CN116700107A

CN116700107A - Controller parameter determining method, device, equipment and readable storage medium

Info

Publication number: CN116700107A
Application number: CN202310792455.0A
Authority: CN
Inventors: 马健成; 唐勇; 徐天宁; 孙德鑫; 马文; 孙陶然; 王泽林
Original assignee: Avic Chengdu Uav System Co ltd
Current assignee: Avic Chengdu Uav System Co ltd
Priority date: 2023-06-30
Filing date: 2023-06-30
Publication date: 2023-09-05

Abstract

The invention discloses a method, a device, equipment and a readable storage medium for determining controller parameters, which are applied to the technical field of flight controllers and comprise the following steps: acquiring an ideal model designed based on flight quality, a nonlinear aircraft control model and an aircraft body dynamics model; determining initial controller parameters of a nonlinear aircraft control model; and determining the optimal value of the controller parameter of the nonlinear aircraft control model by using a global optimizing algorithm. And the real-time flight quality evaluation after optimizing can be realized, and the optimal value of the searched controller parameter is ensured to meet the first-level flight quality requirement. Compared with the current manual adjustment of the controller parameters, the method utilizes the ideal model based on the flight quality related to the frequency domain and the optimizing targets based on the time domain and the complex frequency domain, so that the controller parameters can be simultaneously optimized from the time domain and the frequency domain, and the optimal value of the controller parameters can be intelligently determined based on the global optimizing algorithm, thereby improving the comprehensiveness and the intelligence of optimizing.

Description

Controller parameter determining method, device, equipment and readable storage medium

Technical Field

The present invention relates to the field of flight controllers, and in particular, to a method, an apparatus, a device, and a readable storage medium for determining parameters of a controller.

Background

Due to the strong nonlinearity and complexity of modern aircraft aerodynamics, nonlinear optimization problems associated with nonlinear aircraft control tuning are difficult to directly address. Currently, the tuning of flight controllers is manually adjusted based mainly on expert knowledge. However, this manual adjustment method is difficult to adapt to a flight controller that needs to determine a large number of parameters, and meanwhile, because of the coupling relationship between different parameters, the manual parameter adjustment is difficult to solve the problem of parameter selection, so that it is difficult for the flight controller to achieve good flight quality of the aircraft.

Therefore, there is a need to improve the efficiency of flight controller parameter adjustment and improve the quality of aircraft flight.

Disclosure of Invention

Accordingly, the present invention is directed to a method, apparatus, device and readable storage medium for determining parameters of a controller, which solve the technical problem of low efficiency of adjusting parameters of a controller in the prior art.

In order to solve the technical problems, the invention provides a method for determining parameters of a controller, which comprises the following steps:

Acquiring an ideal model designed based on flight quality, a nonlinear aircraft control model and an aircraft body dynamics model;

determining initial controller parameters of the nonlinear aircraft control model;

determining a controller parameter optimal value of the nonlinear aircraft control model according to the ideal model, the nonlinear aircraft control model, the aircraft body dynamics model and the initial controller parameter by using a global optimization algorithm;

obtaining an optimal nonlinear aircraft control model according to the optimal value of the controller parameter;

obtaining an overall flight control model according to the optimal nonlinear aircraft control model, the ideal model and the aircraft body dynamics model;

and determining the flight quality grade of the overall flight control model, and processing the optimal value of the controller parameter according to the flight quality grade.

Optionally, the obtaining the ideal model designed based on the flight quality includes:

the ideal model designed based on the first-order flight quality is obtained.

Optionally, the obtaining the ideal model based on the flight quality design, the nonlinear aircraft control model and the aircraft body dynamics model includes:

Acquiring a nonlinear aircraft body dynamics model; the nonlinear aircraft body dynamics model comprises at least one of a barycenter dynamics equation, a rotation dynamics equation, a barycenter kinematics equation and a rotation kinematics equation.

Optionally, the determining, by using a global optimization algorithm, an optimal value of a controller parameter of the nonlinear aircraft control model according to the ideal model, the nonlinear aircraft control model, the aircraft body dynamics model, and the initial controller parameter includes:

determining an optimal value of the controller parameter according to the ideal model, the nonlinear aircraft control model, the aircraft body dynamics model and the initial controller parameter by using an improved particle swarm global optimization algorithm; the improved particle swarm global optimization algorithm is a model for improving at least one of a weight form, a learning factor and a particle swarm selection algorithm; the weight form is improved to be self-adaptively adjusted weight, the learning factor is improved to be synchronous learning factor, and the particle swarm selection algorithm is improved to be a particle swarm selection algorithm based on a natural selection mechanism.

Optionally, the determining the flight quality level of the overall flight control model and processing the optimal value of the controller parameter according to the flight quality level includes:

Determining whether the flight quality level corresponding to the response of the overall flight control model is a primary flight quality based on manipulating a desired parametric model;

determining that the controller parameter optimum value is a target controller parameter when the quality of flight level is the primary quality of flight;

and when the flight quality grade is not the first-level flight quality, optimizing the optimal value of the controller parameter as an iterative training initial value of the nonlinear aircraft control model.

Optionally, after the obtaining the overall flight control model according to the optimal nonlinear aircraft control model, the ideal model and the aircraft ontology dynamics model, the method further includes:

linearizing the overall flight control model to obtain a linear aircraft model with flight control;

determining a low-order equivalent model corresponding to the linear aircraft model with flight control;

accordingly, the determining, based on the manipulation expectation parameter model, whether the level of flight quality corresponding to the response of the overall flight control model is a first-level flight quality includes:

determining whether the quality of flight level corresponding to the response of the low-order equivalent model is the primary quality of flight based on the maneuver desired parametric model.

adjusting controller parameters of the nonlinear aircraft control model according to the initial controller parameters by using the global optimizing algorithm;

and when the response result of the aircraft body dynamics model controlled by the nonlinear aircraft control model is the same as the response result of the aircraft body dynamics model controlled by the ideal model, determining the current controller parameter as the optimal value of the controller parameter.

The invention also provides a controller parameter determining device, which comprises:

the model acquisition module is used for acquiring an ideal model designed based on flight quality, a nonlinear aircraft control model and an aircraft body dynamics model;

an initial controller parameter determination module for determining initial controller parameters of the nonlinear aircraft control model;

the controller parameter optimal value determining module is used for determining the controller parameter optimal value of the nonlinear aircraft control model according to the ideal model, the nonlinear aircraft control model, the aircraft body dynamics model and the initial controller parameter by utilizing a global optimizing algorithm;

The optimal nonlinear aircraft control model determining module is used for obtaining an optimal nonlinear aircraft control model according to the optimal value of the controller parameter;

the overall flight control model determining module is used for obtaining an overall flight control model according to the optimal nonlinear aircraft control model, the ideal model and the aircraft body dynamics model;

and the processing module is used for determining the flight quality grade of the overall flight control model and processing the optimal value of the controller parameter according to the flight quality grade.

The invention also provides a control parameter optimizing device, which comprises:

a memory for storing a computer program;

and the processor is used for realizing the controller parameter determining method when executing the computer program.

The present invention also provides a computer readable storage medium having stored therein computer executable instructions which, when loaded and executed by a processor, implement the steps of the controller parameter determination method as described above.

It can be seen that the invention is based on the ideal model of the flight quality design, and nonlinear aircraft control model and aircraft body dynamics model; determining initial controller parameters of a nonlinear aircraft control model; determining the optimal value of the controller parameter of the nonlinear aircraft control model according to the ideal model, the nonlinear aircraft control model, the aircraft body dynamics model and the initial controller parameter by using a global optimizing algorithm, and obtaining the optimal nonlinear aircraft control model according to the optimal value of the controller parameter; obtaining an overall flight control model according to the optimal nonlinear aircraft control model, the ideal model and the aircraft body dynamics model; and determining the flight quality grade of the overall flight control model, and processing the optimal value of the controller parameter according to the flight quality grade. Compared with the current manual adjustment of the controller parameters or the optimization of the controller parameters only according to the time domain, the invention utilizes the ideal model based on the flight quality related to the frequency domain and the nonlinear aircraft control model based on the time domain, so that the controller parameters can be simultaneously optimized from the time domain and the frequency domain, and the optimal value of the controller parameters is intelligently determined based on the global optimization algorithm, thereby improving the comprehensiveness and the intelligence of the optimization.

In addition, the invention also provides a controller parameter determining device, equipment and a readable storage medium, which also have the beneficial effects.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flowchart of a method for determining parameters of a controller according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of the class of an ideal model designed based on first-class flight quality according to an embodiment of the present invention;

FIG. 3 is a flowchart of a standard particle swarm algorithm according to an embodiment of the present invention;

FIG. 4 is a graph showing a comparison of a linearly decreasing weight and an adaptive adjustment weight iteration curve according to an embodiment of the present invention;

FIG. 5 is a graph comparing iteration curves of synchronous learning factors and contraction factors according to an embodiment of the present invention;

FIG. 6 is a graph showing the comparison of simulation iteration numbers of a naturally selected particle swarm and a standard particle swarm according to an embodiment of the present invention;

FIG. 7 is a flowchart illustrating another method for determining parameters of a controller according to an embodiment of the present invention;

FIG. 8 is a flowchart illustrating a method for determining parameters of a controller according to an embodiment of the present invention;

FIG. 9 is an exemplary diagram of a quality determination model for a flight control system according to an embodiment of the present invention;

FIG. 10 is a schematic diagram of a flight controller according to an embodiment of the present invention;

FIG. 11 is a flow chart of a linearization and low-order equivalent fitting procedure provided by an embodiment of the invention;

fig. 12 is a schematic diagram of a frequency domain equivalent system according to an embodiment of the present invention;

FIG. 13 is a schematic diagram of a fitting result provided by an embodiment of the present invention;

fig. 14 is a schematic view of a short-period CAP determination level of a nonlinear aircraft according to an embodiment of the present invention;

fig. 15 is a schematic structural diagram of a controller parameter determining apparatus according to an embodiment of the present invention;

fig. 16 is a schematic structural diagram of a controller parameter determining apparatus according to an embodiment of the present invention.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

Because modern aircraft have strong nonlinear characteristics, at present, control systems of many aircraft adopt PID (proportion, integral and differential) control methods, and three main methods are generally adopted for adjusting parameters of the controller, wherein one method adopts classical control theory, namely, control law parameter adjustment is carried out by applying correction systems such as a frequency method, a root locus method and the like in a complex frequency domain, but the method is not suitable for the earlier-stage setting of control law parameters of a complex system because the coupling parameters can be adjusted only for a single-input single-output system; secondly, a modern control theory is adopted, a command tracking performance index is selected as an optimization index, in this way, the optimized controller has the capability of realizing tracking performance, but the flight quality of the aircraft is not considered in the initial stage of designing the controller, and the optimization is carried out mostly through overshoot, response time and other time domain parameters, and the flight quality of the aircraft is not fully combined for consideration, so that the optimized controller still possibly cannot meet the flight quality requirement. Finally, manual adjustment is performed based on expert knowledge, the manual adjustment method is difficult to adapt to a flight controller needing to determine a large number of parameters, and meanwhile, due to the fact that coupling relations exist among different parameters, the problem of parameter selection is difficult to solve through manual parameter adjustment, and therefore the flight controller is difficult to enable the aircraft to achieve good flight quality. Wherein the flying quality refers to: the pilot safely and comfortably pilotes the aircraft and can better complete the characteristics presented by the mission throughout the flight envelope.

In addition, since most of the algorithms adopted before are local optimization algorithms, a large difference between the optimal values of the controller parameters may be caused. Because the flow for verifying the flight quality in time is not performed after the control law parameters are designed, the later verification through experiments is needed, and more time and material cost are needed.

Referring to fig. 1, fig. 1 is a flowchart of a method for determining parameters of a controller according to an embodiment of the invention. The method may include:

s100, acquiring an ideal model designed based on flight quality, and a nonlinear aircraft control model and an aircraft body dynamics model.

The quality of flight in this embodiment may be a controller design indicator that is intuitively understood by both the pilot and the flight controller designer. Flight quality refers to the dynamics of an aircraft, allowing precise control with little effort by the pilot, revealing the relationship between pilot target class and actual aircraft dynamics. The embodiment is not limited to a specific ideal model as long as the ideal model is designed based on a model of the flight quality setting. For example, the ideal model may be a transfer function of a short period mode; or the ideal model may also be in the form of an ideal model in helicopter mode in the form of an RCAH (helicopter attitude angular rate command). In this embodiment, when the flight quality of the ideal model designed based on the flight quality is one level, the ideal model is most satisfactory, so the flight quality of the ideal model is generally set to the highest level, and when the level of the flight quality changes, the level of the flight quality of the ideal model is the highest level. The selection of the ideal model in this embodiment may be selected according to the technical problem to be solved.

For ease of understanding, please refer to fig. 2, fig. 2 is a schematic diagram of a class of an ideal model designed based on a first-stage flight quality according to an embodiment of the present invention. Since the commanded tracking control capability on the pitch axis is a fundamental requirement in the design of aircraft control augmentation systems or automatic controllers, it is also one of the most important flight quality requirements. Thus, the invention works by taking the design and evaluation of nonlinear aircraft control of pitch axis command tracking control problem as an example, the same method can be adopted for the design and evaluation of nonlinear aircraft control for roll axis command tracking and yaw axis command tracking. For the pitch axis, since the long period has less effect on the aircraft motion, only the transfer function of the short period mode is selected as a form of reference model, namely:

in the above, θ _c Is the pitch angle of the aircraft; delta _e Is the rudder deflection angle; zeta type _sp Is the damping ratio; omega _sp Is undamped natural frequency; k (K) _θ 、T _θ2 S is a frequency domain range, and θ is a pitch angle letter subscript. The invention uses K in the above formula according to the CAP flight evaluation criterion of flight quality _θ Set as 1, T _θ2 Set to 0.7143 ζ _sp Set to 0.707, ω _sp Setting to 3.5, an ideal model can be obtained, and the CAP (control expected parameter) rating result is satisfied, wherein the CAP (control expected parameter) rating result is located at the center of a first-level region in a standard chart, as shown in fig. 2, and fig. 2 is a schematic diagram of the class of the ideal model designed based on first-level flight quality according to the embodiment of the invention. Using these parameter values enables this ideal model, i.e. the transfer function of the short period mode, as a reference model to respond quickly to inputs and it has a first order flight quality level according to the CAP standard.

The embodiment is not limited to a particular type of nonlinear aircraft control model. For example, the nonlinear aircraft control model may be a hypersonic aircraft that is a nonlinear function; or the nonlinear aircraft control model may also be an aircraft controller that accommodates aerodynamic strong nonlinearities. The embodiment is not limited to a particular model of aircraft dynamics. For example, the flight ontology dynamics model may be a nonlinear aircraft ontology dynamics model; or the flight ontology dynamics model may also be an aircraft-like ontology dynamics model.

It should be noted that, the obtaining the ideal model designed based on the flight quality may include: an ideal model based on the first-order flight quality design is obtained. I.e. the ideal reference model is usually chosen with a high level of flight quality.

It should be noted that, the obtaining the ideal model designed based on the flight quality, and the nonlinear aircraft control model and the aircraft body dynamics model may include: acquiring a nonlinear aircraft body dynamics model; the nonlinear aircraft body dynamics model comprises at least one of a barycenter dynamics equation, a rotation dynamics equation, a barycenter kinematics equation and a rotation kinematics equation.

S101, determining initial controller parameters of a nonlinear aircraft control model.

This embodiment is not limited to the initial controller parameters of a particular nonlinear aircraft control model. For example, when the initial control parameter is a gain control parameter, the initial value of the gain control parameter may be 0.5; or when the initial controller parameter is a negative feedback gain parameter, the initial value of the negative feedback gain parameter may be 1; or when the initial controller parameter is an actuator coefficient, which may be 0.8. It will be appreciated that the initial controller parameter is an initial value designed according to the type of parameter.

S102, determining the optimal value of the controller parameter of the nonlinear aircraft control model according to the ideal model, the nonlinear aircraft control model, the aircraft body dynamics model and the initial controller parameter by using a global optimization algorithm.

The embodiment is not limited to a specific global optimization algorithm. For example, the global optimization algorithm may be a standard particle swarm algorithm; or the global optimization algorithm may also be a modified particle swarm algorithm.

It should be noted that, the determining, by using the global optimization algorithm, the optimal value of the controller parameter of the nonlinear aircraft control model according to the ideal model, the nonlinear aircraft control model, the aircraft body dynamics model and the initial controller parameter may include: determining an optimal value of the controller parameter according to the ideal model, the nonlinear aircraft control model, the aircraft body dynamics model and the initial controller parameter by utilizing an improved particle swarm global optimization algorithm; the improved particle swarm global optimization algorithm is a model for improving at least one of a weight form, a learning factor and a particle swarm selection algorithm; the weight form is improved to self-adaptively adjust the weight, the learning factor is improved to synchronous learning factor, and the particle swarm selection algorithm is improved to a particle swarm selection algorithm based on a natural selection mechanism. It may be appreciated that the particle swarm optimization algorithm in this embodiment may be a particle swarm optimization algorithm based on a natural selection mechanism, or the particle swarm optimization algorithm may also be a particle swarm optimization algorithm based on an adaptive weight, or the particle swarm optimization algorithm may also be a particle swarm optimization algorithm based on a synchronous learning factor, or the particle swarm optimization algorithm may also be a particle swarm optimization algorithm based on a natural selection mechanism, an adaptive weight, and a synchronous learning factor. For ease of understanding, please refer to fig. 3, fig. 3 is a flowchart of a standard particle swarm algorithm according to an embodiment of the present invention. The algorithm flow is as follows:

(1) The population size of the particle group, the initial positions of all particles, the initial velocity, the learning factor, and the like are initialized.

(2) And calculating a particle fitness value. And the fitness function is given according to the actual situation to calculate.

(3) And finding the optimal fitness value of the individual. The current fitness value of each particle is compared with the optimal value obtained in the past, if better than the past, the current value is used for replacing the past optimal value, and if not better, the current fitness value is kept unchanged.

(4) Finding out the optimal fitness value of the population. The selection is made among all individual optimum fitness values in the population, and if the individual optimum fitness value of a certain particle is better than global, the individual optimum value is substituted for the global optimum value, and if not, the individual optimum value is kept unchanged.

(5) The speed and position of the particles are readjusted. And (3) adjusting according to the given formula.

(6) And judging whether to end the algorithm. According to the preset ending condition, if the preset ending condition is met, the algorithm ends and gives a solution, otherwise, the iteration is continued from (2) again.

And selecting a new inertia weight form on the basis of the standard particle swarm, and readjusting the learning factors of the particle swarm. As shown in fig. 4, fig. 4 is a comparison chart of a linearly decreasing weight and an adaptive adjustment weight iteration curve provided in an embodiment of the present invention, where the linearly decreasing weight is iterated 12 times to obtain an optimal value. The adaptive adjustment weight reaches an optimal value at 7 times. In contrast, the optimizing speed of the self-adaptive weight adjustment is faster.

The particle swarm algorithm adopting the synchronous learning factor has fewer iteration times compared with the shrinkage factor particle swarm algorithm, and the optimal solution can be obtained after 6 iterations, as shown in fig. 5, and fig. 5 is a comparison chart of the synchronous learning factor and the shrinkage factor iteration curve provided by the embodiment of the invention.

The comparison simulation of the particle swarm based on natural selection and the standard particle swarm is shown in fig. 6, and fig. 6 is a comparison chart of the simulation iteration times of the natural selection particle swarm and the basic particle swarm provided by the embodiment of the invention. As can be seen from FIG. 6, the natural selection particle swarm optimization process has fewer iterations, and the minimum fitness value obtained by multiple simulation experiments is about 6.476 ×10 ^-13 Whereas the average value of the minimum fitness value of the standard particle swarm is about 3.154 ×10 ^-11 It can be seen that the accuracy of the naturally selected particle swarm algorithm is higher.

The invention improves the standard particle swarm. The improved idea is to adjust the inertia weight and learning factor of the particle swarm, and introduce a natural selection mechanism, and the steps of the obtained improved particle swarm when solving the problem of minimum value are as follows:

a. the size of the population of particles, i.e. how many particles are in the population, is set. The starting position and velocity of the particles are given. The maximum and minimum values of the learning factors are set. The maximum and minimum values of the weights are specified.

b. The fitness of each particle is calculated. The particle position and fitness value at this point are stored in the individual extremum. Among all the individual extrema, the particles with the lowest fitness are selected by comparison. Storing the position and fitness value of the particle into a global extremum f _access In the process, the liquid crystal display device comprises a liquid crystal display device,the letters are specifically defined below.

c. And updating the speed of the particles by adopting a synchronous learning factor.

d. The adaptive weights adjust inertial weights.

e. For each particle, if the historical optimal fitness value of the particle is lower than the current fitness value of the particle, then maintaining the historical optimal value, otherwise replacing the historical optimal fitness value with the current fitness value of the particle.

f. And selecting an optimal value of the population. And comparing the historical optimal fitness values of all the particles, if the historical optimal fitness values are smaller than the optimal value of the population, replacing the optimal value of the population, otherwise, keeping the optimal value of the population unchanged.

g. The particles are ordered according to the size of the fitness value, and half of the particles with relatively low fitness values are replaced by half of the particles with relatively high fitness values. And judging whether to end the algorithm. According to the preset ending condition, if the preset ending condition is met, the algorithm ends and gives a solution, otherwise, the iteration is continued from c again.

Wherein the objective function (flight controller parameters are optimal):

in the above formula, w1, w2 and w3 determine the emphasis point of optimization, and the optimizing algorithm supports the user-defined emphasis targets, including speed priority, stability priority, damping ratio priority and the like. Wherein the method comprises the steps ofRepresenting speed priority, representing time domain,/->Representing stability priority, representing time domain,/->The damping ratio is expressed as priority, the complex frequency domain is expressed, th represents the rise time of the response, sigma represents the overshoot of the response, and ζ represents the aircraft damping with the controller. By adjusting the three parameter values of w1, w2 and w3, the speed, stability and damping ratio can all reach better levels.

It should be noted that, the determining, by using the global optimization algorithm, the optimal value of the controller parameter of the nonlinear aircraft control model according to the ideal model, the nonlinear aircraft control model, the aircraft body dynamics model and the initial controller parameter may include: adjusting the controller parameters of the nonlinear aircraft control model according to the initial controller parameters by using a global optimizing algorithm; and when the response result of the control of the aircraft body dynamics model by using the nonlinear aircraft control model is the same as the response result of the control of the aircraft body dynamics model by using the ideal model, determining the current controller parameter as the optimal value of the controller parameter. The current controller parameters in this embodiment are controller parameters that correspond to the same results of controlling the aircraft body dynamics model using the nonlinear aircraft control model as the results of controlling the aircraft body dynamics model using the ideal model, based on the global optimization algorithm. It will be appreciated that determining the optimal value of the controller parameter directly from the response results ensures that the nonlinear aircraft control model is consistent with the maneuver results of the ideal model.

And S103, obtaining an optimal nonlinear aircraft control model according to the optimal value of the controller parameter.

S104, obtaining a total flight control model according to the optimal nonlinear aircraft control model, the ideal model and the aircraft body dynamics model.

S105, determining the flight quality grade of the overall flight control model, and processing the optimal value of the controller parameter according to the flight quality grade.

The embodiment is not limited to a specific manner of processing the optimal values of the controller parameters according to the quality of flight class. For example, the controller parameter optimum value may be directly determined as the target controller parameter; or when the optimal value of the controller parameter is not the first-level flight quality, taking the optimal value of the controller parameter as an initial value of the overall flight control model, performing iterative optimization, and re-determining the optimal value of the controller parameter.

Compared with the current method for manually adjusting the controller parameters or optimizing the controller parameters only according to the time domain, the method for determining the controller parameters provided by the embodiment of the invention utilizes the ideal model based on the flight quality related to the frequency domain and the nonlinear aircraft control model based on the time domain, so that the controller parameters can be optimized from the time domain and the frequency domain simultaneously, and the optimal value of the controller parameters is intelligently determined based on the global optimizing algorithm, so that the flight quality grade of the overall flight control model corresponding to the optimal value of the controller parameters is determined, the optimal value of the controller parameters is processed, the overall optimization is improved because the optimal value of the controller parameters can be determined based on the global optimizing, and the accuracy and the intelligence of the controller parameter determination are improved because the optimal value of the controller parameters can be processed based on the flight quality later. In addition, an ideal model is designed based on the first-level flight quality, so that the performance of the ideal model is higher; and, a nonlinear aircraft body dynamics model is obtained; the nonlinear aircraft body dynamics model comprises at least one of a barycenter dynamics equation, a rotation dynamics equation, a barycenter kinematics equation and a rotation kinematics equation, so that the matching degree of the aircraft body dynamics model and the controller and the diversity of the aircraft body dynamics model are improved; in addition, the improved particle swarm optimization is used as a global optimizing model, so that optimizing speed is improved; and the optimal value of the controller parameter is determined according to the response result, so that the accuracy of determining the optimal value of the controller parameter is improved.

For better understanding of the present invention, please refer to fig. 7, fig. 7 is a flowchart illustrating another method for determining parameters of a controller according to an embodiment of the present invention, which may include:

s700, an ideal model designed based on flight quality, a nonlinear aircraft control model and an aircraft body dynamics model are obtained.

The ideal model designed based on the flight quality in this embodiment may be an ideal model designed based on the first-order flight quality, and since the flight quality is a parameter of the frequency domain direction, the ideal model is a model of the frequency domain direction. The nonlinear aircraft control model in this embodiment is a time domain aspect of the controller model. The method comprises

S701, determining initial controller parameters of a nonlinear aircraft control model.

The initial controller parameters of the nonlinear aircraft control model in this embodiment are initial value parameters determined over a range based on actual characteristics of the parameters.

S702, determining the optimal value of the controller parameter of the nonlinear aircraft control model according to the ideal model, the nonlinear aircraft control model, the aircraft body dynamics model and the initial controller parameter by using a global optimization algorithm.

The embodiment can accurately and quickly determine the optimal value of the controller parameter by utilizing a global optimizing algorithm.

S703, determining an optimal nonlinear aircraft control model according to the optimal value of the controller parameter.

In the embodiment, the optimal value of the controller parameter is used as a parameter value and substituted into the nonlinear aircraft control model to obtain the optimal nonlinear aircraft control model.

S704, determining an overall flight control model according to the optimal nonlinear aircraft control model, the ideal model and the aircraft body dynamics model.

It should be noted that, after obtaining the overall flight control model according to the optimal nonlinear aircraft control model, the ideal model and the aircraft body dynamics model, the method may further include: linearizing the overall flight control model to obtain a linear aircraft model with flight control; determining a low-order equivalent model corresponding to the linear aircraft model with flight control; accordingly, determining whether the corresponding flight quality level of the response of the overall flight control model is a primary flight quality based on manipulating the desired parametric model may include: determining whether the corresponding flight quality level of the response of the low-order equivalent model is the first-order flight quality based on the manipulation desired parameter model.

S705, determining whether the flight quality level corresponding to the overall flight control model is the first-order flight quality based on the manipulation desired parameter model.

This embodiment determines whether the corresponding flight quality level of the overall flight control model is a primary flight quality based on the CAP.

S706, when the flight quality level is the first-level flight quality, it is determined that the optimal value of the controller parameter is the target controller parameter.

This embodiment determines that the optimal value of the selected controller parameter is indeed optimal.

And S707, when the flight quality level is not the first-level flight quality, optimizing the optimal value of the controller parameter as an iterative training initial value of the nonlinear aircraft control model.

In the embodiment, when the flight quality level is not the first-level flight quality, the optimal value of the controller parameter is used as the iterative training initial value of the nonlinear aircraft control model to perform optimization, and the optimizing process is S700 to S707. It can be understood that compared with the current process of verifying the flight quality in time after the controller parameters are designed, which leads to the need of verification through experiments in later period, more time and material cost are needed, the invention can further verify the determined optimal value of the controller parameters in time, and reduces the cost of manpower and material resources.

For better understanding of the present invention, please refer to fig. 8, fig. 8 is a flowchart illustrating an example of a method for determining a controller parameter according to an embodiment of the present invention, which may include:

S800, acquiring a quality determination model of a flight control system; the flight control system quality determining model comprises an ideal model, a flight controller and a nonlinear aircraft full model; the ideal model is a model for reflecting expected performance of the target aircraft according to first-level flight quality determination, and the flight controller is a nonlinear aircraft control model.

Fig. 9 is an exemplary diagram corresponding to a quality determining model of a flight control system in this embodiment, and fig. 9 is an exemplary diagram of a quality determining model of a flight control system according to an embodiment of the present invention. Wherein such a management is usedThe purpose of the reference model is to refer to the command y _c Instructions of the controller) into an equivalent system input u _c (instructions of ideal model). The flight controller obtains an actual flight command y by comparing the actual command with the command of the ideal model, and obtains a command u which is required to be adjusted by the nonlinear aircraft full model by calculating the difference between yc and y.

The structure and parameters of the ideal reference model in this embodiment are determined based on the aircraft configuration and aircraft dynamics. Since the commanded tracking control capability on the pitch axis is a fundamental requirement in the design of aircraft control augmentation systems or automatic controllers, it is also one of the most important flight quality requirements. The present invention therefore takes the pitch axis as an example of the first flying quality. For the pitch axis. The long period has less influence on the motion of the airplane, so that the transfer function of the short period mode is only selected as an ideal model, and the ideal model is adjusted based on the CAP flight criterion so that the parameters corresponding to the ideal model are positioned at the center of the first-level region in the standard diagram. The flight controller in this embodiment uses PID (performing adjustment control) and negative feedback of the angle of attack and pitch angle rate as a model of the controller in the longitudinal direction of the nonlinear aircraft. Referring to fig. 10, fig. 10 is a schematic diagram of a flight controller according to an embodiment of the invention. Wherein DeltaF _e Representing a lever force command input by a pilot, wherein alpha represents an attack angle K _P 、K _I Indicating gain, K _α 、K _q Indicating negative feedback gain, T _e Representing the actuator coefficient, delta theta _c Represents the target pitch angle command, delta theta represents the actual pitch angle signal, delta _e Represents the elevator deflection, Δq represents the pitch rate feedback, Δα represents the angle of attack feedback, and s represents a frequency domain range. On the basis of CAP, a nonlinear aircraft control parameter adjusting method based on an overall flight quality evaluation method is designed. The overall assessment model consists of three parts: ideal models, flight controllers and aircraft. Given certain controller parameters, e.g. K _P 、K _I 、K _α 、K _q Coefficients. Because the ideal model is constructed by adopting the first-level flight quality parameters, the controller can be considered as if the response of the controller and the nonlinear airplane model is the same as that of the ideal modelThe selected parameters are good. The invention adopts an improved particle swarm search algorithm to search the optimal value of the controller parameter in a recommended range (the recommended range is a larger range according to expert experience).

S801, determining an optimal value of a controller parameter corresponding to the flight controller by utilizing an improved particle swarm search algorithm based on the ideal model and the nonlinear aircraft full-scale model, and determining the optimal value of the controller parameter as a control parameter of the flight controller to obtain the target flight controller.

The aircraft model in this embodiment adopts a nonlinear aircraft full-scale model including a barycentric dynamics equation, a rotational dynamics equation, a barycentric kinematics equation, and a rotational kinematics equation of the aircraft. The embodiment adopts an improved standard particle swarm algorithm, and the improvement thought is to adjust the inertia weight and the learning factor of the particle swarm, introduce a natural selection mechanism and obtain the step of solving the problem of minimum value of the improved particle swarm.

The tracking control capability in this embodiment due to instructions on the pitch axis is a fundamental requirement in designing aircraft control augmentation systems or automatic controllers, and is one of the most important flight quality requirements. Therefore, the invention works by taking the design of nonlinear aircraft control with pitch axis command tracking control as an example, and the design and evaluation of nonlinear aircraft control can be carried out by adopting the same method for roll axis command tracking and yaw axis command tracking. For the pitch axis, since the long period has less effect on the aircraft motion, only the transfer function of the short period mode is chosen as a form of ideal model, namely:

in θ _c Is the pitch angle of the aircraft; delta _e Is the rudder deflection angle; zeta type _sp Is the damping ratio; omega _sp Is undamped natural frequency; k (K) _θ 、T _θ2 S is a frequency domain range, and θ is a pitch angle index. According to the CAP flight evaluation criteria of the flight quality,by combining K in the above formula _θ Set as 1, T _θ2 Set to 0.7143 ζ _sp Set to 0.707, ω _sp And setting to 3.5, an ideal model can be obtained, and the CAP (control expected parameter) rating result is located at the center of the primary region in the standard chart.

S802, constructing a general flight power model by using the ideal model, the target flight controller and the nonlinear aircraft full-scale model.

S803, linearizing the overall flight power model, and determining a corresponding low-order equivalent model.

It can be understood that for high-order stability augmentation aircraft, the system comprises feedback, feedforward, intermediate frequency forming filters, other high-frequency components and the like, the order of the high-order stability augmentation aircraft can reach 50-70 orders, and a plurality of additional modes are difficult to distinguish from long and short periodic modes of the aircraft, so that the flight quality of the high-order stability augmentation aircraft cannot be evaluated according to a conventional aircraft method. Thus, army standard STD-1797 claims to reduce its order by low-order fitting. It should be noted that, the process of linearizing the overall flight power model and then deriving the low-order equivalent system thereof is shown in fig. 11, and fig. 11 is a flowchart of a linearization and low-order equivalent fitting procedure provided by the embodiment of the invention. And on the basis of the obtained low-order equivalent system, performing grade determination on the low-order equivalent system by using CAP criterion so as to determine the flight quality of the aircraft model. If the score of the overall evaluation model is high, the selected parameters of the controller are considered good, i.e. consistent with the first-order flight quality.

1) Referring to the linearization and low-order equivalent fitting program flow chart of the invention shown in fig. 11, the whole system (dotted line box) is subjected to linearization processing, and the linearized system transfer function is obtained as follows:

in the method, in the process of the invention,is a transfer function of a closed loop control system; zeta type _sp For damping purposesRatio of; omega _sp Is undamped natural frequency; k (K) _θ 、T _θ2 Is a dimensionless gain parameter, theta(s) represents a pitch angle, theta _c (s) represents an ideal pitch angle command, delta, obtained by the ideal model under pilot operation command _e,c Representing pilot operating instructions, ++>Representing a higher order function.

2) The method comprises the steps of converting a high-order system of a formula into a low-order equivalent model by using a low-order equivalent fitting method, wherein the low-order equivalent fitting is performed by using the following performance indexes in the low-order equivalent fitting:

the frequency characteristic of the known high-order stability augmentation aircraft is G _HOS (jω) and given the frequency characteristic expression G of the low-order equivalent classical aircraft _LOES (jω), seek G _LOES The relevant parameters in (jω) minimize the following index function:

where K represents a weight coefficient between the amplitude error and the phase angle error, and is usually in the interval of 0.016 to 0.02. M is a mismatch parameter, and when the fitting result M is smaller than 100, the fitting is considered to be completed; ΔG (jω) _i ) To be taken from omega ₁ ～ω ₂ The frequency characteristic amplitude difference between the high-order system and the low-order system at a given discrete point is in dB; ΔΦ (jω) _i ) Is the corresponding phase angle difference in units of (°). The number of discrete frequency points is defined as n=20, and ω is required _i Is to take values according to uniform equal division on the frequency logarithmic coordinate axis, j represents complex units in complex frequency domain, phi _HOS (jω _i ) Representing the phase angle value of a higher order system, phi _LOES (jω _i ) As shown in fig. 12, fig. 12 is a schematic diagram of a frequency domain equivalent system according to an embodiment of the present invention.

By the fitting, the values of the parameters in the following formulas can be obtained according to the low-order linear model of the overall evaluation model:

wherein s represents a complex frequency domain variable, e ^-τs Is a term reflecting the high frequency phase lag caused by high order dynamics. T (T) _θ2 Representing a time constant. K (K) _θ Represents dimensionless gain parameters omega _sp Represents the longitudinal short period natural frequency ζ _sp Representing the longitudinal short period damping ratio, the fitting result is shown in fig. 13, fig. 13 is a schematic diagram of a fitting result provided by the embodiment of the present invention, and it can be seen that the low-order equivalent model is substantially coincident with the high-order term response.

S804, determining whether the grade corresponding to the low-order equivalent model is the first-order flight quality by utilizing the manipulation expected parameters.

It should be noted that the determining step may specifically be 1) determining the level of the low-order equivalent model using the flight quality standard. In the present invention, the CAP standard in the quality of flight is selected for the vertical short period to determine the grade.

2) Manipulating the desired parameter (CAP) refers to the ratio of the initial pitch acceleration to the increase in overload caused by elevator stepping. Since a period of time passes before steady state is reached, the pilot needs to indicate a response to the control input in advance, and the initial and final responses cannot be too sensitive nor too insensitive to commanded flight path changes. Thus, CAP is one of the most important criteria for evaluating aircraft systems. For the low-order equivalent model, the CAP value is calculated using the expression:

in the formula omega' _sp Obtaining short period natural frequency by equivalent fitting calculation, n _z Indicating normal overload and alpha indicating the angle of attack. In the CAP standard chart, as shown in fig. 3, the x-axis is the damping ratio of the lower-order equivalent system and the y-axis is the CAP value. If the evaluation result of the equivalent system is sufficiently close to the evaluation result of the ideal model,the closed loop system is considered to have a high level of tracking performance. Meanwhile, the parameters of the nonlinear controller are considered to be satisfactory. For the example of the longitudinal short period equivalent low-order model of the invention, according to the calculated CAP value and the damping ratio obtained by equivalent fitting, the flight quality can be seen as one level at the moment, the result of determining the level corresponding to the low-order equivalent model by using the manipulation expected parameter is shown in FIG. 14, and FIG. 14 is a schematic diagram of determining the level of the CAP of the short period of the nonlinear aircraft provided by the embodiment of the invention.

And S805, taking the optimal value of the controller parameter as a final parameter when the first-level flight quality is achieved.

And S806, when the flight quality is not the first-level flight quality, the optimal value of the controller parameter is used as an initial parameter, and the improved particle swarm model is utilized to redetermine the optimal value of the controller parameter based on the initial parameter, the ideal model and the nonlinear aircraft full-quantity model.

The following describes a controller parameter determining apparatus provided in an embodiment of the present invention, and the controller parameter determining apparatus described below and the controller parameter determining method described above may be referred to correspondingly.

Referring to fig. 15 specifically, fig. 15 is a schematic structural diagram of a controller parameter determining apparatus according to an embodiment of the present invention, which may include:

a model acquisition module 100 for acquiring an ideal model designed based on the flight quality, and a nonlinear aircraft control model and an aircraft body dynamics model;

an initial controller parameter determination module 200 for determining initial controller parameters of the nonlinear aircraft control model;

a controller parameter optimal value determining module 300, configured to determine a controller parameter optimal value of the nonlinear aircraft control model according to the ideal model, the nonlinear aircraft control model, the aircraft body dynamics model, and the initial controller parameter using a global optimization algorithm;

The optimal nonlinear aircraft control model determining module 400 is configured to obtain an optimal nonlinear aircraft control model according to the optimal value of the controller parameter;

the overall flight control model determining module 500 is configured to obtain an overall flight control model according to the optimal nonlinear aircraft control model, the ideal model and the aircraft ontology dynamics model;

and the processing module 600 is used for determining the flight quality grade of the overall flight control model and processing the optimal value of the controller parameter according to the flight quality grade.

Further, based on the above embodiment, the above model obtaining module 100 may include:

and the primary ideal model acquisition unit is used for acquiring the ideal model designed based on the primary flight quality.

Further, based on any of the above embodiments, the model obtaining module 100 may include:

the nonlinear aircraft body dynamics model acquisition module is used for acquiring a nonlinear aircraft body dynamics model; the nonlinear aircraft body dynamics model comprises at least one of a barycenter dynamics equation, a rotation dynamics equation, a barycenter kinematics equation and a rotation kinematics equation.

Further, based on any of the above embodiments, the above controller parameter optimal value determining module 300 may include:

determining an optimal value of a controller parameter by using an improved particle swarm global optimization algorithm, wherein the controller parameter optimal value is determined by using the improved particle swarm global optimization algorithm according to the ideal model, the nonlinear aircraft control model, the aircraft body dynamics model and the initial controller parameter; the improved particle swarm global optimization algorithm is a model for improving at least one of a weight form, a learning factor and a particle swarm selection algorithm; the weight form is improved to be self-adaptively adjusted weight, the learning factor is improved to be synchronous learning factor, and the particle swarm selection algorithm is improved to be a particle swarm selection algorithm based on a natural selection mechanism.

Further, according to any of the above embodiments, the processing module 600 according to the flying quality class may include:

a flight quality level determination unit for determining whether the flight quality level corresponding to the response of the overall flight control model is a first-level flight quality based on a manipulation desired parameter model;

a target controller parameter determination unit configured to determine that the controller parameter optimum value is a target controller parameter when the flight quality level is the first-level flight quality;

And the re-optimizing unit is used for optimizing the optimal value of the controller parameter as an iterative training initial value of the nonlinear aircraft control model when the flying quality grade is not the first-level flying quality.

Further, based on the above embodiment, the above controller model determining apparatus may further include:

the linearization unit is used for linearizing the overall flight control model to obtain a linear aircraft model with flight control;

the low-order equivalent model determining unit is used for determining a low-order equivalent model corresponding to the linear aircraft model with flight control;

accordingly, the flying quality grade determining unit may include:

a flight quality level determination subunit configured to determine, based on the manipulation expectation parameter model, whether the flight quality level corresponding to the response of the low-order equivalent model is the first-order flight quality.

a controller parameter adjustment unit for adjusting the controller parameters of the nonlinear aircraft control model according to the initial controller parameters by using the global optimization algorithm;

An optimal value determination unit configured to determine a current controller parameter as the controller parameter optimal value when a response result of controlling the aircraft body dynamics model using the nonlinear aircraft control model is the same as a response result of controlling the aircraft body dynamics model using the ideal model.

It should be noted that the order of the modules and units in the controller parameter determining apparatus may be changed without affecting the logic.

The device for determining the parameters of the controller provided by the embodiment of the invention can comprise: a model acquisition module 100 for acquiring an ideal model designed based on the flight quality, and a nonlinear aircraft control model and an aircraft body dynamics model; an initial controller parameter determination module 200 for determining initial controller parameters of the nonlinear aircraft control model; a controller parameter optimal value determining module 300, configured to determine a controller parameter optimal value of the nonlinear aircraft control model according to the ideal model, the nonlinear aircraft control model, the aircraft body dynamics model, and the initial controller parameter using a global optimization algorithm; the optimal nonlinear aircraft control model determining module 400 is configured to obtain an optimal nonlinear aircraft control model according to the optimal value of the controller parameter; the overall flight control model determining module 500 is configured to obtain an overall flight control model according to the optimal nonlinear aircraft control model, the ideal model and the aircraft ontology dynamics model; and the processing module 600 is used for determining the flight quality grade of the overall flight control model and processing the optimal value of the controller parameter according to the flight quality grade. Compared with the prior art that the controller parameters are manually adjusted or are optimized only according to the time domain, the invention utilizes the ideal model related to the frequency domain and based on the flight quality and the control target model based on the complex frequency domain and the time domain, so that the controller parameters can be optimized from the time domain and the frequency domain at the same time, and the optimal value of the controller parameters is intelligently determined based on the global optimizing algorithm, thereby determining the flight quality grade of the overall flight control model corresponding to the optimal value of the controller parameters, processing the optimal value of the controller parameters, improving the comprehensiveness of optimizing because the optimal value of the controller parameters can be determined based on the global optimizing, and improving the accuracy and the intelligence of the controller parameter determination because the optimal value of the controller parameters can be processed based on the flight quality later. In addition, an ideal model is designed based on the first-level flight quality, so that the performance of the ideal model is higher; and, a nonlinear aircraft body dynamics model is obtained; the nonlinear aircraft body dynamics model comprises at least one of a barycenter dynamics equation, a rotation dynamics equation, a barycenter kinematics equation and a rotation kinematics equation, so that the matching degree of the aircraft body dynamics model and the controller and the diversity of the aircraft body dynamics model are improved; after the optimal value of the controller parameter is obtained, the optimal value of the parameter is further verified, namely whether the flight quality is the first-level flight quality is timely verified, and the efficiency of flight quality verification is improved; in addition, the related model is subjected to reduced order processing in the verification process, so that the speed of flight quality verification is improved; in addition, the improved particle swarm optimization is used as a global optimizing model, so that optimizing speed is improved; and the optimal value of the controller parameter is determined according to the response result, so that the accuracy of determining the optimal value of the controller parameter is improved.

Referring to fig. 16, fig. 16 is a schematic structural diagram of a controller parameter determining apparatus according to an embodiment of the present invention, which may include:

a memory 10 for storing a computer program;

a processor 20 for executing a computer program to implement the controller parameter determination method described above.

The memory 10, the processor 20, and the communication interface 30 all communicate with each other via a communication bus 40.

In the embodiment of the present invention, the memory 10 is used for storing one or more programs, the programs may include program codes, the program codes include computer operation instructions, and in the embodiment of the present invention, the memory 10 may store programs for implementing the following functions:

determining initial controller parameters of a nonlinear aircraft control model;

determining the optimal value of the controller parameter of the nonlinear aircraft control model according to the ideal model, the nonlinear aircraft control model, the aircraft body dynamics model and the initial controller parameter by using a global optimizing algorithm;

In one possible implementation, the memory 10 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, and at least one application program required for functions, etc.; the storage data area may store data created during use.

In addition, memory 10 may include read only memory and random access memory and provide instructions and data to the processor. A portion of the memory may also include NVRAM. The memory stores an operating system and operating instructions, executable modules or data structures, or a subset thereof, or an extended set thereof, where the operating instructions may include various operating instructions for performing various operations. The operating system may include various system programs for implementing various basic tasks as well as handling hardware-based tasks.

The processor 20 may be a central processing unit (Central Processing Unit, CPU), an asic, a dsp, a fpga or other programmable logic device, and the processor 20 may be a microprocessor or any conventional processor. The processor 20 may call a program stored in the memory 10.

The communication interface 30 may be an interface of a communication module for connecting with other devices or systems.

Of course, it should be noted that the configuration shown in fig. 16 does not limit the controller parameter determining apparatus according to the embodiment of the present invention, and the controller parameter determining apparatus may include more or less components than those shown in fig. 16 or may combine some components in practical applications.

The following describes a computer readable storage medium provided in an embodiment of the present invention, where the computer readable storage medium described below and the method for determining a controller parameter described above may be referred to correspondingly.

The present invention also provides a computer readable storage medium having a computer program stored thereon, which when executed by a processor, implements the steps of the controller parameter determination method described above.

The computer readable storage medium may include: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, so that the same or similar parts between the embodiments are referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.

Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative elements and steps are described above generally in terms of functionality in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

Finally, it is further noted that, in this document, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.

The above description of the method, the device, the equipment and the readable storage medium for determining the parameters of the controller provided by the invention applies specific examples to illustrate the principles and the implementation of the invention, and the above examples are only used for helping to understand the method and the core idea of the invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.

Claims

1. A method for determining a controller parameter, comprising:

2. The controller parameter determination method according to claim 1, wherein the obtaining an ideal model based on a flight quality design comprises:

the ideal model designed based on the first-order flight quality is obtained.

3. The controller parameter determination method of claim 1, wherein the obtaining an ideal model based on a flight quality design, and a nonlinear aircraft control model and an aircraft body dynamics model, comprises:

4. The controller parameter determination method according to claim 1, wherein the determining the controller parameter optimum value of the nonlinear aircraft control model from the ideal model, the nonlinear aircraft control model, the aircraft body dynamics model, and the initial controller parameter using a global optimization algorithm comprises:

5. The controller parameter determination method according to any one of claims 1 to 4, wherein the determining the flight quality level of the overall flight control model and processing the controller parameter optimum value according to the flight quality level includes:

6. The controller parameter determination method of claim 5, further comprising, after said deriving an overall flight control model from said optimal nonlinear aircraft control model, said ideal model, and said aircraft body dynamics model:

7. The controller parameter determination method according to claim 1, wherein the determining the controller parameter optimum value of the nonlinear aircraft control model from the ideal model, the nonlinear aircraft control model, the aircraft body dynamics model, and the initial controller parameter using a global optimization algorithm comprises:

8. A controller parameter determining apparatus, comprising:

9. A control parameter optimizing apparatus, characterized by comprising:

a memory for storing a computer program;

processor for implementing the controller parameter determination method according to any one of claims 1 to 7 when executing the computer program.

10. A computer readable storage medium having stored therein computer executable instructions which when loaded and executed by a processor implement the steps of the controller parameter determination method of any one of claims 1 to 7.