Simulation control method and device for operating load system, and storage medium

Abstract

The present application belongs to the technical field of flight control, and particularly relates to a simulation control method and device of a control load system and a storage medium, aiming at solving the problem of limited effect when the simulation control of the control load system is performed in the prior art. The method of the present application comprises: obtaining control input data; filtering the data of the force or torque of the control stick, and based on the filtered data of the force or torque of the control stick and the control position data, performing dynamic simulation on the control load system through a pre-constructed digital twin system to determine the virtual force or torque trajectory of the control load system for responding to the control input data; obtaining the current flight parameters of the flight simulator, and solving according to a preset aerodynamics model to obtain a reference force or torque trajectory; taking the reference force or torque trajectory as a control target, optimizing and solving the virtual force or torque trajectory to determine the actuator driving instruction. The effect of the present application is better when the control of the control load system is performed.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of flight control, and particularly relates to a simulation control method, system and device of a control load system and a storage medium. BACKGROUND

[0002] A flight simulation training device (FSTD) is a system that reproduces a real flight environment and aircraft behavior through high-precision simulation technology, and is used for pilot training, skill assessment and emergency disposal drills. As the core equipment for pilot training, the authenticity and accuracy of the control load system of the flight simulator directly affect the training effect and safety. The control load system simulates the control force or torque feedback of the aircraft in different flight states (such as the resistance when the control surface is deflected, the pitch moment of the elevator, etc.), so that the pilot obtains consistent control behavior perception as the real aircraft.

[0003] However, the simulation control method of the existing control load system has key defects, which leads to limited simulation effect and is difficult to meet the actual needs, which is specifically manifested in that:

[0004] In one aspect, the control of the control load system relies on the pre-established physical model (such as the rigid body dynamics model based on linearized aerodynamic parameters), which is difficult to reflect the dynamic influence of the aerodynamic environment (such as airspeed, angle of attack, and atmospheric density change) on the control force or torque in real time during flight. For example, when the aircraft is in a high angle of attack or transonic flight state, the nonlinear characteristics of the aerodynamic force (such as shock-induced control surface chattering and sudden change in lift) will cause a significant deviation between the actual control force or torque and the predicted value of the simplified model, resulting in distorted simulation feedback and affecting the pilot's accurate judgment of the flight state;

[0005] On the other hand, the control algorithm does not adequately consider the constraints and real-time performance. Traditional control methods (such as PID control) can achieve basic force or torque tracking, but have obvious limitations in complex working conditions (such as large step input during high maneuvering flight). The physical constraints of the control load system actuator (such as maximum output torque and response rate limit) are not fully considered, which can easily lead to the generation of driving instructions that exceed the capacity of the actuator, causing system overload or simulation failure. SUMMARY

[0006] In order to solve the above problems in the prior art, i.e., the limited effect of the simulation control of the control load system in the prior art, the present application proposes, in a first aspect, a simulation control method of a control load system, applied to the control load system of a flight simulator, the method comprising:

[0007] Obtaining control input data, the control input data including control stick force or torque data applied to the control load system, and control position data;

[0008] filtering the joystick force or torque data, performing dynamic simulation on the control load system based on the filtered joystick force or torque data and the control position data through a pre-constructed digital twin system to determine a virtual force or torque trajectory of the control load system for responding to the control input data;

[0009] obtaining current flight parameters of the flight simulator, and solving the current flight parameters according to a preset aerodynamics model to obtain a reference force or torque trajectory;

[0010] taking the reference force or torque trajectory as a control target, and adopting a preset predictive control algorithm to optimize and solve the virtual force or torque trajectory to determine an actuator driving instruction of the control load system.

[0011] In some preferred embodiments, the construction process of the digital twin system comprises:

[0012] determining a motor electromagnetic equation, a rigid body dynamics equation and a heat conduction equation of the control load system, and constructing a multi-field coupling model of the control load system;

[0013] applying a preset amplitude step current signal to an actual actuator to collect force or torque response data, and calibrating dynamic characteristic parameters of the multi-field coupling model, the dynamic characteristic parameters comprising a torque constant, an equivalent inertia and a viscous damping coefficient;

[0014] determining a mapping relationship between an elastic load deformation and an output force of the control load system based on the calibrated dynamic characteristic parameters to construct a load mapping model of the control load system;

[0015] connecting the multi-field coupling model and the load mapping model in series to obtain the digital twin system, wherein an output of the multi-field coupling model is taken as an input of the load mapping model.

[0016] In some preferred embodiments, the dynamic simulation on the control load system through the pre-constructed digital twin system comprises:

[0017] determining an equivalent driving current according to the filtered joystick force or torque data and the current control position data;

[0018] inputting the equivalent driving current into the digital twin system to output an initial virtual force or torque through a multi-field coupling model of the digital twin system;

[0019] performing elastic deformation compensation on the initial virtual force or torque through a load mapping model of the digital twin system to obtain an intermediate virtual force or torque;

[0020] The intermediate virtual force or torque is dynamically hysteresis compensated to obtain the virtual force or torque trajectory.

[0021] In some preferred embodiments, the dynamic hysteresis compensation of the intermediate virtual force or torque to obtain the virtual force or torque trajectory comprises:

[0022] Obtaining temperature data of an actuator transmission mechanism of the digital twin system;

[0023] According to the temperature data, determining a stiffness attenuation coefficient of the harmonic reducer;

[0024] According to the stiffness attenuation coefficient, determining an effective stiffness at the current temperature;

[0025] According to the effective stiffness, combining historical manipulation position data, determining a hysteresis force component;

[0026] According to the hysteresis force component, dynamically hysteresis compensating the intermediate virtual force or torque.

[0027] In some preferred embodiments, the stiffness attenuation coefficient satisfies the following formula:

[0028] ;

[0029] In the formula, is the stiffness attenuation coefficient, is the nominal stiffness coefficient, β is the temperature attenuation coefficient, T is the real-time temperature, and T0 is the reference temperature;

[0030] The effective stiffness satisfies the following formula:

[0031] ;

[0032] In the formula, is the effective stiffness, is the reference stiffness coefficient, is the stiffness attenuation coefficient;

[0033] The hysteresis force component satisfies the following formula:

[0034] ;

[0035] In the formula, is the effective stiffness, is the hysteresis operator weight, is the displacement threshold sequence, x(t) is the current joystick displacement, x(t−Δt) is the joystick displacement of the last period, and H( ) is the unit step function.

[0036] In some preferred embodiments, the calculating the current flight parameter according to a preset aerodynamic model to obtain a reference force or torque trajectory comprises:

[0037] determining an airspeed, an angle of attack, an altitude, and a rudder deflection angle according to the current flight parameter;

[0038] calling a derivative table of an aerodynamic model based on the airspeed, the angle of attack, the altitude, and the rudder deflection angle to determine a lift coefficient, a drag coefficient, and a pitching moment coefficient in a current state;

[0039] determining a hinge moment reference value based on the rudder deflection angle and a preset aerodynamic derivative;

[0040] smoothing the hinge moment reference value based on the lift coefficient, the drag coefficient, and the pitching moment coefficient to generate the reference force or torque trajectory.

[0041] In some preferred embodiments, the optimizing and solving the virtual force or torque trajectory by using a preset predictive control algorithm comprises:

[0042] determining a discrete state space equation by taking the virtual force or torque trajectory as a state variable of a prediction model;

[0043] defining an objective function for optimizing a future control sequence, the objective function satisfying:

[0044] ;

[0045] wherein Np is a prediction time domain, Nc is a control time domain, Q and R are preset weight matrices, is a reference output at a future k+i time, is an actual output of the prediction system at the future k+i time, K is a current time, i is a step index of a prediction output, and j is a step index of a control input change, is a weighted Euclidean norm square, is a control input variable, and λ is a control weight scalar;

[0046] solving a minimum value of the objective function under a preset constraint condition based on the discrete state space equation at each sampling time to obtain an optimal control sequence;

[0047] outputting a first control amount of the optimal control sequence as an actuator driving instruction.

[0048] In some preferred embodiments, the method further comprises:

[0049] outputting the actuator driving instruction as a reference control instruction to a corresponding actuator in the control load system;

[0050] acquiring a stick force or torque feedback value output by the control loading system in real time, determining a tracking error between an expected stick force or torque value corresponding to the reference control instruction and the stick force or torque feedback value;

[0051] adjusting the reference control instruction by a preset feedback control algorithm according to the tracking error, so as to perform tracking control on the control loading system.

[0052] In a second aspect, the present application further provides an electronic device, comprising:

[0053] at least one processor; and,

[0054] a memory in communication connection with the at least one processor; wherein,

[0055] the memory stores instructions executable by the processor, and the instructions are used to be executed by the processor to implement the method according to the first aspect.

[0056] In a third aspect, the present application further provides a computer readable storage medium, which stores computer instructions, and the computer instructions are used to be executed by the computer to implement the method according to the first aspect.

[0057] The present application has the following beneficial effects:

[0058] The present application is based on the filtered control input data and position data, obtains a virtual trajectory through simulation by a pre-constructed digital twin system, and further corrects the virtual trajectory in combination with a reference force or torque trajectory calculated according to current flight parameters, so as to realize the cooperation of the digital twin system and the aerodynamics model; the digital twin system generates an initial virtual trajectory based on the physical characteristics (such as rigid body dynamics characteristics) of the control loading system, and the aerodynamics model calculates an ideal reference trajectory according to real-time flight parameters, and the two correct the virtual trajectory in real time through data interaction, so that the virtual trajectory can dynamically adapt to complex aerodynamic environment (such as nonlinear aerodynamic force changes in high angle of attack and transonic flight), and the matching degree of the virtual force or torque trajectory and the control feedback in real flight state is significantly improved. BRIEF DESCRIPTION OF DRAWINGS

[0059] Other features, objects and advantages of the present application will become more apparent from the following detailed description of non-limiting embodiments made with reference to the accompanying drawings:

[0060] Figure 1 is a flowchart of a simulation control method of a control loading system according to an embodiment of the present application;

[0061] Figure 2 is a schematic diagram of a simulation control system according to an embodiment of the present application;

[0062] Figure 3 is a schematic diagram of a computer system according to an embodiment of the present application. DETAILED DESCRIPTION

[0063] The application will be further described below in conjunction with the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the related application, and not to limit the application. In addition, it should be noted that only the parts related to the application are shown in the drawings for ease of description.

[0064] It should be noted that the embodiments and features in the embodiments can be combined with each other without conflict. The application will be described in detail below with reference to the drawings and in conjunction with the embodiments.

[0065] Please refer to Figure 1 The first embodiment of the present application provides a simulation control method for a control load system, which comprises:

[0066] Step S10, obtaining control input data, the control input data comprising joystick force or torque data and control position data applied to the control load system;

[0067] In this embodiment, the control input data can be collected in real time by a sensor array independently integrated inside the control load system. The control input data includes two dimensions: one is the joystick force or torque data measured by a six-dimensional force or torque sensor, which is used to reflect the operating force and torque applied by the pilot; the other is the control position data measured by an optical encoder or a potentiometer, which is used to accurately record the displacement state of the joystick.

[0068] In this embodiment, the control input data collected by the sensor array is transmitted to the data processing unit of the flight simulator after hardware synchronization. The data processing unit simultaneously undertakes the management of the control load system and the generation of real-time instructions.

[0069] Step S20, filtering the joystick force or torque data, and based on the filtered joystick force or torque data and the control position data, performing dynamic simulation on the control load system through the digital twin system to determine the virtual force or torque trajectory of the control load system for responding to the control input data;

[0070] It is easy to understand that the control load system of the flight simulator is a complex electromechanical system, which contains significant nonlinear factors (such as Coulomb friction, clearance, elastic deformation) and complex dynamic coupling relationships. If a linear model based on fixed parameters or a simple transfer function in the prior art is used, it is difficult to accurately describe the real behavior, which further leads to distortion of the simulation results.

