CN115933381B - Aerospace vehicle control performance enhancement design method under multiple constraint conditions - Google Patents
Aerospace vehicle control performance enhancement design method under multiple constraint conditions Download PDFInfo
- Publication number
- CN115933381B CN115933381B CN202211444857.3A CN202211444857A CN115933381B CN 115933381 B CN115933381 B CN 115933381B CN 202211444857 A CN202211444857 A CN 202211444857A CN 115933381 B CN115933381 B CN 115933381B
- Authority
- CN
- China
- Prior art keywords
- aerospace vehicle
- model
- control
- performance
- climbing
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 60
- 238000013461 design Methods 0.000 title claims abstract description 43
- 230000009194 climbing Effects 0.000 claims abstract description 56
- 238000013507 mapping Methods 0.000 claims abstract description 20
- 230000002708 enhancing effect Effects 0.000 claims abstract description 11
- 238000010206 sensitivity analysis Methods 0.000 claims abstract description 11
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 7
- 238000004088 simulation Methods 0.000 claims abstract description 7
- 239000011159 matrix material Substances 0.000 claims description 14
- 238000005070 sampling Methods 0.000 claims description 14
- 230000006870 function Effects 0.000 claims description 13
- 238000005457 optimization Methods 0.000 claims description 12
- 238000004458 analytical method Methods 0.000 claims description 7
- 238000013016 damping Methods 0.000 claims description 4
- 230000014509 gene expression Effects 0.000 claims description 4
- 238000007689 inspection Methods 0.000 claims description 4
- 238000013178 mathematical model Methods 0.000 claims description 4
- 238000005259 measurement Methods 0.000 claims description 4
- 238000000342 Monte Carlo simulation Methods 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000005381 potential energy Methods 0.000 claims description 3
- 230000004044 response Effects 0.000 claims description 3
- 230000006835 compression Effects 0.000 claims description 2
- 238000007906 compression Methods 0.000 claims description 2
- 230000001419 dependent effect Effects 0.000 claims description 2
- 238000006073 displacement reaction Methods 0.000 claims description 2
- 230000005284 excitation Effects 0.000 claims description 2
- 239000002243 precursor Substances 0.000 claims description 2
- 238000007670 refining Methods 0.000 claims description 2
- 230000003068 static effect Effects 0.000 claims description 2
- 238000011156 evaluation Methods 0.000 description 4
- 238000002485 combustion reaction Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 238000007476 Maximum Likelihood Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000013401 experimental design Methods 0.000 description 1
- 238000007637 random forest analysis Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
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)
Abstract
The invention discloses an aerospace vehicle control performance enhancement design method under a multi-constraint condition, which comprises the following steps: aiming at climbing tasks of different speed domains of the aerospace vehicle, constructing a complex dynamic model in a wide domain range, and adopting a model sensitivity analysis and parameter identification method to obtain a control-oriented design model of the aerospace vehicle; dividing the climbing sections of the aerospace vehicle into areas, designing control laws of different climbing areas of the aerospace vehicle, constructing a self-adaptive setting algorithm of control parameters by adopting a protection mapping theory, and solving a closed-loop stability boundary under uncertain conditions; determining the accurate mapping relation between the model parameters and the closed-loop performance reachable boundary, enhancing the control performance of the climbing section of the aerospace vehicle, and verifying the effectiveness of the control performance enhancement design result of the aerospace vehicle by adopting numerical simulation. According to the invention, through a design-evaluation-feedback-redesign loop iteration process, the comprehensive design of the air-to-air aircraft model performance and tracking control is realized, and the control performance of the aircraft is enhanced.
Description
Technical Field
The invention relates to the technical field of aerospace vehicle designs, in particular to an aerospace vehicle control performance enhancement design method under a multi-constraint condition.
Background
The climbing section of the aerospace vehicle needs to span different speed areas, whether the climbing section has excellent flight performance directly determines whether a subsequent task can be completed smoothly, the climbing section has large speed area span, and the climbing section is subjected to different flight phases such as a turbine mode, a sub-combustion mode, a super-combustion mode and the like, so that the characteristics of the aerospace vehicle model are complex and variable due to a large airspace wide speed area. Moreover, due to the inherent characteristics and complex stress conditions of the aerospace vehicle, a multi-field coupling relation exists between systems, and the model of the aerospace vehicle presents the remarkable characteristic of strong nonlinearity and quick time variation. In addition, the aerospace vehicle operating environment is more complex than subsonic/supersonic aircraft, and its dynamics are difficult to describe accurately, along with various stochastic disturbances that make the model with large uncertainty. More importantly, the aerospace vehicle is limited by structural load and propulsion efficiency, and the flight state, especially the attack angle, sideslip angle and the like, are required to be strictly restrained, and meanwhile, multiple constraint conditions such as control surface saturation, heat congestion and the like are also required to be met.
Compared with a conventional aircraft, the closed-loop performance requirement of the climbing section of the aerospace vehicle is higher, the cross-domain flight is required to complete a set task, the switching among propulsion modes is required to be comprehensively considered, the robust stability of the climbing process of the aerospace vehicle is ensured, and the influence of strong disturbance is restrained.
Disclosure of Invention
The technical problem to be solved by the invention is to provide the design method for enhancing the control performance of the aerospace vehicle under the multi-constraint condition, and the comprehensive design of the model performance and tracking control of the aerospace vehicle is realized through the design-evaluation-feedback-redesign loop iteration process, so that the control performance of the aerospace vehicle is enhanced.
In order to solve the technical problems, the invention provides an aerospace vehicle control performance enhancement design method under a multi-constraint condition, which comprises the following steps:
step 1, constructing a complex dynamic model in a wide range aiming at climbing tasks of different speed domains of an aerospace vehicle, and obtaining a control-oriented design model of the aerospace vehicle by adopting a model sensitivity analysis and parameter identification method;
step 2, dividing the climbing sections of the aerospace vehicle into areas, designing control laws of different climbing areas of the aerospace vehicle, constructing a self-adaptive setting algorithm of control parameters by adopting a protection mapping theory, and solving a closed-loop stability boundary under an uncertain condition;
and 3, determining the accurate mapping relation between the model parameters and the closed-loop performance reachable boundary, enhancing the control performance of the climbing section of the aerospace vehicle through comprehensive optimization design, and verifying the effectiveness of the control performance enhancement design result of the aerospace vehicle by adopting numerical simulation.
Preferably, in step 1, for climbing tasks in different speed domains of an aerospace vehicle, a complex dynamic model in a wide domain range is constructed, and a model sensitivity analysis and parameter identification method is adopted to obtain a control-oriented design model of the aerospace vehicle, which specifically comprises the following steps:
step 11, aiming at the configuration of the Mulberry-like aerospace vehicle, adopting a machine body/propulsion integrated design mode, wherein an engine is a turbine-based stamping propulsion system, is arranged at the middle and rear parts of the machine body, and takes the machine body part connected with an air inlet channel and a tail nozzle as a precursor air flow compression surface and a rear body expansion section respectively;
according to the climbing task requirement, dynamic pressure, overload and heat flow multi-constraint conditions in the flying climbing process are given, and an engineering estimation method is adopted to calculate aerodynamic force F on a surface element i Aerodynamic moment M i
F i ＝-(C pi Q ∞ +p ∞ )ΔS i n i (1)
M i ＝-(C pi Q ∞ +p ∞ )ΔS i n i ×r i (2)
Wherein C is pi As the pressure coefficient of the bin, deltaS i And n i Representing the area and normal vector of each bin, r i Is the displacement of the ith bin relative to centroid, p ∞ And Q ∞ The static pressure and the dynamic pressure of free flow are respectively added up to sum all aerodynamic forces and moments exerted on the surface element, so that the total aerodynamic force/moment coefficient acting on the aircraft can be obtainedBuilding an algorithm force database of the aircraft;
determining the functional relation between the resistance D and the pitching moment M and the flight condition, the climbing gesture and the control input of the climbing process of the aircraft as f L ,f D ,f m Expressed as
Wherein, alpha, V, h, delta e Phi represents the attack angle, speed, height, control surface deflection angle and propulsion coefficient of the aircraft respectively;
step 12, combining Lagrangian equations
Wherein: zeta type toy i Is generalized coordinates, F N,i To correspond to generalized coordinates ζ i Is used in the field of the general force of (a),as a Lagrangian function
Wherein: ET is total kinetic energy, EV is total potential energy;
according to the principle of virtual work, the generalized force is
Wherein: δW (delta W) vir Constructing a mathematical model of the climbing section of the aerospace vehicle for virtual work
Wherein ω is the angular velocity of the aircraft, m and J represent the mass and moment of inertia matrices, respectively;
step 13, adopting a sensitivity analysis method, respectively taking a lift coefficient, a resistance coefficient and a pneumatic pitching moment coefficient as performance indexes, analyzing the influence degree of each factor, refining main influence parameters related to the climbing process of the aerospace craft, including a flight attack angle, a control surface deflection angle and products thereof, expressing complex aerodynamic force, moment and thrust expressions of the craft as polynomial functions related to flight conditions, state quantities and control quantities, and obtaining a proxy model, wherein the lift coefficient C L And coefficient of resistance C D Represented as
Wherein the method comprises the steps ofFor lift-dependent model coefficients, +.>Model coefficients related to resistance;
step 14, evaluating the similarity between the dynamic characteristics of the models in different climbing speed ranges by adopting a clearance measurement principle, wherein the value range of the interval delta between the two models is [0,1], and the smaller the value of delta is close to 0, the smaller the difference between the two models is; the more the value of delta is close to 1, the larger the difference between the two models is, models of different areas are integrated according to similarity criteria, and a model of the aerospace vehicle facing control is obtained
Wherein x is a state variable; y is an output variable; u is an input variable; the state matrices a, B and the input matrices C, D vary with the height h and the mach number Ma.
Preferably, in step 2, the climbing section of the aerospace vehicle is divided into areas, the control laws of different climbing areas of the aerospace vehicle are designed, a self-adaptive setting algorithm of control parameters is constructed by adopting a protection mapping theory, and the closed loop stability boundary under the uncertain condition is calculated specifically comprises the following steps:
step 21, selecting speed and altitude as scheduling variables aiming at robust optimization results of model parameters of an aerospace vehicle, representing different modes within a flight envelope, and dividing the model parameters into N reg Sub-regions, each sub-region and corresponding modal points, the set of linearization models at these modal points (A i ,B i ,C i ,D i )，i＝1,…,N reg Can replace the linearization model of all points. The set of linearization models at these modal points constitutes a linear parametric model of the aircraft;
step 22, constructing an aircraft wide-area control structure, adopting a nonlinear model to predict local stability of different modal points of the aircraft, and adopting a protection mapping theory to calculate a dispatching variable range of the controller to enable a system area to be stable, wherein a model in the range can be regarded as a subsystem, and a controller parameter is a controller parameter of the modal point; then, taking the boundary point of the range as the next design point, repeating the process until the controller parameters and the corresponding subsystem ranges in the whole flight envelope range are obtained, and ensuring the stability of control switching among different subsystems;
step 23, designing a modal point controller by adopting a predictive control method with a finite time objective function, combining a Lyapunov function and bilinear matrix inequality, converting a control stability problem into a calculation problem for solving one bilinear matrix inequality, finding a common matrix and simultaneously meeting a set of the linear matrix inequality, thereby determining controller parameters and ensuring that a closed loop subsystem of the modal point is robust and gradually stable;
step 24, constructing the relation between the control parameters of the aerospace vehicle and the expected closed-loop performance based on the protection mapping theory
Wherein Ω s Is an open subset of the complex plane, λ (a) is the set of all eigenvalues comprising matrix a, S (Ω) s ) Is a generalized stability set, including all references to Ω s And solving a stable boundary of the closed loop system according to the relation until the stable matrix covers the flight corridor of the climbing section, so as to realize control switching among different modes.
Preferably, in step 3, determining a precise mapping relation between model parameters and a closed-loop performance reachable boundary, enhancing the control performance of the climbing section of the aerospace vehicle by comprehensive optimization design, and verifying the effectiveness of the control performance enhancing design result of the aerospace vehicle by numerical simulation specifically comprises the following steps:
step 31, analyzing tracking errors of model parameters of the aerospace vehicle, selecting a sample point every 10s on a time axis, and respectively carrying out normal distribution inspection on the altitude tracking errors and the speed tracking errors to obtain inspection results of whether the tracking errors at the sampling points are subjected to normal distribution;
step 32, further evaluating quality characteristics of the aerospace vehicle, using an equivalent fitting scheme of time domain and frequency domain mixing to obtain amplitude and phase angle of the high-order system, and then using an equivalent fitting method to obtain amplitude and phase angle of the corresponding low-order system, so as to minimize the mismatch degree parameter y, wherein the definition of the mismatch degree parameter y is as follows
Wherein,for the amplitude and phase angle of the higher order system, +.>Amplitude and phase angle, frequency omega, for low-order systems i Generally, 20 points are selected, and θ is the weight of the system; furthermore, based on the data of the scattered points of the high-order system, the response value of the corresponding low-order system is solved so that the performance index of the following function is minimum when the system inputs the same
Wherein Δy (T) =y h -y l 、y h And y l The system outputs obtained by the high-order system and the low-order system under the same excitation input are respectively, epsilon is the number of discrete time points and T 1 And T ε Respectively the starting time and the ending time of discrete points, simultaneously satisfying formulas (13) and (14) in the fitting process, and determining a low-order equivalent model of the aerospace vehicle
Wherein A is θ ,T θ ,ζ sp ,ω sp X, U are flight states and control inputs, and s is a pull operator;
step 33, after obtaining a low-order equivalent system of the aerospace vehicle, according to performance requirements, including long-period modal indexes, namely damping ratio and time factor; short period mode indexes, namely short period damping and natural frequency; analyzing the performance of the aircraft model according to the determined performance requirement, further calculating the long-period and short-period natural modes at specific state points, and further evaluating the performance of the closed-loop system of the aerospace vehicle according to the type of the aircraft and referring to the performance requirement of the long-period mode;
step 34, based on a closed-loop performance limit theory, including a limiting condition of an unstable pole to an upper limit of a model bandwidth and a maximum bandwidth of a controller under an unstable zero limit, constructing a mapping relation between model parameters and a closed-loop performance reachable boundary of the aerospace vehicle, and enhancing the control performance of the aerospace vehicle by adjusting the model parameters and the control parameters;
step 35, analyzing an optimization result of model parameters of the aerospace vehicle in a given nominal state, and evaluating the validity of model parameter optimization and tracking control in the nominal state; and then, under the severe condition of strong uncertainty, the model parameters of the aerospace vehicle are biased, through Monte Carlo simulation analysis, the variation range of the system output under different input conditions is analyzed by means of a large number of repeated simulation experiments, and whether the flight performance meets the expected requirement is solved, so that the effectiveness of the control performance enhancement design result is verified.
The beneficial effects of the invention are as follows: according to the design method, the complex wide-area model of the aerospace vehicle is built by providing the design method for enhancing the control performance of the aerospace vehicle under the multiple constraint conditions, the control law of the climbing section of the aerospace vehicle is designed by adopting the protection mapping theory, the comprehensive design of the open-loop model and the tracking control under the multiple constraint conditions is realized, and the control performance of the aerospace vehicle is enhanced.
Drawings
FIG. 1 is a schematic diagram of a control-oriented modeling flow for an aerospace vehicle of the present invention.
Fig. 2 is a schematic diagram of a design flow of a wide-area control law of a climbing section of the aerospace vehicle of the invention.
FIG. 3 is a schematic illustration of an aerospace vehicle climb segment performance enhancement design flow in accordance with the present invention.
Detailed Description
An aerospace vehicle control performance enhancement design method under a multi-constraint condition comprises the following steps:
step 1, constructing a complex dynamic model in a wide range, and adopting a model sensitivity analysis and parameter identification method to establish a control-oriented design model of the aerospace vehicle. As shown in fig. 1, the method specifically comprises the following steps:
(1) Aiming at the configuration of the Mulberry-like aerospace vehicle, multiple constraint conditions such as dynamic pressure, overload and heat flow in the flight climbing process are given according to climbing task requirements, an engineering estimation method is adopted to construct a calculation model of the aircraft, the functional relation among the stress and moment in the climbing process of the aircraft, the flight conditions, the climbing gesture and the propulsion mode is determined, and a mathematical model of the climbing section of the aerospace vehicle is constructed by combining a Lagrange equation, a virtual work principle and a Michelson equation.
(2) Aiming at a constructed aerospace vehicle model database and a nonlinear mathematical model, a Latin hyper-square sampling method is adopted to optimize reasonable sample points in the model database, and the Latin hyper-square sampling method is different from the traditional uniform sampling in that each dimension in a sample space is sampled, and the sampling number is the number of samples. The improved Latin super-square sampling method comprises optimal Latin square sampling, latin square sampling based on an orthogonal array and optimal Latin square sampling based on the orthogonal array. The optimal Latin square sampling takes an evaluation index of experimental design as an optimization target, and a reasonable optimization algorithm is adopted to optimize a sample space; the Latin super square sampling based on the orthogonal array can improve the sampling performance by changing the arrangement sequence, so as to optimize the potential energy of the sample, thereby realizing the optimal orthogonal Latin super square sampling based on the orthogonal array.
(3) For the sampled aerospace vehicle model data, a sensitivity analysis method is adopted to extract main influence parameters related to the climbing process of the aerospace vehicle, and the sensitivity analysis method comprises a sensitivity analysis method based on Monte Carlo, a Fourier amplitude spectrum sensitivity analysis, a partial factor iteration method and the like. For an aerospace vehicle proxy model form, a maximum likelihood identification method is adopted to determine a proxy model coefficient, then evaluation and analysis are carried out on different fitting variables within a wide climbing range, complex aerodynamic force, moment and thrust expressions of the aircraft are expressed as polynomial functions related to flight conditions, state quantity and control quantity, a high-order form and a cross form of the aircraft are analyzed, fitting goodness is calculated sequentially, factors with smaller influence are eliminated, feasible expression forms are determined, and a proxy model form is deduced. And evaluating the similarity between the model dynamic characteristics in different climbing speed ranges by adopting a clearance measurement principle, and integrating the models in different areas according to a similarity criterion to obtain a control-oriented design model of the aerospace vehicle.
And 2, dividing the climbing sections of the aerospace vehicle into areas, designing control laws of different climbing areas of the aerospace vehicle, constructing a self-adaptive setting algorithm of control parameters by adopting a protection mapping theory, and solving a closed-loop stability boundary under an uncertain condition. As shown in fig. 2, the method specifically comprises the following steps:
(1) Characteristic parameters of different movement modes of a climbing section of the aerospace vehicle are determined based on a clearance measurement theory, and a wide-area control structure of the aircraft is built, wherein the designed control structure consists of two parts: a nonlinear model predictive controller is adopted for a part to ensure the local stability of the modal points of the aircraft; and the other part applies a protection mapping theory to design a switching rule, so that the stability of control switching among different modes of the aircraft is ensured.
(2) The mode point controller is designed by adopting a predictive control method with a finite time objective function, and the closed-loop subsystem of the mode point of the aerospace vehicle is guaranteed to be robust, gradual and stable by combining the Lyapunov function and bilinear matrix inequality.
(3) And constructing a relation between control parameters of the aerospace vehicle and expected closed-loop performance based on a protection mapping theory, and solving a stability boundary of a closed-loop system until the stability boundary is covered on a flight corridor of a climbing section, so as to realize control switching among different modes.
And 3, determining the accurate mapping relation between the model parameters and the closed-loop performance reachable boundary, and enhancing the control performance of the climbing section of the aerospace vehicle through comprehensive optimization design. As shown in fig. 3, the method specifically comprises the following steps:
(1) And analyzing the tracking error of the model parameters of the aerospace vehicle, and checking whether the model parameters are subjected to normal distribution by adopting a normal distribution checking method. If the model parameter tracking errors of the aerospace craft do not meet the normal distribution, a classification detection scheme based on machine learning can be adopted to classify the model parameter tracking errors, the tracking errors are scattered into a plurality of grades to obtain a multi-attribute data set, and then a tracking error analysis strategy is designed by adopting machine learning methods such as k nearest neighbor, random forest, multi-layer perceptron and the like,
(2) Further evaluating quality characteristics of the aerospace vehicle, and determining a low-order equivalent model of the aerospace vehicle by using an equivalent simulation scheme of time domain and frequency domain mixing, wherein the main steps comprise the following three points: (a) selecting a fitting model and fitting parameter initial values; (b) Firstly, performing amplitude fitting of a time domain range, and firstly identifying four other parameters except delay time; (c) With other parameter values fixed, the system is fitted with the amplitude and phase angle responses in the frequency domain to determine the delay time.
(3) After the low-order equivalent system of the aerospace vehicle is acquired, the flight quality analysis can be divided into two aspects: (a) Analyzing the performance of the aircraft model according to the basic requirements of the flight quality, and further evaluating the performance of a closed-loop system of the aerospace aircraft; (b) And selecting a proper flight performance evaluation criterion according to the climbing task of the aerospace vehicle, and evaluating the flight quality of the climbing section according to a low-order equivalent system.
(4) And according to the results of the tracking error analysis and the performance evaluation of the aerospace vehicle, establishing the performance indexes of the model parameters and the tracking control integrated design, and screening out iteration parameters required by the integrated design. Based on a closed-loop performance limit theory, a mapping relation between model parameters and a closed-loop performance reachable boundary of the aerospace vehicle is constructed, tracking control errors of the aerospace vehicle are reduced by adjusting the model parameters and the control parameters, and climbing performance of the aerospace vehicle is improved.
(5) And analyzing the task instruction under a given nominal state, and evaluating the effectiveness of the control performance enhancement design result under the nominal state. Furthermore, under the severe condition of strong uncertainty, the parameters of the model of the aerospace vehicle are biased, and the effectiveness of the design result of the control performance enhancement is verified through Monte Carlo simulation analysis.
The invention provides an aerospace vehicle control performance enhancement design method under multiple constraint conditions, which realizes the comprehensive design of aerospace vehicle model performance and tracking control through a design-evaluation-feedback-redesign loop iteration process, enhances the control performance of the aircraft, has important research significance for developing a novel aerospace vehicle, and provides theoretical reserve and technical support for autonomous development of the aerospace vehicle in future in China.
Claims (1)
1. The design method for enhancing the control performance of the aerospace vehicle under the multi-constraint condition is characterized by comprising the following steps of:
step 1, constructing a complex dynamic model in a wide range aiming at climbing tasks of different speed domains of an aerospace vehicle, and obtaining a control-oriented design model of the aerospace vehicle by adopting a model sensitivity analysis and parameter identification method; the method specifically comprises the following steps:
step 11, aiming at the configuration of the Mulberry-like aerospace vehicle, adopting a machine body/propulsion integrated design mode, wherein an engine is a turbine-based stamping propulsion system, is arranged at the middle and rear parts of the machine body, and takes the machine body part connected with an air inlet channel and a tail nozzle as a precursor air flow compression surface and a rear body expansion section respectively;
according to the climbing task requirement, dynamic pressure, overload and heat flow multi-constraint conditions in the flying climbing process are given, and an engineering estimation method is adopted to calculate aerodynamic force F on a surface element i Aerodynamic moment M i
F i ＝-(C pi Q ∞ +p ∞ )ΔS i n i (1)
M i ＝-(C pi Q ∞ +p ∞ )ΔS i n i ×r i (2)
Wherein C is pi As the pressure coefficient of the bin, deltaS i And n i Representing the area and normal vector of each bin, r i Is the displacement of the ith bin relative to centroid, p ∞ And Q ∞ Respectively performing free flow static pressure and dynamic pressure, summing all aerodynamic forces and moments applied to the surface elements, so as to obtain a total aerodynamic force/moment coefficient acting on the aircraft, and constructing an aircraft computational force database;
determining the functional relation between the resistance D and the pitching moment M and the flight condition, the climbing gesture and the control input of the climbing process of the aircraft as f L ,f D ,f m Expressed as
Wherein, alpha, V, h, delta e Phi represents the attack angle, speed, height, control surface deflection angle and propulsion coefficient of the aircraft respectively;
step 12, combining Lagrangian equations
Wherein: zeta type toy i Is generalized coordinates, F N,i To correspond to generalized coordinates ζ i Is used in the field of the general force of (a),as a Lagrangian function
Wherein: ET is total kinetic energy, EV is total potential energy;
according to the principle of virtual work, the generalized force is
Wherein: δW (delta W) vir Constructing a mathematical model of the climbing section of the aerospace vehicle for virtual work
Wherein ω is the angular velocity of the aircraft, m and J represent the mass and moment of inertia matrices, respectively;
step 13, adopting a sensitivity analysis method, respectively taking a lift coefficient, a resistance coefficient and a pneumatic pitching moment coefficient as performance indexes, analyzing the influence degree of each factor, refining main influence parameters related to the climbing process of the aerospace craft, including a flight attack angle, a control surface deflection angle and products thereof, expressing complex aerodynamic force, moment and thrust expressions of the craft as polynomial functions related to flight conditions, state quantities and control quantities, and obtaining a proxy model, wherein the lift coefficient C L And coefficient of resistance C D Represented as
Wherein the method comprises the steps ofFor lift-dependent model coefficients, +.>Model coefficients related to resistance;
step 14, evaluating the similarity between the dynamic characteristics of the models in different climbing speed ranges by adopting a clearance measurement principle, wherein the value range of the interval delta between the two models is [0,1], and the smaller the value of delta is close to 0, the smaller the difference between the two models is; the more the value of delta is close to 1, the larger the difference between the two models is, models of different areas are integrated according to similarity criteria, and a model of the aerospace vehicle facing control is obtained
Wherein x is a state variable; y is an output variable; u is an input variable; the state matrices a, B and the input matrix C, D vary with the height h and the mach number Ma;
step 2, dividing the climbing sections of the aerospace vehicle into areas, designing control laws of different climbing areas of the aerospace vehicle, constructing a self-adaptive setting algorithm of control parameters by adopting a protection mapping theory, and solving a closed-loop stability boundary under an uncertain condition; the method specifically comprises the following steps:
step 21, selecting speed and altitude as scheduling variables aiming at robust optimization results of model parameters of an aerospace vehicle, representing different modes within a flight envelope, and dividing the model parameters into N reg Sub-regions, each sub-region and corresponding modal points, the set of linearization models at these modal points (A i ,B i ,C i ,D i )，i＝1,…,N reg The linear model of all the points can be replaced, and the linear model set at the modal points forms a linear variable parameter model of the aircraft;
