CN112631129B - Fault-tolerant flight control method and system for elastic aircraft - Google Patents
Fault-tolerant flight control method and system for elastic aircraft Download PDFInfo
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Abstract
The invention relates to a fault-tolerant flight control method and system for an elastic aircraft. Establishing a basic reference controller according to the elastic aircraft health model, and establishing unknown input state observers capable of respectively describing different fault modes according to the elastic aircraft fault model; determining fault information according to a residual error between the state observer and an output state value of the original airplane model; determining real-time fault parameters by adopting a self-adaptive method according to the fault information; determining a reconstructed control law according to the real-time fault parameter values, and further determining a reconstructed controller according to the reconstructed control law; determining a Lyapunov function of the overall augmentation system, analyzing the stability, and further determining a reconstruction control gain self-adaptive model; adjusting the adaptive rate of the control gain according to the determined reconstruction control gain adaptive model and the flight instruction; the invention can effectively recover the original flight control performance of the elastic airplane under the conditions of actuator failure and strong gust interference.
Description
Technical Field
The invention relates to the field of airplane flight control, in particular to a fault-tolerant flight control method and system for an elastic airplane.
Background
The large commercial aircraft industry is a typical strategic industry with high technology, high added value and high risk, which is knowledge-intensive, technology-intensive and capital-intensive, and is a centralized embodiment of the comprehensive strength of the national industrial and technological level. The flight control system is the most important component of modern commercial aircraft and plays a decisive role in the flight performance and safety of the aircraft. Modern advanced aircraft have strict performance requirements due to increasingly complex and large system structures, and the reliability of the modern advanced aircraft becomes the first problem to be considered in the design of a flight control system. Once the airplane fails or is accidentally damaged, if the flight control system can rapidly change the control strategy according to the fault characteristics and degree, the minimum safety requirement of the airplane is realized through the adjustment or reconstruction of the control system, and the method has important significance for ensuring the continuous execution or safe return of the airplane flight mission. Therefore, the flight control system of the commercial aircraft should have a strong fault tolerance capability to meet the requirement of high reliability.
The organism design of new generation commercial aircraft adopts the high aspect ratio wing, uses combined material to alleviate fuselage weight simultaneously, and this leads to novel commercial aircraft to possess the characteristic that traditional aircraft did not possess, specifically includes: firstly, the traditional fault-tolerant flight control system of the airplane is designed on the assumption that the airplane model is a rigid body model, and along with the enhancement of the structural elasticity of the novel commercial airplane wing, the interaction among rigid body dynamics, structural dynamics and aerodynamics becomes very strong, so that adverse aeroelastic influence can be generated. The addition of the elastic mode and the state quantity thereof can greatly improve the complexity of the model, thereby increasing the design difficulty of the fault-tolerant flight control system. Secondly, the aeroelasticity can cause the aircraft to be unstable, generate unexpected vibration and even cause structural failure. Further, vibration can be coupled with the self-fault of the airplane, and the influence on the stability and the flight performance of the airplane is increased. This makes the design of a fault tolerant flight control system more difficult. The increase of the elasticity of the structure of the airplane wing can cause the change of the shape of the airplane wing, which seriously affects the measurement precision of a sensor positioned on the airplane wing. Since the design of fault diagnosis and reconfiguration control requires the use of accurate sensor measurement signals, designing a fault-tolerant flight control system of a flexible aircraft presents more serious challenges.
Disclosure of Invention
The invention aims to provide a fault-tolerant flight control method and a fault-tolerant flight control system for an elastic aircraft, so that the original flight control performance of the elastic aircraft can be effectively recovered under the conditions of actuator faults and strong gust interference.
In order to achieve the purpose, the invention provides the following scheme:
a fault-tolerant flight control method for a flexible aircraft comprises the following steps:
establishing an elastic aircraft health and fault model in a state space form by adopting a Lagrange energy equation; the elastic aircraft health and fault model comprises an elastic aircraft health model and an elastic aircraft fault model;
establishing a basic reference controller according to the elastic aircraft health model, and establishing unknown input state observers capable of respectively describing different fault modes according to the elastic aircraft fault model;
determining fault information according to a residual difference between the output state value of the state observer and the output state value of the original aircraft model;
determining real-time fault parameters by adopting a self-adaptive method according to the fault information;
according to the real-time fault parameter value, adopting a self-adaptive model following strategy, regarding an augmentation system consisting of an elastic aircraft health model and a basic reference controller in the elastic aircraft health and fault model as a reference model, determining a reconstructed control law, and further determining a reconstructed controller according to the reconstructed control law;
regarding the original aircraft model, the reference model, the state observer and the reconstructed controller as a total augmentation system, determining a Lyapunov function of the total augmentation system, performing stability analysis, and further determining a reconstructed control gain adaptive model;
adjusting the adaptive rate of the control gain according to the determined reconstruction control gain adaptive model and the flight instruction; the flight instructions include angle of attack, pitch angle rate, wing root bending moment, and wing root torsion moment.
Optionally, the establishing a base reference controller according to the elastic aircraft health model, and the establishing unknown input state observers capable of respectively describing different fault modes according to the elastic aircraft fault model specifically include:
using formulas
Determining the base reference controller;
using the formula u-Kxx+KcxcDetermining a control law of the base reference controller;
using formulas
And
determining an unknown input state observer;
wherein the content of the first and second substances,
for the state of the base reference controller, R (t) e RmFor flight control system reference commands, AcAnd BcFor a matrix of suitable dimensions, y (t) e RmRepresenting the measured output vector, KxAnd KcFor normal control of gain, wi(t)∈RnRepresenting the observer state vector, y ∈ RmDenotes the measurement output, u ∈ RpA control input is represented that is a function of,
representing the estimated value of the state vector, Bi＝BLi，Li＝diag{l1,l2,…,lp}， li∈[0,1]，biThe ith column, M, G, N, H, representing B, is the matrix of the state observer.