[0071] To this end, the embodiment constructs a high-fidelity virtual model of the control load system by integrating physical design data, material property data, and historical operation data, which can accurately reproduce the nonlinear dynamic characteristics of the control load system in actual scenarios, thereby simulating the subtle responses of the system at different positions, speeds, and temperatures.

[0072] In addition, based on the digital twin model in the embodiment, the finally determined control algorithm can be first tested, verified, and parameter optimized on the digital twin (i.e., "digital first flight"), and then deployed to the physical system after the performance is stable, thereby effectively reducing the development risk and cost and accelerating the iteration speed.

[0073] Specifically, the real-time filtering process in the embodiment can use an adaptive Kalman filtering algorithm to eliminate measurement noise and mechanical vibration interference while retaining the effective force sensation features in the original data.

[0074] In the embodiment, the filtered data and the control position data are synchronously input into the digital twin system, which constructs an accurate dynamic model of the control load system. The model can simulate and calculate various elements including mechanical transmission characteristics, friction models, and elastic deformation, thereby enabling the digital twin system to generate a virtual force or torque trajectory reflecting the dynamic response of the system through real-time simulation calculation.

[0075] Step S30: Obtain the current flight parameters of the flight simulator, and calculate the reference force or torque trajectory according to the preset aerodynamics model;

[0076] In the embodiment, the current flight parameters, including airspeed, angle of attack, sideslip angle, altitude, and other state variables, are obtained from the flight dynamics model of the flight simulator. These parameters are input into the preset high-precision aerodynamics model (which can generally be established based on computational fluid dynamics data and empirical formulas), and the theoretical hinge torque of the control surface under the current flight state, i.e., the reference force or torque trajectory, is obtained through real-time calculation.

[0077] Step S40: Using the reference force or torque trajectory as the control target, the virtual force or torque trajectory is optimized and solved using a preset predictive control algorithm to determine the actuator driving instructions of the control load system.

[0078] In this embodiment, a model predictive control algorithm is used to track the reference force or torque trajectory and optimize the virtual force or torque trajectory. The model predictive control algorithm establishes a state space model of the system and solves an optimal control problem in a finite time domain at each sampling period to generate an actuator driving instruction that can make the actual output best track the reference trajectory. This instruction can be directly used to drive the motor or hydraulic actuator after power amplification, and finally generate a highly realistic force feedback on the control load system.

[0079] Further, the construction process of the digital twin system comprises:

[0080] The motor electromagnetic equation, rigid body dynamics equation and heat conduction equation of the control load system are determined to construct a multi-field coupled model of the control load system. A step current signal with a preset amplitude is applied to the actual actuator to collect force or torque response data to calibrate the dynamic characteristic parameters of the multi-field coupled model, including torque constant, equivalent inertia and viscous damping coefficient. Based on the calibrated dynamic characteristic parameters, the mapping relationship between the elastic load deformation and the output force of the control load system is determined to construct a load mapping model of the control load system. The multi-field coupled model and the load mapping model are connected in series to obtain the digital twin system, wherein the output of the multi-field coupled model is used as the input of the load mapping model.

[0081] In this embodiment, the motor electromagnetic equation is used to describe how the electrical energy in the control load system is converted into mechanical energy (torque), i.e., the power source of the actuator driving mechanism; the rigid body dynamics equation is used to describe the motion law (such as displacement, velocity, acceleration) of the control load system under the action of torque; and the heat conduction equation is used to describe the heating and cooling of the control load system during operation, and the temperature change will affect the electromagnetic performance and material properties.

[0082] In this embodiment, the electric, magnetic, mechanical, thermal and other physical fields are coupled in the same model, thereby providing a more extensive working interval for the digital twin, which is not limited to a specific working condition, but more generally simulates in multiple working conditions.

