A multi-mode simulation method for flight environment simulation control system

Abstract

This application provides a multi-mode simulation method for a flight environment simulation control system, belonging to the field of aero-engine technology. The method includes selecting either a fully digital simulation experiment or a semi-physical simulation experiment, running an object model and a control model. The control model includes a feedforward controller, an active disturbance rejection controller, and an extended state observer. Simulation parameters are calculated: the feedforward controller calculates the feedforward control quantity based on the engine speed change rate output by the object model; the extended state observer estimates the total system disturbance and feeds it back to the active disturbance rejection controller; an initial control quantity is calculated; the initial control quantity is superimposed with the feedforward control quantity and the estimated value to obtain the final control quantity, which is then input into the hydraulic regulating valve flow model within the object model. The control parameters of the control model are tuned through a fully digital simulation experiment, and the tuned control parameters are optimized through a semi-physical simulation experiment, thus improving simulation confidence and efficiency while reducing costs.

Description

Technical Field

[0001] This application relates to the field of aero-engine technology, and in particular to a multi-mode simulation method for a flight environment simulation control system. Background Technology

[0002] With the rapid development of simulation technology and artificial intelligence technology, flight environment simulation systems in the field of aero-engine flight environment simulation are facing severe challenges such as the rapid development of intelligent control technology and the improvement of simulation testing capabilities. There is an urgent need to develop high-confidence simulation devices to test and study equipment models and simulation methods. At the same time, conducting technical verification or testing directly on flight environment simulation systems and devices through physical testing methods has problems such as high physical testing costs (hundreds of thousands of yuan / hour) and high debugging risks. In particular, the direct debugging and verification of newly developed control technologies on real test bench equipment poses significant technical risks and safety hazards.

[0003] Due to the significant technological advantages of simulation verification technology and virtual testing in reducing technical risks, shortening development cycles, and lowering costs, they have gradually become new approaches for test control technology and equipment modeling and simulation. For a long time, the field of flight environment simulation testing has mainly relied on digital modeling offline simulation methods to conduct partial simulation studies on equipment models or typical operating conditions. For example, the paper "Modeling Method and Device for High-Altitude Test Stands of Aero-engines, Electronic Equipment and Storage Medium" (Publication No.: CN115616933A) proposed a modeling method for aero-engine test stands to establish a digital twin model in virtual space, but it did not involve hardware re-loop simulation or equipment adaptability verification. The paper "Virtual Test System, Method, Electronic Equipment and Medium for High-Altitude Test Stands of Aero-engines" (Publication No.: CN115329605A) proposed a joint simulation device consisting of a simulation module and a main control module. Multiple simulation units are equipped with simulation models. The time management unit of the main control module obtains the ideal machine time for time synchronization through a time server, without involving semi-physical simulation or intelligent control issues. To address the issue of high-precision control of flight environment pressure, the research on composite control technology in intake and exhaust pressure regulation systems (Measurement and Control Technology, No. 11, 2009) proposed a composite control method combining feedforward and fuzzy control, which improved intake pressure accuracy. However, it did not involve control methods linked to engine speed. A patent for a high-altitude test bench intake system disturbance rejection control method based on non-smooth feedback functions (Publication No.: CN115562002B) uses a non-smooth feedback sliding mode controller algorithm and a nonlinear enhanced filtering algorithm in the intake control system to achieve intake pressure control during aero-engine transient tests. This method did not involve feedforward or intelligent disturbance rejection control methods. Summary of the Invention

[0004] In view of this, the embodiments of this application provide a multi-mode simulation method for flight environment simulation control system, which at least partially solves the problem of integrating low-cost and risk-free scientific research verification platforms in the prior art, and improves simulation confidence and efficiency.

[0005] This application provides a multi-mode simulation method for a flight environment simulation control system. The flight environment simulation control system includes a real-time simulation device, a PLC device, a hydraulic pump station, a throttle lever simulation device, and a regulating valve. The method includes:

[0006] Start the hydraulic pump station, regulating valve, PLC device and real-time simulation device, select the full digital simulation experiment or semi-physical simulation experiment, run the object model and control model. The object model includes the hydraulic regulating valve flow model, the air pipeline pressure and temperature model and the engine flow model connected in sequence. The control model includes the feedforward controller, the active disturbance rejection controller and the extended state observer.

[0007] When a semi-physical simulation experiment is selected, the running equipment includes a PLC device, a hydraulic pump station, a hydraulic regulating valve, and a real-time simulation device. The PLC master control mode or the real-time simulation device master control mode is selected. In the PLC master control mode, the control model runs in the PLC device and the object model runs in the real-time simulation device. In the real-time simulation device master control mode, both the control model and the object model run in the real-time simulation device.

[0008] When selecting the all-digital simulation experiment, the running equipment includes a PLC device and a real-time simulation device, and both the control model and the object model run within the real-time simulation device.

