control for morphing aircraft based on fixed-time observer | |
| Wang et al. | Hybrid modeling based double-granularity fault detection and diagnosis for quadrotor helicopter | |
| Nemati et al. | Output feedback sliding mode control of quadcopter using IMU navigation | |
| CN113961010A (en) | Four-rotor plant protection unmanned aerial vehicle tracking control method based on anti-saturation finite time self-adaptive neural network fault-tolerant technology | |
| Qin et al. | Two-layer formation-containment fault-tolerant control of fixed-wing UAV swarm for dynamic target tracking | |
| Vanek et al. | Robust model matching for geometric fault detection filters: A commercial aircraft example | |
| Xu et al. | Adaptive finite‐time attitude tracking control of quadrotor under actuator faults and external disturbances with guaranteed performance |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |