A controller parameter determination method, device, equipment and readable storage medium

Abstract

This invention discloses a method, apparatus, device, and readable storage medium for determining controller parameters, applied in the field of flight controller technology. The method includes: acquiring an ideal model based on flight quality design, a nonlinear aircraft control model, and an aircraft dynamics model; determining the initial controller parameters of the nonlinear aircraft control model; and using a global optimization algorithm to determine the optimal values ​​of the controller parameters for the nonlinear aircraft control model. Furthermore, it enables real-time flight quality evaluation after optimization, ensuring that the optimal controller parameter values ​​meet Level 1 flight quality requirements. Compared to current manual adjustment of controller parameters, this invention utilizes a frequency-domain-dependent ideal model based on flight quality, and optimization objectives based on the time and complex frequency domains, allowing simultaneous optimization of controller parameters in both the time and frequency domains. Moreover, it intelligently determines the optimal controller parameter values ​​based on a global optimization algorithm, improving the comprehensiveness and intelligence of the optimization process.

Description

Technical Field

[0001] This invention relates to the field of flight controller technology, and in particular to a method, apparatus, device, and readable storage medium for determining controller parameters. Background Technology

[0002] Due to the strong nonlinearity and complexity of modern aircraft aerodynamics, nonlinear optimization problems related to nonlinear aircraft control parameter tuning are difficult to solve directly. Currently, flight controller parameter tuning is mainly based on manual adjustments using expert knowledge. However, this manual adjustment method is unsuitable for flight controllers that require determining a large number of parameters. Furthermore, due to the coupling relationships between different parameters, manual parameter tuning struggles to effectively address parameter selection issues, thus hindering the flight controller from achieving optimal flight performance for the aircraft.

[0003] Therefore, it is necessary to improve the efficiency of flight controller parameter adjustment and improve the quality of aircraft flight. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a method, apparatus, device and readable storage medium for determining controller parameters, which solves the technical problem of low efficiency in adjusting controller parameters in the prior art.

[0005] To solve the above technical problems, the present invention provides a method for determining controller parameters, comprising:

[0006] Obtain an ideal model based on flight quality design, as well as a nonlinear aircraft control model and an aircraft body dynamics model;

[0007] Determine the initial controller parameters for the nonlinear aircraft control model;

[0008] The optimal values ​​of the controller parameters of the nonlinear aircraft control model are determined using a global optimization algorithm based on the ideal model, the nonlinear aircraft control model, the aircraft dynamics model, and the initial controller parameters.

[0009] Based on the optimal values ​​of the controller parameters, the optimal nonlinear aircraft control model is obtained;

[0010] The overall flight control model is obtained based on the optimal nonlinear aircraft control model, the ideal model, and the aircraft body dynamics model.

[0011] The flight quality level of the overall flight control model is determined, and the optimal values ​​of the controller parameters are processed according to the flight quality level.

[0012] Optionally, obtaining the ideal model based on flight quality design includes:

[0013] Obtain the ideal model designed based on first-level flight quality.

[0014] Optionally, obtaining the ideal model based on flight quality design, as well as the nonlinear aircraft control model and the aircraft dynamics model, includes:

[0015] Obtain a nonlinear aircraft dynamics model; wherein the nonlinear aircraft dynamics model includes at least one of the following: center of mass dynamics equation, rotational dynamics equation, center of mass kinematics equation, and rotational kinematics equation.

[0016] Optionally, the step of using a global optimization algorithm to determine the optimal values ​​of the controller parameters of the nonlinear aircraft control model based on the ideal model, the nonlinear aircraft control model, the aircraft dynamics model, and the initial controller parameters includes:

[0017] An improved particle swarm optimization algorithm is used to determine the optimal values ​​of the controller parameters based on the ideal model, the nonlinear aircraft control model, the aircraft dynamics model, and the initial controller parameters. The improved particle swarm optimization algorithm is a model that improves at least one of the following: weighting form, learning factor, and particle swarm selection algorithm. The weighting form is improved to adaptively adjust weights, the learning factor is improved to a synchronous learning factor, and the particle swarm selection algorithm is improved to a particle swarm selection algorithm based on natural selection.

[0018] Optionally, determining the flight quality level of the overall flight control model and processing the optimal values ​​of the controller parameters based on the flight quality level includes:

[0019] Based on the desired manipulation parameter model, determine whether the flight quality level corresponding to the response of the overall flight control model is Level 1 flight quality;

[0020] When the flight quality level is Level 1 flight quality, the optimal value of the controller parameter is determined to be the target controller parameter;

[0021] When the flight quality level is not the first-level flight quality, the optimal value of the controller parameters is used as the initial value for iterative training of the nonlinear aircraft control model for optimization.

[0022] Optionally, after obtaining the overall flight control model based on the optimal nonlinear aircraft control model, the ideal model, and the aircraft body dynamics model, the method further includes:

[0023] The overall flight control model is linearized to obtain a linear aircraft model with flight control;

[0024] Determine the low-order equivalent model corresponding to the linear aircraft model with flight control;

[0025] Accordingly, determining whether the flight quality level corresponding to the response of the overall flight control model is Level 1 flight quality based on the manipulation expectation parameter model includes:

[0026] Based on the manipulation expectation parameter model, determine whether the flight quality level corresponding to the response of the low-order equivalent model is the first-level flight quality.

[0027] Optionally, the step of using a global optimization algorithm to determine the optimal values ​​of the controller parameters of the nonlinear aircraft control model based on the ideal model, the nonlinear aircraft control model, the aircraft dynamics model, and the initial controller parameters includes:

[0028] The controller parameters of the nonlinear aircraft control model are adjusted using the global optimization algorithm based on the initial controller parameters.

[0029] When the response result of controlling the aircraft dynamics model using the nonlinear aircraft control model is the same as the response result of controlling the aircraft dynamics model using the ideal model, the current controller parameters are determined as the optimal values ​​of the controller parameters.

[0030] The present invention also provides a controller parameter determination device, comprising:

[0031] The model acquisition module is used to acquire ideal models based on flight quality design, as well as nonlinear aircraft control models and aircraft body dynamics models;

[0032] An initial controller parameter determination module is used to determine the initial controller parameters of the nonlinear aircraft control model;

[0033] The controller parameter optimal value determination module is used to determine the optimal values ​​of the controller parameters of the nonlinear aircraft control model based on the ideal model, the nonlinear aircraft control model, the aircraft body dynamics model, and the initial controller parameters using a global optimization algorithm.

[0034] The optimal nonlinear aircraft control model determination module is used to obtain the optimal nonlinear aircraft control model based on the optimal values ​​of the controller parameters.

[0035] The overall flight control model determination module is used to obtain the overall flight control model based on the optimal nonlinear aircraft control model, the ideal model, and the aircraft body dynamics model.

[0036] The flight quality level processing module is used to determine the flight quality level of the overall flight control model and process the optimal values ​​of the controller parameters according to the flight quality level.

[0037] The present invention also provides a control parameter optimization device, comprising:

[0038] Memory, used to store computer programs;

[0039] A processor is used to implement the controller parameter determination method described above when executing the computer program.

[0040] The present invention also provides a computer-readable storage medium storing computer-executable instructions, which, when loaded and executed by a processor, implement the steps of the controller parameter determination method described above.

[0041] As can be seen, this invention obtains an ideal model based on flight quality design, as well as a nonlinear aircraft control model and an aircraft dynamics model; determines the initial controller parameters of the nonlinear aircraft control model; uses a global optimization algorithm to determine the optimal values ​​of the controller parameters of the nonlinear aircraft control model based on the ideal model, the nonlinear aircraft control model, the aircraft dynamics model, and the initial controller parameters; obtains the optimal nonlinear aircraft control model based on the optimal controller parameters; obtains the overall flight control model based on the optimal nonlinear aircraft control model, the ideal model, and the aircraft dynamics model; determines the flight quality level of the overall flight control model; and processes the optimal values ​​of the controller parameters according to the flight quality level. Compared to current methods that involve manually adjusting controller parameters or optimizing them solely based on the time domain, this invention utilizes a frequency-domain-dependent ideal flight quality model and a time-domain-based nonlinear aircraft control model. This allows for simultaneous optimization of controller parameters in both the time and frequency domains. Furthermore, it intelligently determines the optimal values ​​of controller parameters based on a global optimization algorithm, improving the comprehensiveness and intelligence of the optimization process. Since the flight quality level of the overall flight control model corresponding to the optimal controller parameter values ​​is subsequently assessed, and the optimal controller parameter values ​​are further processed, the accuracy of determining the optimal controller parameter values ​​is enhanced.

[0042] In addition, the present invention also provides a controller parameter determination device, equipment, and readable storage medium, which also have the above-mentioned beneficial effects. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0044] Figure 1 A flowchart of a controller parameter determination method provided in an embodiment of the present invention;

[0045] Figure 2 A schematic diagram illustrating the class of an ideal model designed based on first-level flight quality, provided for an embodiment of the present invention;

[0046] Figure 3 A flowchart of a standard particle swarm optimization algorithm provided in an embodiment of the present invention;

[0047] Figure 4 A comparison diagram of linearly decreasing weight and adaptively adjusted weight iteration curves provided in an embodiment of the present invention;

[0048] Figure 5 A comparison diagram of the iteration curves of the synchronous learning factor and the contraction factor provided in an embodiment of the present invention;

[0049] Figure 6 A comparison chart of simulation iterations between natural selection particle swarm optimization and standard particle swarm optimization provided for embodiments of the present invention;

[0050] Figure 7 A flowchart illustrating another controller parameter determination method provided in an embodiment of the present invention;

[0051] Figure 8 A flowchart illustrating a controller parameter determination method provided in an embodiment of the present invention;

[0052] Figure 9 An example diagram of a flight control system quality determination model provided in an embodiment of the present invention;

[0053] Figure 10 A schematic diagram of a flight controller provided for an embodiment of the present invention;

[0054] Figure 11 A flowchart of a linearization and low-order equivalent fitting procedure provided in an embodiment of the present invention;

[0055] Figure 12 This is a schematic diagram of a frequency domain equivalent system provided in an embodiment of the present invention;

[0056] Figure 13A schematic diagram of a fitting result provided in an embodiment of the present invention;

[0057] Figure 14 A schematic diagram illustrating the determination of the CAP level for a short-period nonlinear aircraft, provided as an embodiment of the present invention.

[0058] Figure 15 This is a schematic diagram of a controller parameter determination device provided in an embodiment of the present invention;

[0059] Figure 16 This is a schematic diagram of a controller parameter determination device provided in an embodiment of the present invention. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0061] Due to the strong nonlinear characteristics of modern aircraft, many aircraft control systems currently employ PID (Proportional, Integral, and Derivative) control methods. There are generally three main methods for adjusting controller parameters. One is using classical control theory, which involves adjusting the control law parameters in the complex frequency domain using methods such as frequency correction and root locus analysis. However, this method is only suitable for single-input, single-output systems and cannot adjust coupling parameters, making it unsuitable for the initial parameter setting of complex systems. The second method uses modern control theory, selecting command tracking performance as the optimization metric. This approach optimizes the controller to achieve tracking performance. However, because the aircraft's flight characteristics were not considered in the initial controller design, optimization often focuses on time-domain parameters such as overshoot and response time. Without sufficient consideration of flight characteristics, the optimized controller may still fail to meet flight quality requirements. Finally, manual adjustment based on expert knowledge is used. Manual adjustment is difficult to adapt to flight controllers that require determining a large number of parameters. Furthermore, due to the coupling relationships between different parameters, manual parameter adjustment struggles to effectively address parameter selection issues, resulting in the flight controller failing to achieve optimal flight characteristics for the aircraft. Flight quality refers to the characteristics exhibited by the pilot when flying the aircraft safely and comfortably and completing the flight mission well throughout the entire flight envelope.

[0062] Furthermore, since the algorithms previously used were mostly local optimization algorithms, the controller parameters may have deviated significantly from their optimal values. Because there was no timely verification process for flight quality after the control law parameters were designed, subsequent experimental verification is required, thus incurring substantial time and material costs.

[0063] Please refer to Figure 1 , Figure 1 A flowchart illustrating a controller parameter determination method provided in an embodiment of the present invention. The method may include:

[0064] S100, to obtain an ideal model based on flight quality design, as well as a nonlinear aircraft control model and an aircraft body dynamics model.

[0065] In this embodiment, flight quality can be a controller design indicator that both pilots and flight controller designers can intuitively understand. Flight quality refers to the dynamic characteristics of an aircraft, allowing for precise control with relatively low pilot workload. Flight quality reveals the relationship between the pilot's target level and the actual aircraft dynamics. This embodiment does not limit the specific ideal model, as long as the ideal model is designed based on the flight quality settings. For example, the ideal model can be a transfer function of a short-period mode; or the ideal model can also be an ideal model in helicopter mode in the form of RCAH (Helicopter Attitude Angular Rate Command). It should be noted that in this embodiment, the ideal model designed based on flight quality is most suitable when its flight quality is Level 1. Therefore, the flight quality of the ideal model is generally set to the highest level, Level 1. When the flight quality level changes, the flight quality level of the ideal model is the highest level. The selection of the ideal model in this embodiment can be based on the technical problem being solved.

[0066] For easier understanding, please refer to Figure 2 , Figure 2 This is a schematic diagram illustrating the level of an ideal model based on first-level flight quality design, provided as an embodiment of the present invention. Since command tracking control capability on the pitch axis is a fundamental requirement for designing aircraft control enhancement systems or automatic controllers, and is one of the most important flight quality requirements, this invention takes the design and evaluation of nonlinear aircraft control for the pitch axis command tracking control problem as an example. The same method can be used for the design and evaluation of nonlinear aircraft control for roll axis command tracking and yaw axis command tracking. For the pitch axis, since long periods have a relatively small impact on aircraft motion, only the transfer function of the short-period mode is selected as the form of the reference model, i.e.:

[0067]

[0068] In the above formula, θ c δ is the pitch angle of the aircraft.e Elevator deflection angle; ζ sp ω is the damping ratio; sp The undamped natural frequency; K θ T θ2 Here, is a dimensionless parameter, s is a frequency domain range, and θ is the pitch angle subscript. This invention, based on the CAP flight quality evaluation criterion, uses K in the above formula... θ Let T be 1. θ2 Set to 0.7143, ζ sp Set to 0.707, ω sp Setting it to 3.5 yields the ideal model, where its CAP (manipulated expectation parameter) rating is located in the center of the first-level region in the standard graph, such as... Figure 2 As shown, Figure 2 This diagram illustrates the classification of an ideal model based on Level 1 flight quality design, as provided in an embodiment of the present invention. Using these parameter values, the transfer function of this ideal model, i.e., the short-cycle mode, serves as a reference model and can respond quickly to inputs, thus achieving Level 1 flight quality according to the CAP standard.

[0069] This embodiment does not limit the specific type of nonlinear aircraft control model. For example, the nonlinear aircraft control model can be a hypersonic aircraft with nonlinear functions; or the nonlinear aircraft control model can also be an aircraft controller adapted to strong aerodynamic nonlinearity. This embodiment does not limit the specific aircraft dynamics model. For example, the flight dynamics model can be a nonlinear aircraft dynamics model; or the flight dynamics model can also be an aircraft-like dynamics model.

[0070] It should be noted that obtaining an ideal model based on flight quality design, as mentioned above, can include obtaining an ideal model based on first-level flight quality design. That is, the ideal reference model is usually selected based on a high level of flight quality.

[0071] It should be noted that the above-mentioned acquisition of the ideal model based on flight quality design, as well as the nonlinear aircraft control model and the aircraft body dynamics model, may include: acquiring a nonlinear aircraft body dynamics model; wherein, the nonlinear aircraft body dynamics model includes at least one of the following: center of mass dynamics equation, rotational dynamics equation, center of mass kinematics equation, and rotational kinematics equation.

[0072] S101, determine the initial controller parameters for the nonlinear aircraft control model.

[0073] This embodiment does not limit the initial controller parameters of a specific nonlinear aircraft control model. For example, when the initial control parameter is a gain control parameter, the initial value of the gain control parameter can be 0.5; or when the initial controller parameter is a negative feedback gain parameter, the initial value of the negative feedback gain parameter can be 1; or when the initial controller parameter is an actuator coefficient, the actuator coefficient can be 0.8. It is understood that the initial controller parameter is an initial value designed according to the type of the parameter.

[0074] S102 uses a global optimization algorithm to determine the optimal values ​​of the controller parameters for the nonlinear aircraft control model based on the ideal model, the nonlinear aircraft control model, the aircraft's dynamics model, and the initial controller parameters.

[0075] This embodiment does not limit the specific global optimization algorithm. For example, the global optimization algorithm can be the standard particle swarm optimization algorithm; or the global optimization algorithm can be an improved particle swarm optimization algorithm.

[0076] It should be noted that the above-mentioned method of using a global optimization algorithm to determine the optimal values ​​of controller parameters for a nonlinear aircraft control model based on an ideal model, a nonlinear aircraft control model, an aircraft dynamics model, and initial controller parameters can include: using an improved particle swarm optimization algorithm to determine the optimal values ​​of controller parameters based on the ideal model, the nonlinear aircraft control model, the aircraft dynamics model, and initial controller parameters; wherein, the improved particle swarm optimization algorithm is a model that improves at least one of the weighting form, learning factor, and particle swarm selection algorithm; the weighting form is improved to adaptively adjusted weights, the learning factor is improved to a synchronous learning factor, and the particle swarm selection algorithm is improved to a particle swarm selection algorithm based on a natural selection mechanism. It can be understood that the improved particle swarm algorithm in this embodiment can be a particle swarm selection algorithm based on a natural selection mechanism, or the improved particle swarm algorithm can also be a particle swarm selection algorithm based on adaptive weights, or the particle swarm algorithm can also be a particle swarm algorithm based on a synchronous learning factor, or the particle swarm selection algorithm can also be a particle swarm algorithm based on a natural selection mechanism, adaptive weights, and a synchronous learning factor. For easier understanding, please refer to [reference needed]. Figure 3 , Figure 3 A flowchart of a standard particle swarm optimization algorithm provided for an embodiment of the present invention. The algorithm flow is as follows:

[0077] (1) Initialize the swarm size, initial position, initial velocity, learning factor, etc. of all particles.

[0078] (2) Calculate the particle fitness value. Calculate the fitness value using the fitness function provided in the actual situation.

[0079] (3) Find the optimal fitness value for each individual. Compare the current fitness value of each particle with the best value obtained in the past. If it is better than the past, replace the past best value with the current value. If it is not better than the past, leave it unchanged.

[0080] (4) Find the optimal fitness value of the population. Select from the optimal fitness values ​​of all individuals in the population. If the optimal fitness value of a certain particle is better than the global optimal value, then replace the global optimal value with the optimal value of that individual. If there is no better value than the global optimal value, then leave it unchanged.

[0081] (5) Readjust the particle's velocity and position. Adjust according to the given formula.

[0082] (6) Determine whether to terminate the algorithm. If the pre-set termination condition is met, the algorithm terminates and provides a solution; otherwise, it restarts from (2) and continues iterating.

[0083] A new inertial weighting method is selected based on the standard particle swarm optimization, and the learning factor of the particle swarm optimization is readjusted. For example... Figure 4 As shown, Figure 4 This diagram compares the iterative curves of linearly decreasing weights and adaptively adjusting weights, as provided in an embodiment of the invention. The linearly decreasing weights obtain their optimal value after 12 iterations, while the adaptively adjusting weights reach their optimal value in just 7 iterations. Therefore, the adaptively adjusting weights optimize much faster.

[0084] The particle swarm optimization algorithm using a synchronous learning factor requires fewer iterations than the shrinking factor particle swarm optimization algorithm, finding the optimal solution after only 6 iterations. Figure 5 As shown, Figure 5 A comparison chart of the iteration curves of the synchronous learning factor and the contraction factor provided in an embodiment of the present invention.

[0085] A comparative simulation of particle swarm optimization based on natural selection and standard particle swarm optimization is presented, with the simulation results as follows: Figure 6 As shown, Figure 6 This is a comparison chart of the number of simulation iterations for natural selection particle swarm optimization and basic particle swarm optimization, provided as an embodiment of the present invention. From... Figure 6 It can be seen that the particle swarm optimization algorithm for natural selection requires fewer iterations, and the average minimum fitness value of the particle swarm optimization algorithm for natural selection, as obtained from multiple simulation experiments, is approximately 6.476 × 10⁻⁶. -13 The average minimum fitness value of the standard particle swarm optimization is approximately 3.154 × 10⁻⁶. -11 It is evident that the particle swarm optimization algorithm based on natural selection is more accurate.

[0086] This invention improves upon the standard particle swarm optimization (PSO) method. The improvement involves adjusting the inertia weights and learning factors of the particle swarm and introducing a natural selection mechanism. The resulting improved PSO method performs the following steps when solving minimum problems:

[0087] a. Define the size of the particle swarm, i.e., the number of particles in the swarm. Specify the initial positions and velocities of the particles. Define the maximum and minimum values ​​of the learning factor. Specify the maximum and minimum values ​​of the weights.

[0088] b. Calculate the fitness of each particle. Store the particle's current position and fitness value in the individual extreme value. Among all individual extreme values, select the particle with the lowest fitness by comparison. Store the position and fitness value of this particle in the global extreme value f. access middle, The specific meanings of the letters are explained below.

[0089] c. Use a synchronous learning factor to update the particle velocity.

[0090] d. Adaptive weight adjustment of inertia weight.

[0091] e. For each particle, if the particle's historical best fitness value is lower than its current fitness value, then keep the historical best value; otherwise, replace the historical best fitness value with the particle's current fitness value.

[0092] f. Select the optimal value of the population. Compare the historical best fitness values ​​of all particles. If any historical best fitness value is lower than the optimal value of the population, replace the optimal value of the population; otherwise, keep the optimal value of the population unchanged.

[0093] g. Sort the particles according to their fitness values, and replace the half of the particles with relatively high fitness values ​​with the half with relatively low fitness values. Determine whether to terminate the algorithm. According to the pre-defined termination condition, if the pre-defined termination condition is met, the algorithm terminates and provides a solution; otherwise, it restarts from c and continues iterating.

[0094] The objective function (optimizing flight controller parameters) is:

[0095]

[0096] In the above formula, w1, w2, and w3 determine the focus of the optimization. This optimization algorithm supports user-defined focus targets, including speed priority, stability priority, and damping ratio priority. This indicates speed priority and reflects the time domain. Indicates stability priority, representing the time domain. ξ represents the damping ratio priority, w1 represents the complex frequency domain, th represents the rise time of the response, σ represents the overshoot of the response, and ξ represents the damping of the aircraft with a controller. By adjusting the values ​​of the three parameters w1, w2, and w3, optimal levels of speed, stability, and damping ratio can be achieved.

[0097] It should be noted that the above-mentioned method of using a global optimization algorithm to determine the optimal values ​​of the controller parameters of the nonlinear aircraft control model based on the ideal model, the nonlinear aircraft control model, the aircraft dynamics model, and the initial controller parameters may include: adjusting the controller parameters of the nonlinear aircraft control model based on the initial controller parameters using the global optimization algorithm; and determining the current controller parameters as the optimal values ​​when the response result of using the nonlinear aircraft control model to control the aircraft dynamics model is the same as the response result of using the ideal model to control the aircraft dynamics model. In this embodiment, the current controller parameters are the controller parameters corresponding to the situation where the response result of using the nonlinear aircraft control model to control the aircraft dynamics model is the same as the response result of using the ideal model to control the aircraft dynamics model, based on the global optimization algorithm. It can be understood that directly determining the optimal values ​​of the controller parameters based on the response result can ensure that the control results of the nonlinear aircraft control model and the ideal model are consistent.

[0098] S103. Based on the optimal values ​​of the controller parameters, the optimal nonlinear aircraft control model is obtained.

[0099] S104. The overall flight control model is obtained based on the optimal nonlinear aircraft control model, the ideal model, and the aircraft's body dynamics model.

[0100] S105, determine the flight quality level of the overall flight control model, and process the optimal values ​​of the controller parameters according to the flight quality level.

[0101] This embodiment does not limit the specific processing method for processing the optimal value of the controller parameters according to the flight quality level. For example, the optimal value of the controller parameters can be directly determined as the target controller parameters; or when the optimal value of the controller parameters is not the first-level flight quality, the optimal value of the controller parameters can be used as the initial value of the overall flight control model, and iterative optimization can be performed to redetermine the optimal value of the controller parameters.

[0102] The controller parameter determination method provided in this invention, compared with the current method of manually adjusting controller parameters or optimizing controller parameters only based on the time domain, utilizes an ideal model based on flight quality related to the frequency domain and a nonlinear aircraft control model based on the time domain. This allows for simultaneous optimization of controller parameters from both the time and frequency domains. Furthermore, it intelligently determines the optimal values ​​of controller parameters based on a global optimization algorithm, thereby determining the flight quality level of the overall flight control model corresponding to the optimal values ​​of controller parameters. Since the optimal values ​​of controller parameters can be determined based on global optimization, the comprehensiveness of the optimization is improved. And since the optimal values ​​of controller parameters are subsequently processed based on flight quality, the accuracy and intelligence of controller parameter determination are improved. Furthermore, an ideal model will be designed based on first-level flight quality, resulting in higher performance of the ideal model; a nonlinear aircraft dynamics model will be obtained, which includes at least one of the following: center-of-mass dynamics equations, rotational dynamics equations, center-of-mass kinematics equations, and rotational kinematics equations, improving the matching degree between the aircraft dynamics model and the controller, as well as the diversity of the flight dynamics model; an improved particle swarm optimization algorithm will be used as the global optimization model, improving the optimization speed; and the optimal values ​​of controller parameters will be determined based on the response results, improving the accuracy of determining the optimal values ​​of controller parameters.

[0103] For a clearer understanding of this invention, please refer to the following details. Figure 7 , Figure 7 A flowchart illustrating another controller parameter determination method provided in this embodiment of the invention may specifically include:

[0104] S700 obtains an ideal model based on flight quality design, as well as a nonlinear aircraft control model and an aircraft dynamics model.

[0105] In this embodiment, the ideal model based on flight quality design can be an ideal model based on first-level flight quality design. Since flight quality is a parameter in the frequency domain, the ideal model is a frequency domain model. The nonlinear aircraft control model in this embodiment is a time-domain controller model.

[0106] S701, determine the initial controller parameters for the nonlinear aircraft control model.

[0107] In this embodiment, the initial controller parameters of the nonlinear aircraft control model are initial value parameters determined within a certain range based on the actual characteristics of the parameters.

[0108] S702 uses a global optimization algorithm to determine the optimal values ​​of the controller parameters for the nonlinear aircraft control model based on the ideal model, the nonlinear aircraft control model, the aircraft's dynamics model, and the initial controller parameters.

[0109] This embodiment can accurately and quickly determine the optimal values ​​of controller parameters using a global optimization algorithm.

[0110] S703 determines the optimal nonlinear aircraft control model based on the optimal values ​​of the controller parameters.

[0111] This embodiment uses the optimal values ​​of the controller parameters as parameter values ​​and substitutes them into the nonlinear aircraft control model to obtain the optimal nonlinear aircraft control model.

[0112] S704 determines the overall flight control model based on the optimal nonlinear aircraft control model, the ideal model, and the aircraft's body dynamics model.

[0113] It should be noted that after obtaining the overall flight control model based on the optimal nonlinear aircraft control model, the ideal model, and the aircraft's physical dynamics model, the process may further include: linearizing the overall flight control model to obtain a linear aircraft model with flight control; determining the low-order equivalent model corresponding to the linear aircraft model with flight control; and accordingly, determining whether the flight quality level corresponding to the response of the overall flight control model is a first-level flight quality based on the desired control parameter model, which may include: determining whether the flight quality level corresponding to the response of the low-order equivalent model is the first-level flight quality based on the desired control parameter model.

[0114] S705 determines whether the flight quality level corresponding to the overall flight control model is Level 1 flight quality based on the maneuver expectation parameter model.

[0115] This embodiment is based on whether the flight quality level corresponding to the CAP overall flight control model is Level 1 flight quality.

[0116] S706, when the flight quality level is Level 1, the optimal values ​​of the controller parameters are determined to be the target controller parameters.

[0117] This embodiment confirms that the selected optimal values ​​for the controller parameters are indeed optimal.

[0118] S707: When the flight quality level is not Level 1, the optimal values ​​of the controller parameters are used as the initial values ​​for iterative training of the nonlinear aircraft control model for optimization.

[0119] In this embodiment, when the flight quality level is not Level 1, the optimal values ​​of the controller parameters are used as the initial values ​​for iterative training of the nonlinear aircraft control model for optimization. The optimization process is from S700 to S707. It is understandable that, compared to the current process where flight quality is not verified promptly after the controller parameters are designed, requiring subsequent experimental verification and thus incurring significant time and material costs, this invention allows for timely further verification of the determined optimal controller parameter values, reducing human and material costs.

[0120] For a clearer understanding of this invention, please refer to the following details. Figure 8 , Figure 8 A flowchart illustrating a controller parameter determination method provided in this embodiment of the invention may specifically include:

[0121] S800, obtain the quality determination model of the flight control system; the quality determination model of the flight control system includes an ideal model, a flight controller and a nonlinear full aircraft model; the ideal model is a model that reflects the expected performance of the target aircraft based on the first-level flight quality, and the flight controller is a nonlinear aircraft control model.

[0122] See the example diagram corresponding to the flight control system quality determination model in this embodiment. Figure 9 , Figure 9 This is an example diagram of a flight control system quality determination model provided in an embodiment of the present invention. The purpose of using this ideal reference model is to determine the reference command y. c The controller's instructions are translated into equivalent system inputs u. c (Instructions from the ideal model). The flight controller compares the actual instructions with the instructions from the ideal model, subtracts them to obtain the actual flight instruction y, and calculates the difference between yc and y to obtain the instruction u that the nonlinear full-scale aircraft model should adjust.

[0123] The structure and parameters of the ideal reference model in this embodiment are determined based on the aircraft configuration and dynamic characteristics. Since command tracking control capability on the pitch axis is a fundamental requirement for designing aircraft control enhancement systems or automatic controllers, and is one of the most important flight quality requirements, this invention uses the pitch axis as the first flight quality example. For the pitch axis, long periods have a relatively small impact on aircraft motion; therefore, only the transfer function of the short-period mode is selected as the ideal model. The ideal model is adjusted based on the CAP flight criterion to ensure that the parameters corresponding to the ideal model are located in the center of the first-level region in the standard diagram. The flight controller in this embodiment uses PID (Execution Regulation Control) and negative feedback for angle of attack and pitch angular velocity as the nonlinear longitudinal controller model for the aircraft. Please refer to... Figure 10 , Figure 10 This is a schematic diagram of a flight controller provided in an embodiment of the present invention. Wherein, ΔFe This represents the stick force command input by the pilot, α represents the angle of attack, and K... P K I K represents the gain. α K q Indicates negative feedback gain, T e Δθ represents the actuator coefficient. c Δθ represents the target pitch angle command, Δθ represents the actual pitch angle signal, and Δδ represents the target pitch angle command. e Let Δq represent the elevator offset, Δα represent the pitch rate feedback value, Δα represent the angle of attack feedback value, and s represent a frequency domain range. Based on CAP, a nonlinear aircraft control parameter tuning method based on the overall flight quality assessment method is designed. The overall assessment model consists of three parts: an ideal model, a flight controller, and the aircraft. Given certain controller parameters, such as K... P K I K α K q Coefficients. Since an ideal model is constructed using first-level flight quality parameters, the selected parameters of the controller can be considered good if the response of the controller + nonlinear aircraft model is the same as that of the ideal model. This invention uses an improved particle swarm search algorithm to search for the optimal values ​​of the controller parameters within a recommended range (a relatively large range derived from expert experience).

[0124] S801 uses an improved particle swarm search algorithm based on an ideal model and a nonlinear full aircraft model to determine the optimal values ​​of the controller parameters corresponding to the flight controller. The optimal values ​​of the controller parameters are then used as the control parameters of the flight controller to obtain the target flight controller.

[0125] The aircraft model in this embodiment adopts a nonlinear full-scale aircraft model, including the aircraft's center-of-mass dynamics equations, rotational dynamics equations, center-of-mass kinematics equations, and rotational kinematics equations. This embodiment uses an improved standard particle swarm optimization algorithm. The improvement involves adjusting the inertia weights and learning factors of the particle swarm and introducing a natural selection mechanism, resulting in improved steps for solving minimum problems using particle swarm optimization.

[0126] In this embodiment, command tracking control capability on the pitch axis is a fundamental requirement for designing aircraft control enhancement systems or automatic controllers, and is also one of the most important flight quality requirements. Therefore, this invention takes the design of nonlinear aircraft control for the pitch axis command tracking control problem as an example. The same method can be used for the design and evaluation of nonlinear aircraft control for roll axis command tracking and yaw axis command tracking. For the pitch axis, since long periods have a relatively small impact on aircraft motion, only the transfer function of the short-period mode is selected as the form of the ideal model, that is:

[0127]

[0128] In the formula, θ c δ is the pitch angle of the aircraft. e Elevator deflection angle; ζ sp ω is the damping ratio; sp The undamped natural frequency; K θ T θ2 Here, is a dimensionless parameter, s is a frequency domain range, and θ is a subscript representing the pitch angle. This invention, based on the CAP flight quality evaluation criterion, uses K in the above formula... θ Let T be 1. θ2 Set to 0.7143, ζ sp Set to 0.707, ω sp Setting it to 3.5 yields the ideal model, satisfying that its CAP (manipulation expectation parameter) rating result is located in the center of the first-level region in the standard graph.

[0129] S802 utilizes an ideal model, a target flight controller, and a nonlinear full-scale aircraft model to construct an overall flight dynamics model.

[0130] S803 linearizes the overall flight dynamics model and determines its corresponding low-order equivalent model.

[0131] Understandably, for high-order stability-enhanced aircraft, the system includes feedback, feedforward, and intermediate frequency shaping filters, as well as other high-frequency components, with orders reaching 50th to 70th. Many of these additional modes are difficult to distinguish from the aircraft's long and short period modes, making it impossible to evaluate their flight quality using conventional aircraft methods. Therefore, the military standard STD-1797 advocates using low-order fitting methods to reduce the order. It should be noted that the process of linearizing the overall flight dynamics model and then deriving its low-order equivalent system, such as... Figure 11 As shown, Figure 11 This is a flowchart illustrating a linearization and low-order equivalent fitting procedure provided in an embodiment of the present invention. Based on the obtained low-order equivalent system, the CAP criterion is used to determine the grade of the low-order equivalent system to ascertain the flight quality of the aircraft model. If the overall evaluation model score is high, the selected parameters of the controller are considered good, i.e., they meet the requirements of Level 1 flight quality.

[0132] 1)Reference Figure 11 The flowchart of the linearization and low-order equivalent fitting procedure of the present invention shown below linearizes the entire system (dashed box) to obtain the linearized system transfer function as follows:

[0133]

[0134] In the formula, ζ is the transfer function of the closed-loop control system; sp ω is the damping ratio;sp The undamped natural frequency; K θ T θ2 Here, θ(s) is a dimensionless gain parameter, representing the pitch angle. c (s) represents the ideal pitch angle command obtained by the ideal model under the pilot's operating commands, δ e,c Represents pilot operating instructions. This represents a higher-order function.

[0135] 2) Use a low-order equivalent fitting method to convert the high-order system of the equation into a low-order equivalent model, as shown in the following equation. The following performance indicators are used for low-order equivalent fitting:

[0136] Given the frequency characteristics of a high-order stability-enhanced aircraft as G HOS (jω), and give the frequency response expression G of a low-order equivalent classical aircraft. LOES (jω), seeking G LOES The parameters in (jω) minimize the following index function:

[0137]

[0138] In the formula, K represents the weighting coefficient between amplitude error and phase error, usually taken in the range of 0.016 to 0.02. M is the mismatch parameter; when the fitting result M is less than 100, the fitting is considered complete; ΔG(jω i ) represents the difference in frequency response amplitude between the higher-order and lower-order systems at given discrete points between ω1 and ω2, in dB; ΔΦ(jω i ) represents the corresponding phase angle difference, in degrees (°). The number of frequency discrete points is specified as n = 20, and ω is required to... i The values ​​are taken evenly on the logarithmic frequency coordinate axis, where j represents the complex unit in the complex frequency domain, and Φ HOS (jω i ) represents the phase angle value of a higher-order system, Φ LOES (jω i () represents the phase angle value of a low-order system, such as Figure 12 As shown, Figure 12 This is a schematic diagram of a frequency domain equivalent system provided in an embodiment of the present invention.

[0139] Based on the above fitting and the low-order linear model of the overall evaluation model, the values ​​of each parameter in the following formula can be obtained:

[0140]

[0141] In the formula, s represents the complex frequency domain variable, e -τs It reflects the high-frequency phase lag term caused by higher-order dynamics. T θ2 K represents the time constant.θ ω represents the dimensionless gain parameter. sp Representing the longitudinal short-period natural frequency, ζ sp Representing the longitudinal short-period damping ratio, the fitting results are as follows: Figure 13 As shown, Figure 13 This is a schematic diagram of a fitting result provided by an embodiment of the present invention. It can be seen that the low-order equivalent model and the response of the high-order term are basically consistent.

[0142] S804 uses the desired control parameters to determine whether the level corresponding to the low-order equivalent model is a first-class flight quality.

[0143] It should be noted that the specific steps involved may be: 1) Using flight quality standards to determine the grade of the low-order equivalent model. In this invention, the CAP standard in longitudinal short-cycle flight quality is selected to determine the grade.

[0144] 2) The desired control parameter (CAP) refers to the ratio of the initial pitch acceleration to the increase in overload caused by elevator stepping. Because a period of time elapses before reaching steady state, the pilot needs to anticipate the response to control inputs, and the initial and final responses must be neither too sensitive nor too insensitive to changes in the commanded flight path. Therefore, CAP is one of the most important criteria for evaluating aircraft systems. For low-order equivalent models, the CAP value is calculated using the following expression:

[0145]

[0146] In the formula, ω′ sp The short-period natural frequency, n, is obtained by equivalent fitting calculation. z Indicates normal overload, α represents angle of attack. In the CAP standard diagram, as shown... Figure 3 As shown, the x-axis represents the damping ratio of the low-order equivalent system, and the y-axis represents the CAP value. If the evaluation results of the equivalent system are sufficiently close to the evaluation results of the ideal model, the closed-loop system is considered to have a high level of tracking performance. Simultaneously, the parameters of the nonlinear controller are considered satisfactory. For the longitudinal short-period equivalent low-order model example of this invention, based on the calculated CAP value and the damping ratio obtained from the equivalent fit, it can be seen that the flight quality is level one. The results for determining the corresponding level of the low-order equivalent model using the desired control parameters are as follows: Figure 14 As shown, Figure 14 This is a schematic diagram illustrating the determination of the CAP level for a short period of a nonlinear aircraft, provided as an embodiment of the present invention.

[0147] S805, when the flight quality is Level 1, uses the optimal value of the controller parameters as the final parameters.

[0148] S806, when not in Level 1 flight quality, uses the optimal values ​​of the controller parameters as initial parameters, and uses an improved particle swarm model to redetermine the optimal values ​​of the controller parameters based on the initial parameters, the ideal model, and the nonlinear full aircraft model.

[0149] The following describes a controller parameter determination device provided by an embodiment of the present invention. The controller parameter determination device described below and the controller parameter determination method described above can be referred to in correspondence.

[0150] Please refer to the details. Figure 15 , Figure 15 A schematic diagram of a controller parameter determination device provided in an embodiment of the present invention may include:

[0151] The model acquisition module 100 is used to acquire an ideal model based on flight quality design, as well as a nonlinear aircraft control model and an aircraft body dynamics model.

[0152] The initial controller parameter determination module 200 is used to determine the initial controller parameters of the nonlinear aircraft control model;

[0153] The controller parameter optimal value determination module 300 is used to determine the optimal value of the controller parameters of the nonlinear aircraft control model based on the ideal model, the nonlinear aircraft control model, the aircraft body dynamics model and the initial controller parameters using a global optimization algorithm.

[0154] The optimal nonlinear aircraft control model determination module 400 is used to obtain the optimal nonlinear aircraft control model based on the optimal values ​​of the controller parameters.

[0155] The overall flight control model determination module 500 is used to obtain the overall flight control model based on the optimal nonlinear aircraft control model, the ideal model, and the aircraft body dynamics model.

[0156] The flight quality level processing module 600 is used to determine the flight quality level of the overall flight control model and process the optimal values ​​of the controller parameters according to the flight quality level.

[0157] Furthermore, based on the above embodiments, the model acquisition module 100 may include:

[0158] The first-level ideal model acquisition unit is used to acquire the ideal model based on the first-level flight quality design.

[0159] Furthermore, based on any of the above embodiments, the model acquisition module 100 may include:

[0160] A nonlinear aircraft dynamics model acquisition module is used to acquire a nonlinear aircraft dynamics model; wherein, the nonlinear aircraft dynamics model includes at least one of the following: center of mass dynamics equation, rotational dynamics equation, center of mass kinematics equation, and rotational kinematics equation.