[0083] In this embodiment, the step response test is used to determine the step response data of the system. A known step current signal is applied to the actual actuator, and the response data of the output force or torque changing with time is collected to construct the response curve. By fitting the response curve measured by the step response test with the response curve simulated by the model, a set of dynamic characteristic parameters that best fit the actual physical system can be deduced and calibrated. Based on this, the digital twin model and the control load system are synchronized with high precision.

[0084] It is easy to understand that the force output by the manipulation load system depends not only on the motor itself, but also on the load driven by the motor. For example, the same command drives different loads (such as different linkage mechanisms), and the final force sensation is different. Therefore, the embodiment constructs a load mapping model of the manipulation load system to compensate for the flexible deformation, clearance and other nonlinear factors of the transmission mechanism, so that the digital twin can predict the force finally acting on the pilot's hand, rather than only the output torque of the motor.

[0085] Further, in the embodiment, the dynamic simulation of the manipulation load system by the digital twin system comprises:

[0086] According to the filtered joystick force or torque data and the current manipulation position data, an equivalent driving current is determined; the equivalent driving current is input to the digital twin system, and an initial virtual force or torque is output by a multi-field coupling model of the digital twin system; the initial virtual force or torque is compensated for elastic deformation by a load mapping model of the digital twin system to obtain an intermediate virtual force or torque; and the intermediate virtual force or torque is compensated for dynamic hysteresis to obtain the virtual force or torque trajectory.

[0087] Further, in the embodiment, the dynamic hysteresis compensation of the intermediate virtual force or torque to obtain the virtual force or torque trajectory comprises:

[0088] Temperature data of an actuator transmission mechanism of the digital twin system is obtained; a stiffness attenuation coefficient of a harmonic reducer is determined according to the temperature data; an effective stiffness at the current temperature is determined according to the stiffness attenuation coefficient; a hysteresis force component is determined according to the effective stiffness in combination with historical manipulation position data; and the intermediate virtual force or torque is compensated for dynamic hysteresis according to the hysteresis force component.

[0089] The calculation formula of the stiffness attenuation coefficient satisfies:

[0090] ;

[0091] In the formula, K is the stiffness attenuation coefficient, K0 is a nominal stiffness coefficient, β is a temperature attenuation coefficient, T is a real-time temperature, and T0 is a reference temperature.

[0092] The calculation formula of the effective stiffness satisfies:

[0093] ;

[0094] In the formula, K is the effective stiffness, K0 is a nominal stiffness coefficient, β is a temperature attenuation coefficient, T is a real-time temperature, and T0 is a reference temperature. ​​is a stiffness decay coefficient;

[0095] the hysteresis force component, the calculation formula of which satisfies:

[0096]

[0097] wherein, is an effective stiffness, is a hysteresis operator weight, is a displacement threshold sequence, x(t) is a current joystick displacement, x(t−Δt) is a joystick displacement of a previous period, and H( ) is a unit step function.

[0098] Further, in the above embodiment, the current flight parameter is calculated according to a preset aerodynamic model to obtain a reference force or torque trajectory, including:

[0099] According to the current flight parameter, airspeed, angle of attack, height, and rudder deflection angle are determined; based on the airspeed, angle of attack, height, and rudder deflection angle, a derivative table of an aerodynamic model is called to determine lift coefficients, drag coefficients, and pitching moment coefficients in the current state; based on the rudder deflection angle and preset aerodynamic derivatives, a hinge moment reference value is determined; and based on the lift coefficients, drag coefficients, and pitching moment coefficients, the hinge moment reference value is smoothed to generate the reference force or torque trajectory.

[0100] Further, in the above embodiment, the virtual force or torque trajectory is optimized and solved by using a preset predictive control algorithm, including:

[0101] The virtual force or torque trajectory is taken as a state variable of a prediction model to determine a discrete state space equation;

[0102] A target function is defined for optimizing a future control sequence, and the target function satisfies:

[0103]

[0104] wherein, Np is a prediction time domain, Nc is a control time domain, Q and R are preset weight matrices, is a reference output at a future k+i moment, is an actual output of a prediction system at the future k+i moment, K is a current moment, i is a step index of a prediction output, and j is a step index of a control input change, is a weighted Euclidean norm square, is a control input variable, and λ is a control weight scalar;

[0105] At each sampling moment, a minimum value of the target function under a preset constraint condition is solved based on the discrete state space equation to obtain an optimal control sequence.​​

[0106] outputting the first control quantity of the optimal control sequence as an actuator driving instruction.