Optionally, the determining the fault information according to the residual error between the output state value of the state observer and the output state value of the original aircraft model specifically includes:
using formulas
Establishing a fault diagnosis side decision algorithm;
wherein, c1> 0 is the weighting of the instantaneous residual signal, c2More than 0 is the weighting of the past residual signal, lambda is more than 0 is the forgetting factor for determining index memory, v is the number of the state observers, tau is the integral variable, ri(t) is a residue.
Optionally, the determining, according to the fault information, a real-time fault parameter by using a self-adaptive method specifically includes:
using formulas
And
determining a self-adaptive equation of a real-time fault parameter;
wherein alpha isi＞0，βi> 0 is the adaptation rate, biIs the ith column, u, of the known matrixiFor the (i) th input(s),
for the ith actuator additive failure,
for the efficiency of the ith actuator, P is the symmetric positive definite matrix, M is the Hurwitz matrix, M is theTP + PM ═ Q, Q is a positive definite matrix.
Optionally, the determining a reconstructed control law by using an adaptive model following strategy according to the real-time fault parameter value and taking an augmentation system formed by an elastic aircraft health model and a basic reference controller in the elastic aircraft health and fault model as a reference model, and then determining the reconstructed controller according to the reconstructed control law specifically includes:
using the formula up＝Keemp+Kmxm+Kffm+ΘdDetermining a reconstructed control law;
wherein, Km，KfAnd ΘdAre all control gains, upFor the reconstructed control law, empAs an error between the object model and the reference model, fmIs a virtual multiplicative fault.
A resilient aircraft fault tolerant flight control system comprising:
the elastic aircraft health and fault model building module is used for building an elastic aircraft health and fault model in a state space form by adopting a Lagrange energy equation; the elastic aircraft health and fault model comprises an elastic aircraft health model and an elastic aircraft fault model;
the state observer and basic reference controller establishing module is used for establishing a basic reference controller according to the elastic aircraft health model and establishing unknown input state observers capable of respectively describing different fault modes according to the elastic aircraft fault model;
the fault information determination module is used for determining fault information according to a residual error between the output state value of the state observer and the output state value of the original airplane model;
the real-time fault parameter determining module is used for determining real-time fault parameters by adopting a self-adaptive method according to the fault information;
the reconstructed controller determining module is used for determining a reconstructed control law by taking an augmentation system consisting of an elastic aircraft health model and a basic reference controller in the elastic aircraft health and fault model as a reference model according to the real-time fault parameter value and adopting an adaptive model following strategy, and further determining the reconstructed controller according to the reconstructed control law;
a reconstruction control gain adaptive model determining module, configured to regard the original aircraft model, the reference model, the state observer, and the reconstructed controller as a total augmentation system, determine a Lyapunov function of the total augmentation system, perform stability analysis, and further determine a reconstruction control gain adaptive model;
the control gain self-adaptive rate adjusting module is used for adjusting the control gain self-adaptive rate according to the determined reconstruction control gain self-adaptive model and the flight instruction; the flight instructions include angle of attack, pitch angle rate, wing root bending moment, and wing root torsion moment.
Optionally, the state observer and base reference controller establishing module specifically includes:
a base reference controller determination unit for utilizing a formula
Determining the base reference controller;
a control law determination unit of the base reference controller for using the formula u ═ Kxx+KcxcDetermining a control law of the base reference controller;
a state observer determination unit for utilizing a formula
And
determining a state observer of the unknown input;
wherein the content of the first and second substances,
for the state of the base reference controller, R (t) e RmFor flight control system reference commands, AcAnd BcFor a matrix of suitable dimensions, y (t) e RmRepresenting the measured output vector, KxAnd KcFor normal controlGain, wi(t)∈RnRepresenting the observer state vector, y ∈ RmDenotes the measurement output, u ∈ RpA control input is represented that is a function of,
representing the estimated value of the state vector, Bi＝BLi，Li＝diag{l1,l2,…,lp}， li∈[0,1]，biThe ith column, M, G, N, H, representing B, is the matrix of the state observer.
Optionally, the fault information determining module specifically includes:
a fault diagnosis side-decision algorithm establishing unit for utilizing a formula
Establishing a fault diagnosis side decision algorithm;
wherein, c1> 0 is the weighting of the instantaneous residual signal, c2More than 0 is the weighting of the past residual signal, lambda is more than 0 is the forgetting factor for determining index memory, v is the number of the state observers, tau is the integral variable, ri(t) is a residue.
Optionally, the real-time fault parameter determining module specifically includes:
real-time adaptive equation determination unit for fault parameters using equations
And
determining a self-adaptive equation of a real-time fault parameter;
wherein alpha isi＞0，βi> 0 is the adaptation rate, biIs the ith column, u, of the known matrixiFor the (i) th input(s),
for the ith actuator additive failure,
for the efficiency of the ith actuator, P is the symmetric positive definite matrix, M is the Hurwitz matrix, M is theTP + PM ═ Q, Q is a positive definite matrix.
Optionally, the reconstructed controller determining module specifically includes:
a reconstructed control law determining unit for using the formula up＝Keemp+Kmxm+Kffm+ΘdDetermining a reconstructed control law;
wherein, Km，KfAnd ΘdAre all control gains, upFor the reconstructed control law, empAs an error between the object model and the reference model, fmIs a virtual multiplicative fault.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides a fault-tolerant flight control method and system for an elastic aircraft. And then, fault diagnosis is carried out on actuator faults possibly occurring in the elastic aircraft by utilizing a multi-model structure and a self-adaptive technology, and a detection and isolation result and real-time fault value estimation are given. The method has the advantages that real-time fault information is added into a reconstruction control law, the stability of an overall system formed by the airplane, the controller and the diagnosis module is analyzed, an output feedback strategy is utilized, a self-adaptive expression of control gain is given, the problem that the traditional rigid airplane cannot effectively process coupling between rigid flying motion and elastic structure load is solved, the problem that the internal mutual interference of the traditional rigid airplane and the elastic structure load cannot be effectively solved by independently designing a fault diagnosis and control reconstruction scheme is solved, and the survival capability of the novel elastic airplane when the novel elastic airplane encounters faults in flight is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive labor.
FIG. 1 is a schematic flow chart of a method for controlling fault-tolerant flight of a flexible aircraft according to the present invention;
FIG. 2 is a general flowchart of a fault-tolerant flight control method for a flexible aircraft according to the present invention;
FIG. 3 is a system block diagram of a method provided by the present invention;
FIG. 4 is a schematic diagram illustrating the method of the present invention applied to the diagnosis of actuator failure;
FIG. 5 is a schematic diagram illustrating the method of the present invention applied to the diagnosis of the stuck-at fault of the actuator;
FIG. 6 is a schematic diagram illustrating the method of the present invention applied to the diagnosis of damage failure of an actuator;
FIG. 7 is a schematic view of the structural loading of a faultless aircraft in which the method of the present invention is applied;
FIG. 8 is a schematic view of the flight attitude of a non-fault aircraft to which the method of the present invention is applied;
FIG. 9 is a schematic diagram of structural loads applied to damage faults of an aircraft actuator by the method provided by the invention;
FIG. 10 is a schematic diagram of a flight attitude of an aircraft actuator damage fault to which the method of the present invention is applied;
FIG. 11 is a schematic diagram of the structural loading applied to an aircraft actuator stuck fault by the method of the present invention;
FIG. 12 is a schematic diagram of a flight attitude of an aircraft actuator stuck fault to which the method of the present invention is applied;
FIG. 13 is a schematic diagram of structural loading applied to flutter failure of an aircraft actuator by the method of the present invention;
FIG. 14 is a schematic diagram of the flight attitude of an aircraft actuator flutter fault according to the method of the present invention;
fig. 15 is a schematic structural diagram of a fault-tolerant flight control system of a flexible aircraft according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a fault-tolerant flight control method and a fault-tolerant flight control system for an elastic aircraft, so that the original flight control performance of the elastic aircraft can be effectively recovered under the conditions of actuator faults and strong gust interference.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
The principle of the invention is as follows: firstly, a state space form elastic aircraft health and fault model is established by using Lagrange energy equation. Based on the model, a group of unknown input observers capable of describing different fault modes respectively are established, and then residuals between an original system and the group of observers are compared respectively to determine the fault type. And further designing an augmented system comprising an ideal system and a corresponding controller as a reference system, wherein the output of the augmented system is used as the reference input of an actual system, namely, a control target is reconstructed to enable the output of the system after the fault to track the output of the ideal system. The real-time accurate fault information provided by the fault estimation part is needed when the reconstruction controller is designed, so that an original fault system, the reconstruction controller and a fault estimation module are regarded as an expanded overall system, a Lyapunov function is searched for stability analysis of the overall system, and a self-adaptive expression of fault estimation and control gain is obtained by using a self-adaptive technology, so that the lowest flight task requirement can be met under the condition that the elastic aircraft encounters actuator faults and strong gust wind interference.
Fig. 1 is a schematic flow chart of a fault-tolerant flight control method of a flexible aircraft according to the present invention, and fig. 2 is an overall flow chart of the fault-tolerant flight control method of the flexible aircraft according to the present invention, as shown in fig. 1 and fig. 2, the fault-tolerant flight control method of the flexible aircraft according to the present invention includes:
s101, establishing an elastic aircraft health and fault model in a state space form by adopting a Lagrange energy equation; the elastic aircraft health and fault model comprises an elastic aircraft health model and an elastic aircraft fault model;
s102, establishing a basic reference controller according to the elastic aircraft health model, and establishing unknown input state observers capable of respectively describing different fault modes according to the elastic aircraft fault model;
s102 specifically comprises the following steps:
using formulas
Determining the base reference controller;
using the formula u-Kxx+KcxcDetermining a control law of the base reference controller;
using formulas
And
determining a state observer of the unknown input;
wherein the content of the first and second substances,
for the state of the base reference controller, R (t) e RmFor flight control system reference commands, AcAnd BcFor a matrix of suitable dimensions, y (t) e RmRepresenting the measured output vector, KxAnd KcFor normal control of gain, wi(t)∈RnRepresenting the observer state vector, y ∈ RmDenotes the measurement output, u ∈ RpA control input is represented that is a function of,
representing the estimated value of the state vector, Bi＝BLi，Li＝diag{l1,l2,…,lp}， li∈[0,1]，biThe ith column, M, G, N, H, representing B, is the matrix of the state observer.