[0009] Set the initial conditions for simulation, and simultaneously call the control model and object model to calculate simulation parameters in real time;

[0010] The feedforward control quantity is calculated in real time based on the engine speed change rate output by the engine flow model by the feedforward controller; the total disturbance of the system is estimated by the extended state observer to obtain the estimated value, and then fed back to the active disturbance rejection controller.

[0011] The active disturbance rejection controller calculates the initial control quantity in real time. The initial control quantity is superimposed with the feedforward control quantity and the estimated value to obtain the final control quantity. The final control quantity is input to the hydraulic regulating valve flow model to realize the dynamic control of system pressure.

[0012] The control parameters of the control model are tuned through a fully digital simulation experiment. When the fully digital simulation experiment passes, a semi-physical simulation experiment is carried out to optimize the control parameters tuned in the fully digital simulation experiment, so as to meet the adaptability requirements of the control algorithm in the control model to the physical equipment.

[0013] According to a specific implementation of an embodiment of this application, when a semi-physical simulation experiment is selected, the PLC device and the real-time simulation device communicate in real time through an Ethernet switch; when a fully digital simulation experiment is selected, the object model and the control model interact with each other in real time through a reflective memory switch.

[0014] According to a specific implementation of an embodiment of this application, the initial simulation conditions include intake pressure, intake temperature, flight altitude, Mach number, engine speed, inlet pressure of the first hydraulic regulating valve, inlet temperature of the first hydraulic regulating valve, inlet pressure of the second hydraulic regulating valve, and inlet temperature of the second hydraulic regulating valve.

[0015] According to a specific implementation of an embodiment of this application, the hydraulic regulating valve flow model includes a first hydraulic regulating valve flow model and a second hydraulic regulating valve flow model;

[0016] The real-time calculation simulation parameters include:

[0017] The inlet pressure, inlet temperature, flow cross-sectional area, flow coefficient, and opening degree of the first hydraulic regulating valve are input into the flow model of the first hydraulic regulating valve to obtain the flow rate of the first hydraulic regulating valve.

[0018] The inlet pressure, inlet temperature, flow cross-sectional area, flow coefficient, and opening degree of the second hydraulic regulating valve are input into the flow model of the second hydraulic regulating valve to obtain the flow rate of the second hydraulic regulating valve.

[0019] Pushing the throttle lever simulation device causes a change in engine speed. The engine speed, throttle lever angle, flight altitude, and Mach number are input into the engine flow model to obtain the engine mass flow rate.

[0020] The flow rates of the first hydraulic regulating valve, the second hydraulic regulating valve, and the engine mass flow rate are input into the air network pressure and temperature model to calculate the intake air temperature and intake air pressure in real time.

[0021] According to a specific implementation of this application, the expression for the flow model of the first hydraulic regulating valve is:

[0022] ,

[0023] ,

[0024] in, Let m1 be the differential of the flow rate m1 of the first hydraulic regulating valve, P1 be the inlet pressure of the first hydraulic regulating valve, T1 be the inlet temperature of the first hydraulic regulating valve, and S1 be the flow cross-sectional area of ​​the first valve. Let be the first flow coefficient, k1 be the first valve opening, R be a specific gas constant, and r1 be the radius of the first hydraulic regulating valve;

[0025] The expression for the flow rate model of the second hydraulic regulating valve is:

[0026] ,

[0027] ,

[0028] in, Let P2 be the differential of the flow rate m2 of the second hydraulic control valve, P2 be the inlet pressure of the second hydraulic control valve, T2 be the inlet temperature of the second hydraulic control valve, and S2 be the flow cross-sectional area of ​​the second valve. Here, k2 is the second flow coefficient, k2 is the second valve opening, and r2 is the radius of the second hydraulic regulating valve.

[0029] The expression for the engine flow model is:

[0030] ,

[0031] in, Engine mass flow rate m engine The derivative of , n is the engine speed, H is the flight altitude, Ma is the Mach number, m0 is the reference flow rate, and a, b, and c are the first, second, and third coefficients determined by the experimental data calibration method, respectively;

[0032] The expression for the air pipeline pressure and temperature model is as follows:

[0033] ,

[0034] ,

[0035] Among them, T c P is the temperature of the intake manifold cavity. c V is the intake manifold cavity pressure. c T is the volume of the intake manifold cavity. engine For engine exhaust temperature, C p C is the isobaric specific heat capacity of the gas, C1 is the first average gas flow velocity, C2 is the second average gas flow velocity, and C... engine This is the third average airflow velocity.

[0036] According to a specific implementation of this application, the calculation formula for the feedforward control quantity is as follows:

[0037] ,

[0038] Among them, uf K is the feedforward control variable. f1 For the first feedforward gain, K f2 For the second feedforward gain, K f3 The third feedforward gain is given by sign, where sign is the sign function. The term represents the exponential decay, α is the quadratic gain coefficient, and β is the hysteresis compensation coefficient. For high-frequency attenuation gain, The rotational speed attenuation coefficient is... N is the rate of change of engine speed, n is the engine speed, N1 is the boundary value between the low speed range and the medium speed range, and N2 is the boundary value between the high speed range and the medium speed range.

[0039] According to a specific implementation of an embodiment of this application, the expression of the extended state observer is:

[0040] ,

[0041] Where z1 is the real-time estimate of the actual output intake pressure of the system by the expansion state observer. z1 is the derivative of z1, and z2 is an estimate of the rate of change of the system output pressure. z2 is the derivative of z2, and z3 is an estimate of the total disturbance of the system. It is the derivative of z3, and β1, β2, and β3 are the first, second, and third gain coefficients of the extended state observer, respectively. Here, b0 is the saturation interval coefficient, b0 is the normalized coefficient of the control gain, and u is the final control quantity. It is the switching threshold between linear and nonlinear operating ranges, e is the tracking error, and sat is the saturation function. It is an estimate of unknown dynamics and external disturbances.

[0042] According to a specific implementation of an embodiment of this application, the active disturbance rejection controller calculates an initial control quantity in real time, and then superimposes the initial control quantity with a feedforward control quantity and an estimated value to obtain a final control quantity, including:

[0043] The pressure setpoint of the step signal is converted into a smooth signal with a smooth transition by a tracking differentiator to avoid overshoot caused by the step signal.

[0044] The active disturbance rejection controller calculates the initial control quantity in real time based on the pressure setpoint for smooth transition, the real-time estimate of the actual output intake pressure of the system by the expansion state observer, and the estimate of the rate of change of the system output pressure.

[0045] The final control quantity is obtained based on the initial control quantity, the feedforward control quantity, and the estimate of the total system disturbance.

[0046] According to a specific implementation of an embodiment of this application, the expression for the tracking differentiator is:

[0047] ,

[0048] Where R1 is the speed factor, and v1 is the smoothed tracking signal of the pressure setpoint. Let v1 be the first value of the differential of v1, and v2 be the second value of the differential of v1. The derivative of v2 is given by r(t), where r(t) is the pressure setpoint.

[0049] According to a specific implementation of this application, the calculation formula for the initial control quantity is as follows:

[0050] ,

[0051] Where e is the tracking error, Let k2 be the derivative of the tracking error e, k2 be the control gain, and r be the derivative of the tracking error e. f The coefficient h determines the nonlinear intensity. f is the step size, fhan is the fastest control synthesis function, and u0 is the initial control input;

[0052] The formula for calculating the final control quantity is:

[0053] .

[0054] Beneficial effects:

[0055] This application addresses technical challenges such as accurate full-digital / semi-physical simulation of aero-engine flight environments, rapid pressure response and high-precision control in transient tests, and multi-mode simulation design and system integration. It designs a multi-mode simulation method for a flight environment simulation control system. This control system comprises a real-time simulation device, a PLC device, a throttle lever simulation device, a host computer, regulating valves, and a hydraulic pump station. It can perform both full-digital and semi-physical simulation experiments, and can select either PLC-based or real-time simulation-based master control modes for aero-engine flight environment simulation. High-precision pressure control is achieved through a composite control algorithm combining engine speed feedforward and active disturbance rejection in the control model. By constructing a flight environment simulation control system that integrates the PLC control system, equipment, and model, and utilizing full-digital and semi-physical technologies for aero-engine flight environment simulation and control method testing and verification, costs are reduced and simulation efficiency is improved. Attached Figure Description

[0056] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is a schematic diagram of the flight environment simulation control system according to an embodiment of the present invention;

[0058] Figure 2 This is a schematic diagram of a fully digital and semi-physical simulation experiment according to an embodiment of the present invention;

[0059] Figure 3 This is a schematic diagram of the computational logic of an object model and a control model according to an embodiment of the present invention.

[0060] Figure 4 The following is a simulation result diagram according to an embodiment of the present invention, wherein (a) is a pressure control effect diagram and (b) is a control quantity compensation and valve adjustment effect diagram;

[0061] Figure 5 Another simulation result diagram according to an embodiment of the present invention, wherein (a) is a simulation result diagram of air environment pressure, (b) is a simulation result diagram of Mach number, (c) is a simulation result diagram of flight altitude, and (d) is a simulation result diagram of engine flow rate;

[0062] Figure 6 The figures show a comparison of simulation results between the method of the present invention and the conventional PID control method, where (a) is a comparison of pressure results and (b) is a comparison of pressure deviation results. Detailed Implementation

[0063] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0064] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. This application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0065] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this application, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.

[0066] It should also be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. The illustrations only show the components related to this application and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0067] Furthermore, specific details are provided in the following description to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.

[0068] This application provides a multi-mode simulation method for a flight environment simulation control system, which is described below. Figures 1 to 6 Provide a detailed description.

[0069] In one embodiment, refer to Figures 1 to 3 A multi-mode simulation method for a flight environment simulation control system is provided. The flight environment simulation control system includes a real-time simulation device, a PLC device, a hydraulic pump station, a throttle lever simulation device, and a regulating valve. The method includes:

[0070] Step 1: Start the hydraulic pump station, regulating valve, PLC device and real-time simulation device, select the full digital simulation experiment or the semi-physical simulation experiment, and run the object model and control model. The object model includes the hydraulic regulating valve flow model, the air pipeline pressure and temperature model and the engine flow model connected in sequence. The control model includes the feedforward controller, the active disturbance rejection controller and the extended state observer.

[0071] When a semi-physical simulation experiment is selected, the running equipment includes a PLC device, a hydraulic pump station, a hydraulic regulating valve, and a real-time simulation device. The PLC master control mode or the real-time simulation device master control mode is selected. In the PLC master control mode, the control model runs in the PLC device and the object model runs in the real-time simulation device. In the real-time simulation device master control mode, both the control model and the object model run in the real-time simulation device.

[0072] When selecting the all-digital simulation experiment, the running equipment includes a PLC device and a real-time simulation device, and both the control model and the object model run within the real-time simulation device.

[0073] Step 2: Set the initial simulation conditions, and simultaneously call the control model and object model to calculate the simulation parameters in real time;

[0074] Step 3: Calculate the feedforward control quantity in real time based on the engine speed change rate output by the engine flow model using the feedforward controller; estimate the total system disturbance using the extended state observer and feed it back to the active disturbance rejection controller.

[0075] Step 4: The active disturbance rejection controller calculates the initial control quantity in real time. The initial control quantity is superimposed with the feedforward control quantity and the estimated value to obtain the final control quantity. The final control quantity is input to the hydraulic regulating valve flow model to realize the dynamic control of system pressure.

[0076] Step 5: Tune the control parameters of the control model through a fully digital simulation experiment. When the fully digital simulation experiment passes, conduct a semi-physical simulation experiment to optimize the control parameters tuned in the fully digital simulation experiment, so as to meet the adaptability requirements of the control algorithm in the control model to the physical equipment.

[0077] In practical implementation, when selecting the PLC master control mode, the control model runs within the PLC device. The control model includes a composite control algorithm for engine speed feedforward and active disturbance rejection, while the object model runs on a real-time simulation device. Taking intake pressure simulation as an example, the control principle is as follows: the control command is calculated by the engine speed feedforward and active disturbance rejection composite control algorithm built into the PLC device, forming a control signal. This signal is then transmitted through the PLC output module to the servo valve to drive the hydraulic regulating valve. The valve position feedback signal enters the PLC device through the PLC input module and is transmitted to the real-time simulation device via the network. The real-time simulation device calculates the flow rate of the hydraulic regulating valve in real time based on the valve position signal. Simultaneously, it runs the intake pipe network cavity model, engine flow model, etc., to calculate intake pressure signals, engine flow signals, etc., in real time, and transmits these signals to the CPU of the PLC device via the network. The CPU then runs the engine speed feedforward and active disturbance rejection composite control algorithm to achieve closed-loop control of the intake pressure.

[0078] When selecting the real-time simulation device as the main control mode, both the control model and the object model run within the real-time simulation device. Taking intake pressure simulation as an example, the control principle is as follows: the control command is calculated by the engine speed feedforward and active disturbance rejection composite control algorithm built into the real-time simulation device, forming a control signal that is transmitted to the servo valve via the output board of the real-time simulation device to drive the hydraulic regulating valve. The valve position signal enters the real-time simulation device via the input board. The real-time simulation device calculates the flow rate of the hydraulic regulating valve in real time based on the valve position feedback signal, and simultaneously runs the intake pipe network cavity model, engine flow model, etc., to calculate real-time intake pressure, engine flow, and other signals, and performs pressure closed-loop control through the engine speed feedforward and active disturbance rejection composite control algorithm.

[0079] Furthermore, when a semi-physical simulation experiment is selected, the PLC device and the real-time simulation device communicate in real time through an Ethernet switch, and the PLC device, the real-time simulation device, and the control valve and equipment model operate in tandem to achieve hardware-in-the-loop semi-physical simulation; when a fully digital simulation experiment is selected, the object model and the control model interact with each other in real time through a reflective memory switch, which can perform digital simulation of the flight environment conditions of aero-engines.

[0080] In one embodiment, the initial simulation conditions include intake pressure, intake temperature, flight altitude, Mach number, engine speed, inlet pressure of the first hydraulic regulating valve, inlet temperature of the first hydraulic regulating valve, inlet pressure of the second hydraulic regulating valve, and inlet temperature of the second hydraulic regulating valve.