[0161] Furthermore, based on any of the above embodiments, the controller parameter optimal value determination module 300 may include:

[0162] An improved particle swarm optimization algorithm is used to determine the optimal values ​​of controller parameters. This algorithm is used to determine the optimal values ​​of the controller parameters based on the ideal model, the nonlinear aircraft control model, the aircraft's physical dynamics model, and the initial controller parameters. The improved particle swarm optimization algorithm is a model that improves at least one of the following: weighting form, learning factor, and particle swarm selection algorithm. The weighting form is improved to adaptively adjust weights, the learning factor is improved to a synchronous learning factor, and the particle swarm selection algorithm is improved to a particle swarm selection algorithm based on natural selection.

[0163] Furthermore, based on any of the above embodiments, the flight quality level processing module 600 may include:

[0164] The flight quality level determination unit is used to determine whether the flight quality level corresponding to the response of the overall flight control model is a first-level flight quality based on the maneuver expectation parameter model.

[0165] The target controller parameter determination unit is used to determine that the optimal value of the controller parameter is the target controller parameter when the flight quality level is the first-level flight quality.

[0166] The re-optimization unit is used to optimize the controller parameters by using the optimal values ​​as the initial values ​​for iterative training of the nonlinear aircraft control model when the flight quality level is not the first-level flight quality.

[0167] Furthermore, based on the above embodiments, the controller model determination device may further include:

[0168] A linearization unit is used to linearize the overall flight control model to obtain a linear aircraft model with flight control.

[0169] A low-order equivalent model determination unit is used to determine the low-order equivalent model corresponding to the linear aircraft model with flight control.

[0170] Accordingly, the aforementioned flight quality level determination unit may include:

[0171] The flight quality level determination subunit is used to determine, based on the control expectation parameter model, whether the flight quality level corresponding to the response of the low-order equivalent model is the first-level flight quality.

[0172] Furthermore, based on any of the above embodiments, the controller parameter optimal value determination module 300 may include:

[0173] A controller parameter adjustment unit is used to adjust the controller parameters of the nonlinear aircraft control model based on the initial controller parameters using the global optimization algorithm.

[0174] The optimal value determination unit is used to determine the current controller parameters as the optimal values ​​of the controller parameters when the response result of controlling the aircraft dynamics model using the nonlinear aircraft control model is the same as the response result of controlling the aircraft dynamics model using the ideal model.

[0175] It should be noted that the order of the modules and units in the controller parameter determination device can be changed without affecting the logic.

[0176] The controller parameter determination device provided in this embodiment of the invention may include: a model acquisition module 100, used to acquire an ideal model based on flight quality design, as well as a nonlinear aircraft control model and an aircraft dynamics model; an initial controller parameter determination module 200, used to determine the initial controller parameters of the nonlinear aircraft control model; a controller parameter optimal value determination module 300, used to determine the optimal values ​​of the controller parameters of the nonlinear aircraft control model using a global optimization algorithm based on the ideal model, the nonlinear aircraft control model, the aircraft dynamics model, and the initial controller parameters; an optimal nonlinear aircraft control model determination module 400, used to obtain an optimal nonlinear aircraft control model based on the optimal controller parameter values; an overall flight control model determination module 500, used to obtain an overall flight control model based on the optimal nonlinear aircraft control model, the ideal model, and the aircraft dynamics model; and a flight quality level processing module 600, used to determine the flight quality level of the overall flight control model and process the optimal values ​​of the controller parameters based on the flight quality level. Compared to current methods that involve manually adjusting controller parameters or optimizing them solely based on the time domain, this invention utilizes a frequency-domain-dependent ideal model based on flight quality, along with a control target model based on both the complex frequency and time domains. This allows for simultaneous optimization of controller parameters in both the time and frequency domains. Furthermore, it intelligently determines the optimal values ​​of the controller parameters based on a global optimization algorithm, thereby determining the flight quality level of the overall flight control model corresponding to the optimal controller parameter values. This process further refines the optimal controller parameter values. Since the optimal controller parameter values ​​can be determined based on global optimization, the comprehensiveness of the optimization process is improved. Moreover, the subsequent processing of the optimal controller parameter values ​​based on flight quality enhances the accuracy and intelligence of the controller parameter determination. Furthermore, an ideal model is designed based on Level 1 flight quality, resulting in higher performance. A nonlinear aircraft dynamics model is also acquired, comprising at least one of the following: center-of-mass dynamics equations, rotational dynamics equations, center-of-mass kinematics equations, and rotational kinematics equations. This improves the matching degree between the aircraft dynamics model and the controller, as well as the diversity of the flight dynamics model. After obtaining the optimal controller parameters, these optimal values ​​are further verified, i.e., timely verification of whether the flight quality is Level 1, improving the efficiency of flight quality verification. During the verification process, the relevant models are downgraded, increasing the speed of flight quality verification. An improved particle swarm optimization algorithm is used as the global optimization model, improving the optimization speed. Finally, the optimal controller parameters are determined based on the response results, improving the accuracy of determining the optimal controller parameters.

[0177] The following describes a controller parameter determination device provided by an embodiment of the present invention. The controller parameter determination device described below and the controller parameter determination method described above can be referred to in correspondence.

[0178] Please refer to Figure 16 , Figure 16 A schematic diagram of a controller parameter determination device provided in an embodiment of the present invention may include:

[0179] Memory 10 is used to store computer programs;

[0180] The processor 20 is used to execute a computer program to implement the controller parameter determination method described above.

[0181] The memory 10, processor 20, and communication interface 30 all communicate with each other through the communication bus 40.

[0182] In this embodiment of the invention, the memory 10 is used to store one or more programs. The programs may include program code, which includes computer operation instructions. In this embodiment of the invention, the memory 10 may store programs for implementing the following functions:

[0183] Obtain an ideal model based on flight quality design, as well as a nonlinear aircraft control model and an aircraft body dynamics model;

[0184] Determine the initial controller parameters for the nonlinear aircraft control model;

[0185] The optimal values ​​of the controller parameters for the nonlinear aircraft control model are determined using a global optimization algorithm based on the ideal model, the nonlinear aircraft control model, the aircraft dynamics model, and the initial controller parameters.

[0186] Based on the optimal values ​​of the controller parameters, the optimal nonlinear aircraft control model is obtained;

[0187] The overall flight control model is obtained based on the optimal nonlinear aircraft control model, the ideal model, and the aircraft's body dynamics model.

[0188] Determine the flight quality level of the overall flight control model, and process the optimal values ​​of the controller parameters according to the flight quality level.

[0189] In one possible implementation, the memory 10 may include a program storage area and a data storage area, wherein the program storage area may store the operating system and applications required for at least one function; and the data storage area may store data created during use.