S103, determining fault information according to a residual error between the output state value of the state observer and the output state value of the original airplane model;
s103 specifically comprises the following steps:
using formulas
Establishing a fault diagnosis side decision algorithm;
wherein, c1> 0 is the weighting of the instantaneous residual signal, c2More than 0 is the weighting of the past residual signal, lambda is more than 0 is the forgetting factor for determining index memory, v is the number of the state observers, tau is the integral variable, ri(t) is a residue.
When a certain residual error corresponds to SiAnd (t) when the fault type is less than a given threshold (given according to the actual situation), the fault type described by the observer occurs in the original elastic aircraft system, and the fault diagnosis is finished.
S104, determining real-time fault parameters by adopting a self-adaptive method according to the fault information;
s104 specifically comprises the following steps:
using formulas
And
determining a self-adaptive equation of a real-time fault parameter;
wherein alpha isi＞0，βi> 0 is the adaptation rate, biIs the ith column, u, of the known matrixiFor the (i) th input(s),
for the ith actuator additive failure,
for the efficiency of the ith actuator, P is the symmetric positive definite matrix, M is the Hurwitz matrix, M is theTP + PM ═ Q, Q is a positive definite matrix.
S105, according to the real-time fault parameter value, adopting a self-adaptive model following strategy, regarding an augmentation system formed by an elastic aircraft health model and a basic reference controller in the elastic aircraft health and fault model as a reference model, determining a reconstructed control law, and further determining a reconstructed controller according to the reconstructed control law;
s105 specifically comprises the following steps:
using the formula up＝Keemp+Kmxm+Kffm+ΘdDetermining a reconstructed control law;
wherein, Km，KfAnd ΘdAre all control gains, upFor the reconstructed control law, empAs an error between the object model and the reference model, fmIs a virtual multiplicative fault.
Selecting a suitable gain Km，KfAnd ΘdMake A am-Ap-BpKm＝0，BpKf+B p0 and BpΘd+Bpfa+Epd is 0, the difference e between the original system state and the state estimation value given by the observermpCan be written as
At this time, an appropriate gain K is selectedeCan make A bep-BpKeIs Hurwitz.
S106, regarding the original aircraft model, the reference model, the state observer and the reconstructed controller as a total augmentation system, determining a Lyapunov function of the total augmentation system, performing stability analysis, and further determining a reconstruction control gain adaptive model;
the following Lyapunov function was chosen:
wherein the content of the first and second substances,
PI＝diag{Ppo,Pmpis positive definite matrix, phi is Ke(t)-Ke(0)，Ψ＝Km(t)-Km(0)，Λ＝Kf(t)-Kf(0)，ξ＝Θd(t)-Θd(0)， αi＞0，βi＞0，Γ1，Γ2，Γ3And Γ4Is a positive definite matrix.
Through stability analysis, the expression of the reconstruction control gain is given as:
wherein e ismpRepresenting the state error between the actual faulty system and the reference model.
S107, adjusting the adaptive rate of the control gain according to the determined reconstruction control gain adaptive model and the flight instruction; the flight instructions include angle of attack, pitch angle rate, wing root bending moment, and wing root torsion moment.
The method not only uses flight maneuvering parameters (attack angle, pitch angle and pitch angle rate) as the basis for adjusting the controller and the observer, but also adjusts the gain according to the aerodynamic load parameters (wing root bending moment and wing root torsion moment). The controller and the observer are optimized from the aspects of dynamics and aeroelasticity respectively.
The following provides a specific embodiment to further illustrate the scheme of the present invention, and the specific process comprises the following steps:
step 1: designing a basic reference controller for the healthy elastic aircraft model;
a post-fault system model is first established. Consider the following fault-free linear time invariant system
y(t)＝Cx(t)+Dgwg(t)；
Wherein x (t) e RnRepresents the system state vector, u (t) e RpRepresenting a control input vector, wg(t)∈RqRepresents the external excitation vector, d (t) e RqRepresents the external interference vector, y (t) e RmRepresenting the measurement output vector. A, B, C and E are known matrices with suitable dimensions. B isgIs a matrix, D, that calculates the effect of external excitations on the kinetic modelgRepresenting the effect of an external stimulus on the output. To avoid loss of generality, assume that the known unknown input interference matrix E is column-full rank, otherwise the following rank decomposition method can be used to decompose the matrix E:
Ed(t)＝E1E2d(t)；
wherein E is1Is a column full rank matrix, E2d (t) can be considered as a new unknown input.
For a fault-free system, its normal controller equations may be set as:
wherein the content of the first and second substances,
denotes controller state, R (t) ε RmRepresenting a system reference instruction, AcAnd BcIs a matrix with suitable dimensions. An augmentation system capable of obtaining a controlled object by combining the system and the controller:
wherein, it is provided with
yn＝y，
And Cn＝[C 0]。
The following basic control laws were designed:
u＝Kxx+Kcxc；
Kxand KcFor normal control of the gain, it can be determined by a linear optimization method.
Step 2: aiming at elastic aircraft models with different actuator faults, a group of state observers are designed, so that the elastic aircraft models are sensitive to the faults and robust to external disturbance (gust and the like). And then, respectively comparing the system state estimation value output of each observer with the original system state value, if the residual error generated by the system and a certain observer tends to zero (or is smaller than a certain given threshold), performing the step 3, and otherwise, continuing to perform the step 2. The diagnostic results are shown in fig. 4 to 6, where the diagnostic logic needs to be introduced as follows:
diagnostic logic 1: when the designed diagnostic algorithm is used for fault diagnosis of the object system, firstly, the residual error output of a Normal observer needs to be observed, and if the output is close to zero, the object system is a fault-free system; if the output is not close to zero, further fault diagnosis by the following diagnostic logic is required.