[0081] Furthermore, the hydraulic regulating valve flow model includes a first hydraulic regulating valve flow model and a second hydraulic regulating valve flow model;

[0082] The real-time calculation simulation parameters include:

[0083] The inlet pressure, inlet temperature, flow cross-sectional area, flow coefficient, and opening degree of the first hydraulic regulating valve are input into the flow model of the first hydraulic regulating valve to obtain the flow rate of the first hydraulic regulating valve.

[0084] The inlet pressure, inlet temperature, flow cross-sectional area, flow coefficient, and opening degree of the second hydraulic regulating valve are input into the flow model of the second hydraulic regulating valve to obtain the flow rate of the second hydraulic regulating valve.

[0085] Pushing the throttle lever simulation device causes a change in engine speed. The engine speed, throttle lever angle, flight altitude, and Mach number are input into the engine flow model to obtain the engine mass flow rate.

[0086] The flow rates of the first hydraulic regulating valve, the second hydraulic regulating valve, and the engine mass flow rate are input into the air network pressure and temperature model to calculate the intake air temperature and intake air pressure in real time.

[0087] Furthermore, the expression for the flow model of the first hydraulic regulating valve is:

[0088] ,

[0089] ,

[0090] in, Let m1 be the differential of the flow rate m1 of the first hydraulic regulating valve, P1 be the inlet pressure of the first hydraulic regulating valve, T1 be the inlet temperature of the first hydraulic regulating valve, and S1 be the flow cross-sectional area of ​​the first valve. Let be the first flow coefficient, k1 be the first valve opening, R be a specific gas constant, and r1 be the radius of the first hydraulic regulating valve;

[0091] The expression for the flow rate model of the second hydraulic regulating valve is:

[0092] ,

[0093] ,

[0094] in, Let P2 be the differential of the flow rate m2 of the second hydraulic control valve, P2 be the inlet pressure of the second hydraulic control valve, T2 be the inlet temperature of the second hydraulic control valve, and S2 be the flow cross-sectional area of ​​the second valve. Here, k2 is the second flow coefficient, k2 is the second valve opening, and r2 is the radius of the second hydraulic regulating valve.

[0095] The expression for the engine flow model is:

[0096] ,

[0097] in, Engine mass flow rate m engine The derivative of , n is the engine speed, H is the flight altitude, Ma is the Mach number, m0 is the reference flow rate, and a, b, and c are the first, second, and third coefficients determined by the experimental data calibration method, respectively;

[0098] The expression for the air pipeline pressure and temperature model is as follows:

[0099] ,

[0100] ,

[0101] Among them, T c P is the temperature of the intake manifold cavity. c V is the intake manifold cavity pressure. c T is the volume of the intake manifold cavity. engine For engine exhaust temperature, C p C is the isobaric specific heat capacity of the gas, C1 is the first average gas flow velocity, C2 is the second average gas flow velocity, and C... engine This is the third average airflow velocity.

[0102] In one embodiment, the feedforward controller includes a feedforward mapping function based on engine dynamic characteristics. The feedforward control quantity is calculated in real-time based on the engine speed change rate using this function. This feedforward control quantity is the feedforward compensation amount for the valve opening. The calculation formula for the feedforward control quantity is as follows:

[0103] ,

[0104] Among them, u f K is the feedforward control variable. f1 For the first feedforward gain, K f2 For the second feedforward gain, K f3 The third feedforward gain is given by sign, where sign is the sign function. The term represents the exponential decay, α is the quadratic gain coefficient, and β is the hysteresis compensation coefficient. For high-frequency attenuation gain, The rotational speed attenuation coefficient is... N is the rate of change of engine speed, n is the engine speed, N1 is the boundary value between the low speed range and the medium speed range, and N2 is the boundary value between the high speed range and the medium speed range.

[0105] In one embodiment, the expression for the extended state observer is:

[0106] ,

[0107] Where z1 is the real-time estimate of the actual output intake pressure of the system by the expansion state observer. z1 is the derivative of z1, and z2 is an estimate of the rate of change of the system output pressure. z2 is the derivative of z2, and z3 is an estimate of the total disturbance of the system. It is the derivative of z3, and β1, β2, and β3 are the first, second, and third gain coefficients of the extended state observer, respectively. Here, b0 is the saturation interval coefficient, b0 is the normalized coefficient of the control gain, and u is the final control quantity. It is the switching threshold between linear and nonlinear operating ranges, e is the tracking error, and sat is the saturation function. It is an estimate of unknown dynamics and external disturbances.

[0108] In specific implementation, the extended state observer outputs the real-time estimated value z1 of the actual output intake pressure of the system, the estimated value z2 of the rate of change of the system output pressure, and the estimated value z3 of the total system disturbance. The real-time estimated value z1 of the actual output intake pressure of the system is used to calculate the tracking error with the pressure setpoint. The estimated value z2 of the rate of change of the system output pressure is used to input the active disturbance rejection controller. The estimated value z3 of the total system disturbance is used to calculate the final control quantity together with the initial control quantity and the feedforward control quantity.

[0109] In one embodiment, the active disturbance rejection controller calculates an initial control quantity in real time, and then superimposes the initial control quantity with a feedforward control quantity and an estimated value to obtain a final control quantity, including:

[0110] The pressure setpoint of the step signal is converted into a smooth signal with a smooth transition by a tracking differentiator to avoid overshoot caused by the step signal.

[0111] The active disturbance rejection controller calculates the initial control quantity in real time based on the pressure setpoint for smooth transition, the real-time estimate of the actual output intake pressure of the system by the expansion state observer, and the estimate of the rate of change of the system output pressure.

[0112] The final control quantity is obtained based on the initial control quantity, the feedforward control quantity, and the estimate of the total system disturbance.

[0113] Furthermore, the expression for the tracking differentiator is:

[0114] ,

[0115] Where R1 is the speed factor, and v1 is the smoothed tracking signal of the pressure setpoint. Let v1 be the first value of the differential of v1, and v2 be the second value of the differential of v1. The derivative of v2 is given by r(t), where r(t) is the pressure setpoint.

[0116] Furthermore, the formula for calculating the initial control quantity is as follows:

[0117] ,

[0118] Where e is the tracking error, Let k2 be the derivative of the tracking error e, k2 be the control gain, and r be the derivative of the tracking error e. f The coefficient h determines the nonlinear intensity. f is the step size, fhan is the fastest control synthesis function, and u0 is the initial control input;

[0119] The formula for calculating the final control quantity is:

[0120] .

[0121] In practice, the final control quantity is used to control the servo valve to drive the valve action, increase or decrease the valve flow, so as to quickly offset the impact of disturbances and improve the rapid response of pressure control.

[0122] In one embodiment, the control parameters of the control model are tuned through a fully digital simulation experiment. When the fully digital simulation experiment passes, a semi-physical simulation experiment is conducted to optimize the control parameters tuned in the fully digital simulation experiment, in order to meet the adaptability requirements of the control algorithm in the control model to the physical equipment, including:

[0123] Step 5.1: Verify the engine speed feedforward and active disturbance rejection composite control algorithm through a fully digital simulation experiment, adjusting different feedforward control parameters (α, β, ...). When the percentage deviation between the controlled pressure and the pressure setpoint is no greater than ±0.5% for the active disturbance rejection control parameters (k2, b0), the simulation experiment can be considered successful.

[0124] Step 5.2: Based on the fully digital simulation experiment, a semi-physical simulation experiment is then conducted to test the feedforward control parameters (α, β, ...) tuned in the fully digital simulation experiment. The semi-physical simulation experiment was conducted to verify the active disturbance rejection control parameters (k2, b0). When the valve position of the regulating valve swings frequently, the deviation between the controlled pressure and the pressure setpoint increases or fails to converge, the control parameters need to be optimized and readjusted. When the percentage deviation between the controlled pressure and the pressure setpoint is no greater than ±0.5%, the semi-physical simulation experiment can be considered successful and the control algorithm is feasible for deployment.

[0125] Furthermore, the multi-mode simulation method for the flight environment simulation control system also includes:

[0126] By changing the test conditions and repeating steps 1 to 4 above, real-time simulation of various working conditions of the aero-engine can be achieved, verifying the adaptability of the control algorithm to various working conditions and ensuring the accuracy and efficiency of the simulation test.

[0127] This application constructs a flight environment simulation control system using a real-time simulation device, a PLC device, a host computer, a throttle lever simulation device, a hydraulic pump station, regulating valves, and a switch. The real-time simulation device is used to run the test equipment model and control model; the PLC device is used to control the algorithm, collect data, and monitor the equipment operation; the host computer is used for simulation task design, real-time monitoring, command input, and data viewing; the hydraulic pump station is used to provide power for the regulating valves; the regulating valves are used to simulate the operation of the test equipment and verify the adaptability of the algorithm; and the throttle lever simulation device is used to generate throttle lever signals and simulate the engine acceleration and deceleration process. The integrated simulation of PLC control system and regulating valve equipment enables high-confidence simulation of the operation process of large-scale test equipment. It can switch between fully digital and semi-physical simulation modes and can simulate the flight environment of aero-engines under various working conditions through two main control models: PLC and real-time simulation platform. Based on the composite controller composed of engine speed feedforward and active disturbance rejection, the feedforward controller obtains the engine speed change rate in real time to generate valve rapid compensation quality commands, and the extended state observer observes system disturbances in real time and quickly suppresses controlled pressure disturbances. Together, they achieve rapid pressure response and high-precision control, solving the integration problem of low-cost and risk-free scientific research verification platform and improving simulation confidence and efficiency.