step 22, constructing an aircraft wide-area control structure, adopting a nonlinear model to predict local stability of different modal points of the aircraft, and adopting a protection mapping theory to calculate a dispatching variable range of the controller to enable a system area to be stable, wherein a model in the range is regarded as a subsystem, and a controller parameter is a controller parameter of the modal points; then, taking the boundary point of the range as the next design point, repeating the process until the controller parameters and the corresponding subsystem ranges in the whole flight envelope range are obtained, and ensuring the stability of control switching among different subsystems;
step 23, designing a modal point controller by adopting a predictive control method with a finite time objective function, combining a Lyapunov function and bilinear matrix inequality, converting a control stability problem into a calculation problem for solving one bilinear matrix inequality, finding a common matrix and simultaneously meeting a set of the linear matrix inequality, thereby determining controller parameters and ensuring that a closed loop subsystem of the modal point is robust and gradually stable;
step 24, constructing the relation between the control parameters of the aerospace vehicle and the expected closed-loop performance based on the protection mapping theory
Wherein Ω s Is an open subset of the complex plane, λ (a) is the set of all eigenvalues comprising matrix a, S (Ω) s ) Is a generalized stability set, including all references to Ω s The stable matrix is used for solving the stable boundary of the closed loop system according to the relation until the stable boundary is covered to the flight corridor of the climbing section, so that control switching among different modes is realized;
step 3, determining the accurate mapping relation between the model parameters and the closed-loop performance reachable boundary, enhancing the control performance of the climbing section of the aerospace vehicle through comprehensive optimization design, and verifying the effectiveness of the control performance enhancement design result of the aerospace vehicle by adopting numerical simulation; the method specifically comprises the following steps:
step 31, analyzing tracking errors of model parameters of the aerospace vehicle, selecting a sample point every 10s on a time axis, and respectively carrying out normal distribution inspection on the altitude tracking errors and the speed tracking errors to obtain inspection results of whether the tracking errors at the sampling points are subjected to normal distribution;
step 32, further evaluating quality characteristics of the aerospace vehicle, using an equivalent fitting scheme of time domain and frequency domain mixing to obtain amplitude and phase angle of the high-order system, and using an equivalent fitting method to obtain amplitude and phase angle of the corresponding low-order system to minimize the mismatch degree parameter gamma, wherein the definition of the mismatch degree parameter gamma is as follows
Wherein G is h (jω i )、For amplitude and phase angle of higher order system, G l (jω i )、/>Amplitude and phase angle, frequency omega, for low-order systems i Generally, 20 points are selected, and θ is the weight of the system; furthermore, based on the data of the scattered points of the high-order system, the response value of the corresponding low-order system is solved so that the performance index of the following function is minimum when the system inputs the same
Wherein Δy (T) =y h -y l 、y h And y l The system outputs obtained by the high-order system and the low-order system under the same excitation input are respectively, epsilon is the number of discrete time points and T 1 And T ε Respectively the starting time and the ending time of discrete points, simultaneously satisfying formulas (13) and (14) in the fitting process, and determining a low-order equivalent model of the aerospace vehicle
Wherein A is θ ,T θ ,ζ sp ,ω sp X, U are flight states and control inputs, and s is a pull operator;
step 33, after obtaining a low-order equivalent system of the aerospace vehicle, according to performance requirements, including long-period modal indexes, namely damping ratio and time factor; short period mode indexes, namely short period damping and natural frequency; analyzing the performance of the aircraft model according to the determined performance requirement, further calculating the long-period and short-period natural modes at specific state points, and further evaluating the performance of the closed-loop system of the aerospace vehicle according to the type of the aircraft and referring to the performance requirement of the long-period mode;
step 34, based on a closed-loop performance limit theory, including a limiting condition of an unstable pole to an upper limit of a model bandwidth and a maximum bandwidth of a controller under an unstable zero limit, constructing a mapping relation between model parameters and a closed-loop performance reachable boundary of the aerospace vehicle, and enhancing the control performance of the aerospace vehicle by adjusting the model parameters and the control parameters;
step 35, analyzing an optimization result of model parameters of the aerospace vehicle in a given nominal state, and evaluating the validity of model parameter optimization and tracking control in the nominal state; and then, under the severe condition of strong uncertainty, the model parameters of the aerospace vehicle are biased, through Monte Carlo simulation analysis, the variation range of the system output under different input conditions is analyzed by means of a large number of repeated simulation experiments, and whether the flight performance meets the expected requirement is solved, so that the effectiveness of the control performance enhancement design result is verified.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211444857.3A CN115933381B (en) | 2022-11-18 | 2022-11-18 | Aerospace vehicle control performance enhancement design method under multiple constraint conditions |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211444857.3A CN115933381B (en) | 2022-11-18 | 2022-11-18 | Aerospace vehicle control performance enhancement design method under multiple constraint conditions |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115933381A CN115933381A (en) | 2023-04-07 |