The estimated values (Estimation) in fig. 4 show results of estimating fault parameters (stuck positions or actuator efficiencies) of 8 types of faults, i.e., a stuck fault occurring in actuator No. 1 (LIP1), a stuck fault occurring in actuator No. 2 (LIP2), a stuck fault occurring in actuator No. 3 (LIP3), a stuck fault occurring in actuator No. 4 (LIP4), a reduced efficiency fault occurring in actuator No. 1 (LOE1), a reduced efficiency fault occurring in actuator No. 2 (LOE2), a reduced effectiveness occurring in actuator No. 3 (LOE3), and a reduced efficiency occurring in actuator No. 4 (LOE4), by using adaptive rhythms, respectively. At this time, a diagnosis logic is introduced to determine whether the fault estimation value given by the adaptive observer can be used to estimate the fault parameter of the object system.
Diagnostic logic 2: (1) if the error output of the observer is close to zero, the corresponding fault parameter estimation value can be used for fault estimation; if the error output of the observer is not close to zero, the corresponding fault parameter estimation value cannot be used for fault estimation. (2) When both the observer output residual and the failure estimation value correspond to each other, the object system must have a failure that establishes the assumption of the observer.
And step 3: and (3) aiming at the fault information judged in the step (2), calculating an expression of a specific fault parameter by using a self-adaptive method to obtain a real-time fault parameter self-adaptive equation as follows:
wherein alpha isi＞0，βiThe rate of adaptation is > 0 and can adjust and influence the rate of convergence of the adaptive estimation. r isi(t) is the residual defined in step 2. P is a symmetric positive definite matrix and is a unique solution of the following Lyapunov matrix equation:
MTP+PM＝-Q；
and 4, step 4: substituting real-time fault parameter values into the design of a reconstruction control law, using a self-adaptive model following strategy, regarding an augmented system of an ideal system and a basic controller as a reference model, wherein the output of the augmented system is the reference input of an actual system, and designing a reconstruction control gain parameter so that the output of the actual system can track the output of the reference model within a limited time, wherein the specific flow is as follows:
a reference model is first designed. According to fig. 3, the control principle of Model Reference Adaptive Control (MRAC) is: and (d) simultaneously inputting the object model P and the reference model M by the reference command r (t), and enabling the output of the object model P to quickly and accurately track the output of the reference model M by designing a control law. The reference model M comprises an ideal fault-free model and a basic controller thereof, and the expression of the reference model M is as follows:
ym＝Cmxm+Dgwg；
wherein x ism＝[x xc]T，um＝r，ym＝y，
Cm＝[C 0]。
The reference system M comprises both an ideal fault-free system model and a basic controller, and for a system considering structure elasticity, an external excitation vector is regarded as an input influencing the system structure and not only interference, so that not only interference needs to be added into the reference modelThe command input r is added with external excitation wg。
Then, an object model P is designed, and the expression of the object model P is as follows:
yp＝Cpxp+Dgwg；
wherein xp＝[x xc]T，yp＝y，
Cp＝[C 0]。
The error between the object model and the reference model is defined as emp(t)＝xm(t)-xp(t), i.e. when the object model outputs the unbiased tracking reference model output, the error will tend to zero. The control law is designed as follows:
up＝Keemp+Kmxm+Kffm+Θd；
substituting the system model and the control law into an error expression to obtain:
selecting a suitable gain Km，KfAnd ΘdMake A am-Ap-BpKm＝0，BpKf+B p0 and BpΘd+Bpfa+Epd is 0, the error equation can be rewritten as
At this time, an appropriate gain K is selectedeCan make A bep-BpKeIs Hurwitz.
And 5: according to the step 3 and the step 4, the actual system, the reference model, the observer and the reconstruction controller are regarded as a total augmentation system, a Lyapunov function of the total system is searched, stability analysis is carried out, and then the adaptive expression mode of the reconstruction control gain in the step 4 is determined, wherein the expression is as follows:
step 6: and respectively analyzing the control performance of the airplane from flight performance parameters (attack angle, pitch angle and pitch angle rate) and structural load parameters (wing root bending moment and wing root torsion moment), and adjusting the control gain self-adaption rate. The results were analyzed as follows:
first, control surface δroa，δriaAnd deltareThe case of no fault is subjected to simulation verification. Fig. 7 shows the structural elastic load of the aircraft after a gust, and it can be seen from fig. 7 that the load suppression effect is significantly better when the system is closed than when the system is open. When the actuator has no fault, the Fault Tolerant (FTC) controller and the SOF controller designed by the invention can effectively restrain the structural elastic load of the airplane. FIG. 8 shows the flight maneuver attitude response of a novel green-leg aircraft (GRA), when the aircraft encounters a gust during cruising and three control surfaces of the aircraft are all in normal operation, the light solid lines show the pitch angle and the pitch angle of the aircraft under the condition of open loopRate and nose angle of attack variation; the curves with dark solid lines represent the variation of the pitch angle, the pitch angle rate and the nose attack angle of the aircraft in a closed loop using the FTC controller; the dashed lines represent the pitch angle, pitch rate, and nose-nose angle of attack variation in the aircraft in a closed loop using a SOF controller (as a control, without actuator failure effects being considered at design time). It can be seen from fig. 8 that under normal operation of the aircraft actuators, after the aircraft encounters a gust, the attitude of the aircraft can be restored to stability using both the FTC controller and the SOF controller.