[0107] Further, in the above embodiment, the method further comprises:

[0108] outputting the actuator driving instruction as a reference control instruction to a corresponding actuator in the control load system; acquiring a joystick force or torque feedback value output by the control load system in real time, determining a tracking error between an expected joystick force or torque value corresponding to the reference control instruction and the joystick force or torque feedback value; and adjusting the reference control instruction according to the tracking error through a preset feedback control algorithm to perform tracking control on the control load system.

[0109] The embodiment further proposes a feedforward-feedback composite control strategy on the basis of the foregoing steps, aiming to further improve the control accuracy, robustness and anti-interference ability of the force sensation simulation system.

[0110] Specifically, the embodiment first outputs the actuator driving instruction calculated by the predictive control algorithm as a reference control instruction to the physical actuator (such as a motor) of the flight simulator; at the same time, a high-precision force or torque sensor is used to measure the force or torque value (joystick force or torque feedback value) actually output by the control load system in real time.

[0111] On this basis, the actual measurement value is compared with the expected joystick force or torque value (i.e., the reference target) predicted by the digital twin system in real time, and a tracking error is obtained. This error signal reveals all the differences between the ideal model and the physical reality, including but not limited to: minor mismatches of the digital twin model, external disturbances not included in the model (such as temperature drift, cable pulling, mechanical wear), slow time-varying characteristics of the system nonlinearity, etc.

[0112] Further, a preset feedback control algorithm (such as PID control, adaptive control, etc.) is used to generate a correction amount (adjust the reference control instruction) according to the tracking error calculated in real time. This correction amount is used to offset the error identified in the previous step. The feedback controller tends the error to zero through real-time calculation and correction, thereby ensuring that the actual output of the system conforms to the expected target as much as possible.

[0113] The application combines digital twin technology with traditional predictive control through the above-mentioned embodiments, predicts the dynamic characteristics of the system in advance by establishing a high-precision virtual simulation model, and then optimizes and compensates through the predictive control algorithm, effectively solving the problems of nonlinearity, time-varying and delay in the control load system of the flight simulator, significantly improving the realism and accuracy of force sensation simulation, and effectively improving the simulation effect of the control load system.

[0114] Further, referring to Figure 2 , the second embodiment of the present application provides a simulation control system, comprising:

[0115] a data acquisition module 210, configured to acquire manipulation input data, wherein the manipulation input data comprises joystick force or torque data and manipulation position data applied to the manipulation load system;

[0116] a data filtering module 220, configured to perform filtering processing on the joystick force or torque data, and to determine a virtual force or torque trajectory of the manipulation load system for responding to the manipulation input data by performing dynamic simulation on the manipulation load system based on the filtered joystick force or torque data and the manipulation position data;

[0117] a parameter solving module 230, configured to acquire current flight parameters of the flight simulator, and to obtain a reference force or torque trajectory by solving the current flight parameters according to a preset aerodynamics model;

[0118] a predictive control module 240, configured to take the reference force or torque trajectory as a control target, to perform optimization solving on the virtual force or torque trajectory by using a preset predictive control algorithm, and to determine an actuator driving instruction of the manipulation load system.

[0119] The third embodiment of the present application further provides an electronic device, comprising:

[0120] at least one processor; and a memory connected with the at least one processor in communication, wherein the memory stores instructions executable by the processor, and the instructions are used to be executed by the processor to implement the method of the first embodiment.

[0121] The fourth embodiment of the present application further provides a computer readable storage medium, wherein the computer readable storage medium stores computer instructions, and the computer instructions are used to be executed by the computer to implement the method of the first embodiment.