[0128] In one embodiment, the pressure control effect, control quantity compensation, and valve regulation effect of the control system are as follows: Figure 4 As shown, rapid compensation and accelerated response capabilities were achieved through valve regulation. The results displaying changes in flight altitude, Mach number, ambient pressure, and engine flow rate are as follows: Figure 5 As shown, a comparison of the pressure control effects using the simulation method of this invention and the conventional PID control method is presented. Figure 6 The controlled pressure using the simulation method of this invention can quickly follow the change of the pressure setpoint, with a maximum deviation of 0.64 kPa and a convergence time of 2 seconds. The controlled pressure using the conventional PID control algorithm has a maximum deviation of 5.39 kPa, a convergence time that lags behind the change time of the pressure setpoint, and an adjustment time of 17 seconds. It can be seen that the simulation method proposed in this invention has better transient control accuracy and robustness.

[0129] This invention constructs a simulation control system with multiple modes, capable of realistically simulating the operation of test equipment, by using a real-time simulation device, a PLC device, a throttle lever simulation device, a host computer, and regulating valves. By integrating engine speed feedforward and active disturbance rejection control, a method is formed that enables rapid pressure response and high-precision intelligent control. This solves the problem of integrating a low-cost, risk-free scientific research verification platform and improves simulation confidence and efficiency.

[0130] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A multi-mode simulation method for a flight environment simulation control system, characterized in that, The flight environment simulation control system includes a real-time simulation device, a PLC device, a hydraulic pump station, a throttle lever simulation device, and a regulating valve. The method includes: Start the hydraulic pump station, regulating valve, PLC device and real-time simulation device, select the full digital simulation experiment or semi-physical simulation experiment, run the object model and control model. The object model includes the hydraulic regulating valve flow model, the air pipeline pressure and temperature model and the engine flow model connected in sequence. The control model includes the feedforward controller, the active disturbance rejection controller and the extended state observer. When a semi-physical simulation experiment is selected, the running equipment includes a PLC device, a hydraulic pump station, a hydraulic regulating valve, and a real-time simulation device. The PLC master control mode or the real-time simulation device master control mode is selected. In the PLC master control mode, the control model runs in the PLC device and the object model runs in the real-time simulation device. In the real-time simulation device master control mode, both the control model and the object model run in the real-time simulation device. When selecting the all-digital simulation experiment, the running equipment includes a PLC device and a real-time simulation device, and both the control model and the object model run within the real-time simulation device. Set the initial conditions for simulation, and simultaneously call the control model and object model to calculate simulation parameters in real time; The feedforward control quantity is calculated in real time based on the engine speed change rate output by the engine flow model by the feedforward controller; the total disturbance of the system is estimated by the extended state observer to obtain the estimated value, and then fed back to the active disturbance rejection controller. The active disturbance rejection controller calculates the initial control quantity in real time. The initial control quantity is superimposed with the feedforward control quantity and the estimated value to obtain the final control quantity. The final control quantity is input to the hydraulic regulating valve flow model to realize the dynamic control of system pressure. The control parameters of the control model are tuned through a fully digital simulation experiment. When the fully digital simulation experiment passes, a semi-physical simulation experiment is carried out to optimize the control parameters tuned in the fully digital simulation experiment, so as to meet the adaptability requirements of the control algorithm in the control model to the physical equipment.

2. The multi-mode simulation method for flight environment simulation control system according to claim 1, characterized in that, When a semi-physical simulation experiment is selected, the PLC device and the real-time simulation device communicate in real time through an Ethernet switch. When a fully digital simulation experiment is selected, the object model and the control model exchange information in real time through a reflective memory switch.

3. The multi-mode simulation method for flight environment simulation control system according to claim 1, characterized in that, The initial simulation conditions include intake pressure, intake temperature, flight altitude, Mach number, engine speed, inlet pressure of the first hydraulic regulating valve, inlet temperature of the first hydraulic regulating valve, inlet pressure of the second hydraulic regulating valve, and inlet temperature of the second hydraulic regulating valve.

4. The multi-mode simulation method for flight environment simulation control system according to claim 3, characterized in that, The hydraulic regulating valve flow model includes a first hydraulic regulating valve flow model and a second hydraulic regulating valve flow model. The real-time calculation simulation parameters include: The inlet pressure, inlet temperature, flow cross-sectional area, flow coefficient, and opening degree of the first hydraulic regulating valve are input into the flow model of the first hydraulic regulating valve to obtain the flow rate of the first hydraulic regulating valve. The inlet pressure, inlet temperature, flow cross-sectional area, flow coefficient, and opening degree of the second hydraulic regulating valve are input into the flow model of the second hydraulic regulating valve to obtain the flow rate of the second hydraulic regulating valve. Pushing the throttle lever simulation device causes a change in engine speed. The engine speed, throttle lever angle, flight altitude, and Mach number are input into the engine flow model to obtain the engine mass flow rate. The flow rates of the first hydraulic regulating valve, the second hydraulic regulating valve, and the engine mass flow rate are input into the air network pressure and temperature model to calculate the intake air temperature and intake air pressure in real time.