Second, simulation verification was performed for the case where the right-hand outboard flap efficiency decreased by 50%. Figure 9 shows the response of the wing root bending moment and torque in the event of actuator failure. It can be seen that, since the SOF controller is designed without considering the fault information, although it can recover the attitude of the aircraft by its own robustness, the effect of suppressing the structural loads caused by gusts and reduced control surface efficiency is not significant, and as shown in fig. 9, the wing root moment generated when the aircraft uses the SOF is much larger than when the FTC is used. The fault information is considered during design of the integrally designed FTC fault-tolerant controller, so that the FTC controller can restore the flying maneuver attitude before the airplane fault and can effectively restrain structural elastic load generated by gust and actuator fault. As shown in fig. 10, when the aircraft with the actuator failure encounters a gust of wind, both the FTC controller and the SOF controller can restore the aircraft to the pre-failure attitude.
Again, simulation verification was performed for the case where the right hand outboard flap seized at 5 °. Fig. 11 shows the responses of the wing root bending moment and the torque when the actuator fails, and it can be seen that although the rigid body flight response of the two control methods is not very different when the control surface stuck fault occurs, the suppression performance of the two control methods for the structural elastic load is greatly different. Since the SOF controller is designed without considering fault information, although the SOF controller can recover the attitude of the aircraft by means of self robustness, the SOF controller has no way to effectively suppress structural loads (wing root bending moment and torque) caused by gusts and dead control surface blocking, and the flutter of the wing root part is quite obvious within 0.3s as shown in the figure, which is very unfavorable for the safety of the aircraft. The fault information is considered in the design of the FTC fault-tolerant controller integrally designed by the invention, so that the FTC controller can restore the flying maneuver attitude before the aircraft fault, and can effectively restrain the structural elastic load generated by gust and actuator faults. It can be seen from fig. 12 that when the aircraft with the actuator failure encounters a wind gust, both the FTC controller and the SOF controller can restore the aircraft to the flight attitude before the failure.
Finally, simulation verification is carried out for the situation that OFC faults occur on the right-hand outer aileron. Fig. 13 shows the response of the wing root bending moment and the torque when the actuator fails, and since the SOF controller is designed without considering the failure information, the SOF controller can restore the attitude of the aircraft by means of self robustness, but the SOF controller has lower effect of inhibiting the structure elastic load than the FTC controller. The fault information is considered in the design of the FTC fault-tolerant controller which is integrally designed, so that the FTC controller can restore the flying attitude before the airplane fault and can effectively restrain structural elastic load generated by gust and actuator fault. Unlike the previous cases, the OFC failure effect on the structural loads can only be mitigated and cannot be eliminated. It can be seen from fig. 14 that when the aircraft with the actuator failure encounters a wind gust, both the FTC controller and the SOF controller can restore the aircraft to the flight attitude before the failure.
The invention achieves the following effects:
(1) the invention solves the problem of fault detection and diagnosis considering the influence of structure elasticity. The existing fault detection and diagnosis method only considers the influence of the fault on the flight maneuverability and does not analyze the influence of the fault on the structure elasticity when analyzing the fault mode and fault modeling. Because the rigid body frequency and the elastic frequency of the novel commercial aircraft are close, unexpected flutter is easily generated after the aircraft breaks down, and then the system is unstable. Therefore, the existing fault detection and diagnosis method cannot be used for detecting and diagnosing the fault of the flexible aircraft. In addition, the increased flexibility of the wing and fuselage can cause inaccurate measurements by sensors located therein, which increases the difficulty in designing fault detection and diagnosis algorithms. The invention simultaneously analyzes the influence of the airplane fault on rigid body maneuver and structure elasticity, and establishes an airplane fault mode set as comprehensive as possible. Further, a design flow of elastic aircraft fault detection and diagnosis is given based on a multi-model self-adaptive unknown input observer technology;
(2) the reconstruction control algorithm provided by the invention solves the problem of the coupled fault-tolerant flight control of the dynamic model of the elastic aircraft. The existing fault-tolerant control method generally aims at a rigid body model, because the structural elasticity influence of a new generation of commercial aircraft is increased and the natural frequency is reduced, the coupling between rigid body motion and structural elasticity is enhanced, and the fault-tolerant control method designed based on the rigid body model cannot be directly used for designing a fault-tolerant control system of the elastic aircraft. The method uses a modeling method based on a Langcange energy equation, considers the rigid body state and the elastic mode of the airplane together to establish a dynamic model, and designs a controller by using an output feedback mode aiming at a coupling part in a system matrix.
(3) The invention solves the problem of fault-tolerant flight control of internal mutual interference existing in fault diagnosis and reconstruction control. The conventional fault-tolerant control method usually designs the fault diagnosis module and the reconstruction control module separately, but the fault diagnosis module and the reconstruction control module have an influence relationship with each other. Considering the mutual influence between fault diagnosis and reconfiguration control, it becomes more difficult to ensure that the integrated fault-tolerant control system can simultaneously handle structural coupling and diagnose reconfiguration coupling. The invention integrally designs fault diagnosis and reconstruction control by using an output feedback method based on an observer and designs a diagnosis and reconstruction integrated fault-tolerant flight control system with self-adaptive dynamic regulation capability.