[0190] Furthermore, memory 10 may include read-only memory and random access memory, providing instructions and data to the processor. A portion of the memory may also include NVRAM. The memory stores operating systems and operating instructions, executable modules, or data structures, or subsets thereof, or extended sets thereof, wherein the operating instructions may include various operating instructions for implementing various operations. The operating system may include various system programs for implementing various basic tasks and handling hardware-based tasks.

[0191] Processor 20 can be a central processing unit (CPU), an application-specific integrated circuit, a digital signal processor, a field-programmable gate array, or other programmable logic device. Processor 20 can be a microprocessor or any conventional processor. Processor 20 can call programs stored in memory 10.

[0192] The communication interface 30 can be an interface for the communication module, used to connect with other devices or systems.

[0193] Of course, it should be noted that, Figure 16 The structure shown does not constitute a limitation on the controller parameter determination device in the embodiments of the present invention. In practical applications, the controller parameter determination device may include... Figure 16 More or fewer components as shown, or combinations of certain components.

[0194] The following describes the computer-readable storage medium provided in the embodiments of the present invention. The computer-readable storage medium described below and the controller parameter determination method described above can be referred to in correspondence.

[0195] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the controller parameter determination method described above.

[0196] The computer-readable storage medium may include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0197] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0198] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0199] Finally, it should be noted that in this document, relationships such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0200] The present invention has provided a detailed description of a controller parameter determination method, apparatus, device, and readable storage medium. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A controller parameter determination method characterized by, The method comprises the following steps: obtaining a frequency domain related ideal model based on flight quality design, a time domain related nonlinear aircraft control model and an aircraft body dynamics model; determining initial controller parameters of the nonlinear aircraft control model; determining optimal controller parameters of the nonlinear aircraft control model by using a global optimization algorithm according to the ideal model, the nonlinear aircraft control model, the aircraft body dynamics model and the initial controller parameters; obtaining an optimal nonlinear aircraft control model according to the optimal controller parameters; obtaining a total flight control model according to the optimal nonlinear aircraft control model, the ideal model and the aircraft body dynamics model; determining a flight quality level of the total flight control model and processing the optimal controller parameters according to the flight quality level; wherein the optimal controller parameters of the nonlinear aircraft control model are determined by using a global optimization algorithm according to the ideal model, the nonlinear aircraft control model, the aircraft body dynamics model and the initial controller parameters, comprising: the optimal controller parameters are determined by using an 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 parameters; wherein the improved particle swarm global optimization algorithm is an algorithm improved in weight, learning factor and particle swarm selection algorithm of the particle swarm; the weight form is improved to be self-adaptive adjustment weight, the learning factor is improved to be a synchronous learning factor, the particle swarm selection algorithm is improved to be a particle swarm selection algorithm based on a natural selection mechanism, and the improved particle swarm global optimization algorithm determines user-defined side emphasis targets, including speed priority, stability priority and damping ratio priority.

2. The controller parameter determination method of claim 1, wherein, The ideal model based on flight quality design related to the frequency domain is obtained, comprising: the ideal model based on first-level flight quality design is obtained.

3. The controller parameter determination method of claim 1, wherein The ideal model based on flight quality design related to the frequency domain, the nonlinear aircraft control model and the aircraft body dynamics model are obtained, comprising: a nonlinear aircraft body dynamics model is obtained; wherein the nonlinear aircraft body dynamics model comprises at least one of a mass center dynamics equation, a rotation dynamics equation, a mass center kinematics equation and a rotation kinematics equation.

4. The controller parameter determination method according to any one of claims 1 to 3, characterized by, The flight quality level of the total flight control model is determined, and the optimal controller parameters are processed according to the flight quality level, comprising: determining whether the flight quality level corresponding to the response of the total flight control model is a first-level flight quality based on a control expectation parameter model; when the flight quality level is the first-level flight quality, the optimal controller parameters are determined to be target controller parameters; when the flight quality level is not the first-level flight quality, the optimal controller parameters are taken as initial values for iterative training optimization of the nonlinear aircraft control model.

5. The controller parameter determination method of claim 4, wherein, After the overall flight control model is obtained according to the optimal nonlinear aircraft control model, the ideal model and the aircraft body dynamics model, the method further comprises: linearizing the overall flight control model to obtain a linear flight control aircraft model; determining a low-order equivalent model corresponding to the linear flight control aircraft model; Correspondingly, the method for determining whether the flight quality level corresponding to the response of the overall flight control model to the control expected parameter model is a first flight quality level comprises: determining whether the flight quality level corresponding to the response of the low-order equivalent model to the control expected parameter model is the first flight quality level.

6. The controller parameter determination method of claim 1, wherein, The method for determining the optimal value of the controller parameter of the nonlinear aircraft control model by using a global optimization algorithm according to the ideal model, the nonlinear aircraft control model, the aircraft body dynamics model and the initial controller parameter comprises: adjusting the controller parameter of the nonlinear aircraft control model according to the initial controller parameter by using the global optimization algorithm; when the response result of the aircraft body dynamics model controlled by using the nonlinear aircraft control model is the same as the response result of the aircraft body dynamics model controlled by using the ideal model, determining the current controller parameter as the optimal value of the controller parameter.

7. A controller parameter determination apparatus characterized by comprising: The method comprises: a model acquisition module configured to acquire an ideal model based on flight quality design related to a frequency domain, a nonlinear aircraft control model based on a time domain and an aircraft body dynamics model; an initial controller parameter determination module configured to determine an initial controller parameter of the nonlinear aircraft control model; a controller parameter optimal value determination module configured to determine an optimal value of a controller parameter of the nonlinear aircraft control model by using a global optimization algorithm according to the ideal model, the nonlinear aircraft control model, the aircraft body dynamics model and the initial controller parameter; an optimal nonlinear aircraft control model determination module configured to obtain an optimal nonlinear aircraft control model according to the optimal value of the controller parameter; an overall flight control model determination module configured to obtain an overall flight control model according to the optimal nonlinear aircraft control model, the ideal model and the aircraft body dynamics model; a flight quality level processing module configured to determine a flight quality level of the overall flight control model and to process the optimal value of the controller parameter according to the flight quality level; The controller parameter optimal value determination module comprises: The controller parameter optimal value determination unit is configured to determine the controller parameter optimal value 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 an algorithm in which weights, learning factors and particle swarm selection algorithms of a particle swarm are improved; the weight form is improved to be self-adaptive adjustment of the weights, the learning factor is improved to be a synchronous learning factor, the particle swarm selection algorithm is improved to be a particle swarm selection algorithm based on a natural selection mechanism, and a user-defined focus target is determined in the improved particle swarm global optimization algorithm, including speed priority, stability priority and damping ratio priority.

8. A parameter optimization device for controlling, characterized by, The application further provides a computer readable storage medium storing a computer executable instruction, wherein the computer executable instruction is loaded and executed by a processor to implement the steps of the controller parameter determination method. The computer readable storage medium stores a computer executable instruction, wherein the computer executable instruction is loaded and executed by a processor to implement the steps of the controller parameter determination method. ​ 9. A computer-readable storage medium, characterized in that, ​

Images