[0122] Reference is made to Figure 3 which shows a structural schematic diagram of a computer system of a server suitable for implementing the method, system and device embodiments of the present application. Figure 3 The server shown is only an example, and should not bring any limitation to the functions and use range of the embodiments of the present application.

[0123] As Figure 3As shown, the computer system includes a central processing unit 301 which can perform various appropriate actions and processes according to programs stored in a read only memory 302 or programs loaded from the storage section 308 into a random access memory 303. In the random access memory 303, various programs and data required for the operation of the system are also stored. The central processing unit 301, the read only memory 302, and the random access memory 303 are connected to each other through a bus 304. An I / O interface 305 is also connected to the bus 304.

[0124] Connected to the I / O interface 305 are an input section 306 including a keyboard, a mouse, etc.; an output section 307 including a display such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as necessary. A removable media 311 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is attached to the drive 310 as necessary, so that a computer program read therefrom is installed in the storage section 308 as necessary.

[0125] In particular, the processes described above with reference to the flowcharts can be implemented as a computer software program according to embodiments of the present application. For example, embodiments of the present application include a computer program product comprising a computer program carried on a computer readable medium, the computer program containing program code for executing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network by the communication section 309, and installed from the removable media 311. When the computer program is executed by the central processing unit 301, the above-described functions defined in the methods of the present application are performed. It should be noted that the computer readable medium described above in the present application can be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable storage medium may, for example, be - but is not limited to - an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any combination of the above.

[0126] More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0127] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0128] The computer program product of the present application can be a computer program embodied on a non-transitory computer readable medium. Such non-transitory computer readable medium can include, but is not limited to, floppy diskettes, CD-ROMs, DVDs, flash memories, memory sticks, and hard drives.

[0129] The terms "first", "second", etc. are used to distinguish similar objects, rather than to describe or indicate a particular order or sequence.

[0130] The term "comprising" or any other similar term is intended to encompass the inclusion of one or more stated elements, steps, integers, or components, but does not preclude the inclusion of one or more other elements, steps, integers, or components not expressly listed or the presence of one or more other elements, steps, integers, or components that are well-known in the art.

[0131] The technical solutions of the present application have been described above in conjunction with the preferred embodiments shown in the drawings.

[0132] The above only describes the embodiments of the present application and is not intended to limit the present application. The present application can have various modifications and changes for those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the scope of claims of the present application.

Claims

1. A simulation control method for a control load system, applied to the control load system of a flight simulator, characterized in that, The method includes: Acquire manipulation input data, which includes data on the lever force or torque applied to the manipulation load system, and manipulation position data; The joystick force or torque data is filtered, and based on the filtered joystick force or torque data and the control position data, the control load system is dynamically simulated through a pre-built digital twin system to determine the virtual force or torque trajectory of the control load system in response to the control input data. The current flight parameters of the flight simulator are obtained, and the current flight parameters are calculated according to the preset aerodynamic model to obtain the reference force or torque trajectory. Using the reference force or torque trajectory as the control target, a preset predictive control algorithm is used to optimize and solve the virtual force or torque trajectory to determine the actuator drive command of the load control system. The dynamic simulation of the manipulated load system using a pre-built digital twin system includes: Based on the filtered joystick force or torque data and the control position data, the equivalent drive current is determined; the equivalent drive current is input to the digital twin system, and the initial virtual force or torque is output through the multi-field coupling model of the digital twin system; the initial virtual force or torque is elastically deformed and compensated through the load mapping model of the digital twin system to obtain the intermediate virtual force or torque; the intermediate virtual force or torque is dynamically hysteresis compensated to obtain the trajectory of the virtual force or torque. The step of calculating the current flight parameters based on a preset aerodynamic model to obtain a reference force or torque trajectory includes: Based on the current flight parameters, determine the airspeed, angle of attack, altitude, and control surface deflection. Based on the airspeed, angle of attack, altitude, and control surface deflection, the derivative table of the aerodynamic model is called to determine the lift coefficient, drag coefficient, and pitching moment coefficient under the current state. Based on the control surface deflection angle and the preset aerodynamic derivative, determine the hinge torque reference value; Based on the lift coefficient, drag coefficient, and pitch moment coefficient, the hinge moment reference value is smoothed to generate the reference force or moment trajectory.