Fig. 15 is a schematic structural diagram of a fault-tolerant flight control system of a flexible aircraft according to the present invention, and as shown in fig. 15, the fault-tolerant flight control system of the flexible aircraft according to the present invention includes:
the elastic aircraft health and fault model building module 1501 is used for building an elastic aircraft health and fault model in a state space form by adopting a Lagrange energy equation;
a state observer and basic reference controller establishing module 1502, configured to establish a basic reference controller according to the elastic aircraft health model, and establish unknown input state observers capable of describing different failure modes respectively according to the elastic aircraft failure model;
a fault information determination module 1503, configured to determine fault information according to a residual error between the output state value of the state observer and the output state value of the original aircraft model;
a real-time fault parameter determining module 1504, configured to determine a real-time fault parameter by using a self-adaptive method according to the fault information;
a reconstructed controller determining module 1505, configured to determine a reconstructed control law according to the reconstructed control law by using an adaptive model following strategy to regard an augmentation system formed by an elastic aircraft health model and a basic reference controller in the elastic aircraft health and fault model as a reference model according to the real-time fault parameter value;
a reconstruction control gain adaptive model determining module 1506, configured to regard the original aircraft model, the reference model, the state observer, and the reconstructed controller as a total augmentation system, determine a Lyapunov function of the total augmentation system, perform stability analysis, and further determine a reconstruction control gain adaptive model;
a control gain adaptive rate adjustment module 1507, configured to adjust a control gain adaptive rate according to the determined reconstructed control gain adaptive model and the flight command; the flight instructions include angle of attack, angle of depression, pitch rate, wing root bending moment, and wing root torsion moment.
The state observer and base reference controller establishing module 1502 specifically includes:
a base reference controller determination unit for utilizing a formula
Determining the base reference controller;
control law for base reference controllerA fixed unit for using the formula u ═ Kxx+KcxcDetermining a control law of the base reference controller;
a state observer determination unit for utilizing a formula
And
determining a state observer of the unknown input;
wherein the content of the first and second substances,
for the state of the base reference controller, R (t) e RmFor flight control system reference commands, AcAnd BcFor a matrix of suitable dimensions, y (t) e RmRepresenting the measured output vector, KxAnd KcFor normal control of gain, wi(t)∈RnRepresenting the observer state vector, y ∈ RmDenotes the measurement output, u ∈ RpA control input is represented that is a function of,
representing the estimated value of the state vector, Bi＝BLi，Li＝diag{l1,l2,…,lp}， li∈[0,1]，biThe ith column, M, G, N, H, representing B, is the matrix of the state observer.
The fault information determination module 1503 specifically includes:
a fault diagnosis side-decision algorithm establishing unit for utilizing a formula
Establishing a fault diagnosis side decision algorithm;
wherein, c1> 0 is the weighting of the instantaneous residual signal, c2More than 0 is the weighting of the past residual signal, lambda more than 0 is the forgetting factor for determining index memory, v is the number of the state observers, tau is the integral variable,ri(t) is a residue.
The real-time fault parameter determining module 1504 specifically includes:
real-time adaptive equation determination unit for fault parameters using equations
And
determining a self-adaptive equation of a real-time fault parameter;
wherein alpha isi＞0，βi> 0 is the adaptation rate, biIs the ith column, u, of the known matrixiFor the (i) th input(s),
for the ith actuator additive failure,
for the efficiency of the ith actuator, P is the symmetric positive definite matrix, M is the Hurwitz matrix, M is theTP + PM ═ Q, Q is a positive definite matrix.
The reconstructed controller determining module 1505 specifically includes:
a reconstructed control law determining unit for using the formula up＝Keemp+Kmxm+Kffm+ΘdDetermining a reconstructed control law;
wherein, Km，KfAnd ΘdAre all control gains, upFor the reconstructed control law, empAs an error between the object model and the reference model, fmIs a virtual multiplicative fault.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (2)
1. A fault-tolerant flight control method for a flexible aircraft is characterized by comprising the following steps:
establishing an elastic aircraft health and fault model in a state space form by adopting a Lagrange energy equation; the elastic aircraft health and fault model comprises an elastic aircraft health model and an elastic aircraft fault model;
establishing a basic reference controller according to the elastic aircraft health model, and establishing unknown input state observers capable of respectively describing different fault modes according to the elastic aircraft fault model;
determining fault information according to a residual error between the output state value of the state observer and the output state value of the original airplane model;
determining real-time fault parameters by adopting a self-adaptive method according to the fault information;
according to the real-time fault parameter value, adopting a self-adaptive model following strategy, regarding an augmentation system consisting of an elastic aircraft health model and a basic reference controller in the elastic aircraft health and fault model as a reference model, determining a reconstructed control law, and further determining a reconstructed controller according to the reconstructed control law;
regarding the original aircraft model, the reference model, the state observer and the reconstructed controller as a total augmentation system, determining a Lyapunov function of the total augmentation system, performing stability analysis, and further determining a reconstructed control gain adaptive model;
adjusting the adaptive rate of the control gain according to the determined reconstruction control gain adaptive model and the flight instruction; the flight instructions comprise an attack angle, a pitch angle rate, a wing root bending moment and a wing root torsion moment;
the establishing of the basic reference controller according to the elastic aircraft health model and the establishing of the unknown input state observers capable of respectively describing different fault modes according to the elastic aircraft fault model specifically comprise:
using formulas
Determining the base reference controller;
using the formula u-Kxx+KcxcDetermining a control law of the base reference controller;
using formulas
And
determining a state observer of the unknown input;