5. The multi-mode simulation method for flight environment simulation control system according to claim 4, characterized in that, The expression for the flow rate model of the first hydraulic regulating valve is: ， ， in, Let m1 be the differential of the flow rate m1 of the first hydraulic regulating valve, P1 be the inlet pressure of the first hydraulic regulating valve, T1 be the inlet temperature of the first hydraulic regulating valve, and S1 be the flow cross-sectional area of ​​the first valve. Let be the first flow coefficient, k1 be the first valve opening, R be a specific gas constant, and r1 be the radius of the first hydraulic regulating valve; The expression for the flow rate model of the second hydraulic regulating valve is: ， ， in, Let P2 be the differential of the flow rate m2 of the second hydraulic control valve, P2 be the inlet pressure of the second hydraulic control valve, T2 be the inlet temperature of the second hydraulic control valve, and S2 be the flow cross-sectional area of ​​the second valve. Here, k2 is the second flow coefficient, k2 is the second valve opening, and r2 is the radius of the second hydraulic regulating valve. The expression for the engine flow model is: ， in, Engine mass flow rate m engine The derivative of , n is the engine speed, H is the flight altitude, Ma is the Mach number, m0 is the reference flow rate, and a, b, and c are the first, second, and third coefficients determined by the experimental data calibration method, respectively; The expression for the air pipeline pressure and temperature model is as follows: ， ， Among them, T c P is the temperature of the intake manifold cavity. c V is the intake manifold cavity pressure. c T is the volume of the intake manifold cavity. engine For engine exhaust temperature, C p C is the isobaric specific heat capacity of the gas, C1 is the first average gas flow velocity, C2 is the second average gas flow velocity, and C... engine This is the third average airflow velocity.

6. The multi-mode simulation method for flight environment simulation control system according to claim 1, characterized in that, The formula for calculating the feedforward control quantity is: ， Among them, u f K is the feedforward control variable. f1 For the first feedforward gain, K f2 For the second feedforward gain, K f3 The third feedforward gain is given by sign, where sign is the sign function. The term represents the exponential decay, α is the quadratic gain coefficient, and β is the hysteresis compensation coefficient. For high-frequency attenuation gain, The rotational speed attenuation coefficient is... N is the rate of change of engine speed, n is the engine speed, N1 is the boundary value between the low speed range and the medium speed range, and N2 is the boundary value between the high speed range and the medium speed range.

7. The multi-mode simulation method for flight environment simulation control system according to claim 6, characterized in that, The expression for the extended state observer is: ， Where z1 is the real-time estimate of the actual output intake pressure of the system by the expansion state observer. Z1 is the derivative of z1, and z2 is an estimate of the rate of change of the system output pressure. z2 is the derivative of z2, and z3 is an estimate of the total disturbance of the system. It is the derivative of z3, and β1, β2, and β3 are the first, second, and third gain coefficients of the extended state observer, respectively. Here, b0 is the saturation interval coefficient, b0 is the normalized coefficient of the control gain, and u is the final control quantity. It is the switching threshold between linear and nonlinear operating ranges, e is the tracking error, and sat is the saturation function. It is an estimate of unknown dynamics and external disturbances.

8. The multi-mode simulation method for flight environment simulation control system according to claim 7, characterized in that, The active disturbance rejection controller calculates the initial control quantity in real time. The initial control quantity is then superimposed with the feedforward control quantity and the estimated value to obtain the final control quantity, including: The pressure setpoint of the step signal is converted into a smooth signal with a smooth transition by a tracking differentiator to avoid overshoot caused by the step signal. The active disturbance rejection controller calculates the initial control quantity in real time based on the pressure setpoint for smooth transition, the real-time estimate of the actual output intake pressure of the system by the expansion state observer, and the estimate of the rate of change of the system output pressure. The final control quantity is obtained based on the initial control quantity, the feedforward control quantity, and the estimate of the total system disturbance.

9. The multi-mode simulation method for flight environment simulation control system according to claim 8, characterized in that, The expression for the tracking differentiator is: ， Where R1 is the velocity factor, and v1 is the smoothed tracking signal of the pressure setpoint. Let v1 be the first value of the differential of v1, and v2 be the second value of the differential of v1. The derivative of v2 is given by r(t), where r(t) is the pressure setpoint.

10. The multi-mode simulation method for flight environment simulation control system according to claim 9, characterized in that, The formula for calculating the initial control quantity is: ， Where e is the tracking error, Let k2 be the derivative of the tracking error e, k2 be the control gain, and r be the derivative of the tracking error e. f The coefficient h determines the nonlinear intensity. f is the step size, fhan is the fastest control synthesis function, and u0 is the initial control input; The formula for calculating the final control quantity is: 。

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