2. The simulation control method for a load manipulation system according to claim 1, characterized in that, The construction process of the digital twin system includes: Determine the electromagnetic equations of the motor, the dynamic equations of the rigid body, and the heat conduction equations of the control load system, and construct a multi-field coupling model of the control load system. By applying a step current signal of preset amplitude to the actual actuator, force or torque response data is collected to calibrate the dynamic characteristic parameters of the multi-field coupling model. The dynamic characteristic parameters include torque constant, equivalent inertia and viscous damping coefficient. Based on the calibrated dynamic characteristic parameters, the mapping relationship between the elastic load deformation and the output force of the control load system is determined, so as to construct the load mapping model of the control load system; The digital twin system is obtained by concatenating the multi-field coupling model with the load mapping model, wherein the output of the multi-field coupling model is used as the input of the load mapping model.

3. The simulation control method for a load manipulation system according to claim 1, characterized in that, The step of performing dynamic hysteresis compensation on the intermediate virtual force or torque to obtain the trajectory of the virtual force or torque includes: Acquire temperature data of the actuator transmission mechanism of the digital twin system; Based on the temperature data, determine the stiffness attenuation coefficient of the harmonic reducer; The effective stiffness at the current temperature is determined based on the stiffness attenuation coefficient. Based on the effective stiffness and combined with historical manipulation position data, the hysteresis force component is determined. Dynamic hysteresis compensation is performed on the intermediate virtual force or torque based on the hysteresis force component.

4. The simulation control method for a load manipulation system according to claim 3, characterized in that, The stiffness attenuation coefficient is calculated using the following formula: ； In the formula This is the stiffness attenuation coefficient. β is the nominal stiffness coefficient, T is the temperature decay coefficient, T is the real-time temperature, and T0 is the reference temperature. The effective stiffness is calculated using the following formula: ； In the formula, For effective stiffness, As the reference stiffness coefficient, This is the stiffness attenuation coefficient; The calculation formula for the hysteresis force component satisfies: ； In the formula, For effective stiffness, For the weights of the hysteresis operator, Let x(t) be the current joystick displacement, x(t−Δt) be the joystick displacement of the previous cycle, and H() be the unit step function.

5. The simulation control method for a load manipulation system according to claim 1, characterized in that, The step of using a preset predictive control algorithm to optimize and solve the virtual force or torque trajectory includes: Using the virtual force or torque trajectory as the state variable of the prediction model, the discrete state-space equation is determined; Define an objective function to optimize future control sequences, wherein the objective function satisfies: ； In the formula, N p For predicting the time domain, N c To control the time domain, Q and R are preset weight matrices. As a reference output for the future k+i time, To predict the actual output of the system at a future time k+i, where K is the current time, i is the step index for predicting the output, and j is the step index for controlling the input change, For the weighted square of the Euclidean norm, To control the input variables, λ is the control weight scalar; At each sampling time, based on the discrete state-space equation, the minimum value of the objective function under preset constraints is solved to obtain the optimal control sequence; The first control variable of the optimal control sequence is output as the actuator drive instruction.

6. The simulation control method for a load manipulation system according to claim 1, characterized in that, The method further includes: The actuator drive command is used as a reference control command and output to the corresponding actuator in the load control system; The joystick force or torque feedback value output by the control load system is acquired in real time, and the tracking error between the expected joystick force or torque value corresponding to the reference control command and the joystick force or torque feedback value is determined. Based on the tracking error, the reference control command is adjusted through a preset feedback control algorithm to perform tracking control on the manipulated load system.

7. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to at least one of the processors; wherein, The memory stores instructions executable by the processor to implement the method according to any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are executed by the computer to implement the method of any one of claims 1-6.

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