| CN115933381B true CN115933381B (en) | 2023-12-01 |
Family
ID=86648212
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211444857.3A Active CN115933381B (en) | 2022-11-18 | 2022-11-18 | Aerospace vehicle control performance enhancement design method under multiple constraint conditions |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115933381B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103713517A (en) * | 2013-12-20 | 2014-04-09 | 南京航空航天大学 | Flight control system self-adaption parameter adjustment method |
| CN105867119A (en) * | 2016-01-15 | 2016-08-17 | 南京航空航天大学 | Aerospace vehicle large envelope switching control method adopting protection mapping theory |
| CN108459505A (en) * | 2018-03-12 | 2018-08-28 | 南京航空航天大学 | A kind of unconventional layout aircraft fast modeling method of suitable control Iterative Design |
-
2022
- 2022-11-18 CN CN202211444857.3A patent/CN115933381B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103713517A (en) * | 2013-12-20 | 2014-04-09 | 南京航空航天大学 | Flight control system self-adaption parameter adjustment method |
| CN105867119A (en) * | 2016-01-15 | 2016-08-17 | 南京航空航天大学 | Aerospace vehicle large envelope switching control method adopting protection mapping theory |
| CN108459505A (en) * | 2018-03-12 | 2018-08-28 | 南京航空航天大学 | A kind of unconventional layout aircraft fast modeling method of suitable control Iterative Design |
Non-Patent Citations (7)
| Title |
|---|
| Control law design of hypersonic vehicles using the elastic surrogate model;Liu Yanbin 等;Journal of Low Frequency Noise, Vibration and Active Control;全文 * |
| Guardian Maps Based Switching Tracking Control of Large Envelope Hypersonic Vehicles;Haidong Shen 等;2016 35th Chinese Control Conference;全文 * |
| Performance analysis of hypersonic vehicle based on aerodynamic derivatives;YuHui Li 等;2017 36th Chinese Control Conference;全文 * |
| Robust Multiple Model Predictive Control for Ascent Trajectory Tracking of Aerospace Vehicles;Rui Cao 等;IEEE Transactions on Aerospace and Electronic Systems;全文 * |
| 面向一体化设计的高超声速飞行器变参建模方法;汤佳骏 等;哈尔滨工程大学学报;全文 * |
| 高超声速飞行器应用保护映射的大包线控制律;刘梦影;刘燕斌;肖地波;陆宇平;张亚平;;控制理论与应用(11);全文 * |
| 高超声速飞行器底层性能评价指标的可行性分析;朱鸿绪 等;航空学报;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115933381A (en) | 2023-04-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Kou et al. | Data-driven modeling for unsteady aerodynamics and aeroelasticity | |
| Houtzager et al. | Recursive predictor-based subspace identification with application to the real-time closed-loop tracking of flutter | |
| CN113885320B (en) | Aircraft random robust control method based on mixed quantum pigeon swarm optimization | |
| Chen et al. | Parametric reduced-order modeling of unsteady aerodynamics for hypersonic vehicles | |
| CN107480335A (en) | A kind of hypersonic vehicle Iterative Design method | |
| CN105867119A (en) | Aerospace vehicle large envelope switching control method adopting protection mapping theory | |
| Borglund et al. | Efficient computation of robust flutter boundaries using the mu-k method | |
| CN115774900B (en) | Variable configuration aircraft instruction robust optimization design method under uncertain conditions | |
| CN113221237A (en) | Large attack angle flutter analysis method based on reduced order modeling | |
| CN115933381B (en) | Aerospace vehicle control performance enhancement design method under multiple constraint conditions | |
| Li et al. | Application of neural network based on real-time recursive learning and Kalman filter in flight data identification | |
| Tondji et al. | Identification of the Bombardier CRJ-700 stall dynamics model using neural networks | |
| Liu et al. | Correction methods of aerodynamic force and moment coefficients based on the identification data | |
| NOLL | Aeroservoelasticity | |
| Millidere et al. | Kalman based neural network model analysis with resampling methods for longitudinal aerodynamic coefficient estimation | |
| Barzgaran et al. | Real-time Model Predictive Control for Gust Load Alleviation on an Aeroelastic Wind Tunnel Test Article | |
| Cameron et al. | Metamodel assisted multi-objective global optimisation of natural laminar flow aerofoils | |
| Mocsányi et al. | Grid and polytopic LPV modeling of aeroelastic aircraft for co-design | |
| Biertümpfel et al. | Finite horizon analysis of autolanded aircraft in final approach under crosswind | |
| CN114859952A (en) | Nonlinear increment self-adaptive dynamic optimization control method for helicopter | |
| Xie et al. | An effort saving method to establish global aerodynamic model using CFD | |
| Omran et al. | Global aircraft aero-propulsive linear parameter-varying model using design of experiments | |
| Gong | Research on Civil Aircraft Fuel Consumption in Cruise Phase Based on Least Square Support Vector Regression with Genetic Algorithm | |
| Morton et al. | Computational aircraft and armament stability and control techniques applied to the F-16 | |
| CN117908385B (en) | Aircraft sliding mode control method based on parameter identification |
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 |