wherein the content of the first and second substances,
for the state of the base reference controller, R (t) e RmFor flight control system reference commands, AcAnd BcFor a matrix of suitable dimensions, y (t) e RmRepresenting the measured output vector, KxAnd KcFor normal control of gain, wi(t)∈RnRepresenting the observer state vector, y ∈ RmDenotes the measurement output, u ∈ RpA control input is represented that is a control input,
representing the estimated value of the state vector, Bi＝BLi，Li＝diag{l1,l2,…,lp}，li∈[0,1]，biThe ith column representing B, M, G, N and H are matrixes of the state observer;
the determining fault information according to the residual error between the output state value of the state observer and the output state value of the original aircraft model specifically includes:
using formulas
Establishing a fault diagnosis side decision algorithm;
wherein, c1> 0 is the weighting of the instantaneous residual signal, c2More than 0 is the weighting of the past residual signal, lambda is more than 0 is the forgetting factor for determining index memory, v is the number of the state observers, tau is the integral variable, ri(t) is the residual;
the determining a real-time fault parameter by a self-adaptive method according to the fault information specifically includes:
using formulas
And
determining a self-adaptive equation of a real-time fault parameter;
wherein alpha isi＞0，βi> 0 is the adaptation rate, biIs the ith column, u, of the known matrixiFor the (i) th input(s),
for the ith actuator additive failure,
for the efficiency of the ith actuator, P is the symmetric positive definite matrix, M is the Hurwitz matrix, M is theTP + PM ═ Q, Q is a positive definite matrix;
the method comprises the following steps of adopting a self-adaptive model following strategy according to the real-time fault parameter value, regarding an augmentation system formed by an elastic aircraft health model and a basic reference controller in the elastic aircraft health and fault model as a reference model, determining a reconstructed control law, and determining a reconstructed controller according to the reconstructed control law, wherein the method specifically comprises the following steps:
using the formula up＝Keemp+Kmxm+Kffm+ΘdDetermining a reconstructed control law;
wherein, Km，KfAnd ΘdAre all control gains, upFor the reconstructed control law, empAs an error between the object model and the reference model, fmIs a virtual multiplicative fault.
2. A resilient aircraft fault tolerant flight control system, comprising:
the elastic aircraft health and fault model building module is used for building an elastic aircraft health and fault model in a state space form by adopting a Lagrange energy equation;
the state observer and basic reference controller establishing module is used for establishing a basic reference controller according to the elastic aircraft health model and establishing unknown input state observers capable of respectively describing different fault modes according to the elastic aircraft fault model;
the fault information determination module is used for determining fault information according to a residual error between the output state value of the state observer and the output state value of the original airplane model;
the real-time fault parameter determining module is used for determining real-time fault parameters by adopting a self-adaptive method according to the fault information;
the reconstructed controller determining module is used for determining a reconstructed control law by taking an augmentation system consisting of an elastic aircraft health model and a basic reference controller in the elastic aircraft health and fault model as a reference model according to the real-time fault parameter value and adopting an adaptive model following strategy, and further determining the reconstructed controller according to the reconstructed control law;
a reconstruction control gain adaptive model determining module, configured to regard the original aircraft model, the reference model, the state observer, and the reconstructed controller as a total augmentation system, determine a Lyapunov function of the total augmentation system, perform stability analysis, and further determine a reconstruction control gain adaptive model;
the control gain self-adaptive rate adjusting module is used for adjusting the control gain self-adaptive rate according to the determined reconstruction control gain self-adaptive model and the flight instruction; the flight instructions comprise an attack angle, a pitch angle rate, a wing root bending moment and a wing root torsion moment;
the state observer and base reference controller establishing module specifically includes:
a base reference controller determination unit for utilizing a formula
Determining the base reference controller;
a control law determination unit of the base reference controller for using the formula u ═ Kxx+KcxcDetermining a control law of the base reference controller;
a state observer determination unit for utilizing a formula
And
determining a state observer of the unknown input;
wherein the content of the first and second substances,
for the state of the base reference controller, R (t) e RmFor flight control system reference commands, AcAnd BcFor a matrix of suitable dimensions, y (t) e RmRepresenting the measured output vector, KxAnd KcFor normal control of gain, wi(t)∈RnRepresenting the observer state vector, y ∈ RmIndicating measurement inputOut, u is belonged to RpA control input is represented that is a control input,
representing the estimated value of the state vector, Bi＝BLi，Li＝diag{l1,l2,…,lp}，li∈[0,1]，biThe ith column representing B, M, G, N and H are matrixes of the state observer;
the fault information determination module specifically includes:
a fault diagnosis side-decision algorithm establishing unit for utilizing a formula
Establishing a fault diagnosis side decision algorithm;
wherein, c1> 0 is the weighting of the instantaneous residual signal, c2More than 0 is the weighting of the past residual signal, lambda is more than 0 is the forgetting factor for determining index memory, v is the number of the state observers, tau is the integral variable, ri(t) is the residual;
the real-time fault parameter determination module specifically includes:
real-time adaptive equation determination unit for fault parameters using equations
And
determining a self-adaptive equation of a real-time fault parameter;
wherein alpha isi＞0，βi> 0 is the adaptation rate, biIs the ith column, u, of the known matrixiFor the (i) th input(s),
for the ith actuator additive failure,
for the efficiency of the ith actuator, P is the symmetric positive definite matrix, M is the Hurwitz matrix, M is theTP + PM ═ Q, Q is a positive definite matrix;
the reconstructed controller determining module specifically includes:
a reconstructed control law determining unit for using the formula up＝Keemp+Kmxm+Kffm+ΘdDetermining a reconstructed control law;
wherein, Km，KfAnd ΘdAre all control gains, upFor the reconstructed control law, empAs an error between the object model and the reference model, fmIs a virtual multiplicative fault.
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