Large air exhaust compressor unit header pipe pressure strong anti-interference control method

Abstract

The invention provides a large air exhaust compressor unit header pipe pressure strong anti-interference control method, and belongs to the technical field of aero-engines, and the method comprises the steps: building an air exhaust header pipe pressure controlled object model, and obtaining a state space model; constructing a bipolar discrete tracking differential module, and obtaining a pressure instruction filtering signal and a pressure instruction differential signal of a pressure given value, and a pressure feedback filtering output and a pressure feedback output differential signal of a pressure measurement signal with noise; constructing a discrete nonlinear extended state observation module to obtain a total disturbance real-time observation estimated value; inputting the parameters into a nonlinear fast convergence stability control module to generate an actual control input quantity; updating a control input gain estimation value of the regulating valve in real time based on a parameter self-tuning algorithm; and the steps are repeated, and large air exhaust compressor unit header pipe pressure strong anti-interference control is achieved. The dynamic response speed, the robustness and the anti-interference capability of the control system are improved, and the adjustment time and the dynamic deviation are shortened.

Description

Technical Field

[0001] The present application relates to the field of aero-engine technology, and in particular to a method for controlling the main pipe pressure of a large-scale air extraction compressor unit with strong anti-disturbance. Background Art

[0002] The mainline pressure control system (PM control system) for large-scale extraction compressor units is a core automatic control system for aircraft engine flight environment simulation testing. By controlling the mainline pressure, this system automatically compensates for engine test flow disturbances and ensures the safe operation of large-scale extraction unit cluster tests. The steady-state and transient control accuracy of the PM control system is crucial for improving environmental simulation test capabilities, especially those at flight altitude. Furthermore, the disturbance rejection performance of the PM control system directly determines the operational safety of large-scale extraction unit cluster tests and the scope of test project requirements. Currently, the PM control system still uses a traditional PID control approach based on single-variable feedback. While this approach can meet most routine requirements, the PID's limited disturbance rejection and robustness make it difficult to effectively improve the overall control quality when operating at unit boundary points and experiencing large instantaneous flow rate fluctuations. This limits the compressor unit's performance, especially at its critical boundary, resulting in high energy consumption, low efficiency, and limited testing capabilities.

[0003] To achieve the high-quality control goals of robust and highly disturbance-resistant PM control systems, the core technical challenges faced include two aspects: first, how to accurately and dynamically extract various information from measurement noise, thereby laying a solid foundation for the design of a high-performance state feedback controller; second, how to enhance the robustness of the system and quickly eliminate the effects of large engine flow variations and various uncertain disturbances on the exhaust manifold pressure, so as to maximize the unit's critical operating performance and ensure the safe operation of the exhaust compressor unit. Therefore, the problem ultimately comes down to robust disturbance rejection control. Given the advantages of highly robust and disturbance-resistant control methods, which offer real-time estimation and extraction of disturbance information and timely disturbance rejection, a robust and disturbance-resistant control method and device for exhaust manifold pressure is needed to effectively overcome the current issues of slow transient state test manifold pressure response and poor disturbance rejection. This method and device can rapidly improve the disturbance rejection performance of the PM control system, facilitate the improvement of transient state test capabilities in airborne environment simulation, and effectively utilize the critical operating performance of the exhaust unit. Summary of the Invention

[0004] In view of this, an embodiment of the present application provides a strong anti-interference control method for the main pipe pressure of a large-scale exhaust compressor unit to solve the problems of slow transition state test response and poor anti-interference effect of the current exhaust main pipe pressure control system, and to improve the comprehensive control quality of the current control system in the transition state test.

[0005] The present application provides a method for strong anti-disturbance control of the main pipe pressure of a large-scale extraction compressor unit, the method comprising: Step 1: Establish a controlled object model of the main exhaust pipe pressure and obtain a state space model of the controlled object; Step 2: Build the first bipolar discrete tracking differential module to obtain the pressure set value Pressure command filter signal and pressure command differential signal ; Build the second bipolar discrete tracking differential module to obtain the noisy pressure measurement signal Pressure feedback filter output And pressure feedback output differential signal ; Step 3: Based on the controlled object model, construct a discrete nonlinear extended state observation module to obtain the total disturbance acting on the controlled object. Real-time observation estimates of ; Step 4: Filter the pressure command signal , pressure command differential signal , pressure feedback filter output , pressure feedback output differential signal And the total disturbance acting on the controlled object Input to the nonlinear fast convergence stabilization control module to generate the actual control input ; Step 5: Based on the parameter self-tuning algorithm, the control input gain estimation value b0 of the regulating valve in the discrete nonlinear extended state observation module and the nonlinear fast convergence stabilization control module is updated in real time; Step 6: Encapsulate each module and the parameter self-tuning algorithm into a functional module and download it to the actual PLC hardware controller, and complete the signal input and output connection according to the parameter transfer relationship determined in steps 2 to 5; Step 7: Repeat steps 2 to 6 to achieve strong anti-disturbance control of the main pipe pressure of the large-scale exhaust compressor unit.

[0006] According to a specific implementation of the embodiment of the present application, establishing a controlled object model of the exhaust main pressure to obtain a state space model of the controlled object includes: Establish the controlled object model of exhaust main pressure; Since the actual control input u F There is a first-order inertia link to the regulating valve angle F, and the first relationship is established; Based on the controlled plant model and the first relation, a state space model of the second-order controlled plant is obtained.

[0007] According to a specific implementation of the embodiment of the present application, the expression of the controlled object model is: , Where T is the temperature in the cavity, p is the pressure in the cavity, V is the volume of the cavity, and W in Air mass flow of intake regulating valve, W eng is the air mass flow rate discharged from the simulation cabin, W out is the total air mass flow rate at the inlet of the exhaust unit, h in is the enthalpy of the air entering the regulating valve, h e is the exhaust enthalpy of the simulation chamber, h o is the inlet enthalpy of the exhaust unit, c p is the specific heat capacity of gas at constant pressure, is the heat exchanged between the cavity and the metal tube wall per unit time, R is the gas constant, h out is the enthalpy of the gas in the cavity; The first relational expression is: , Among them, F is the regulating valve angle, K is the differential of the regulating valve angle, mF K is the ratio of the air flow of the regulating valve to the angle of the regulating valve. θ1 is the proportional coefficient, T θ1 is the inertia time constant of the regulating valve movement; The expression of the state space model is: , in, and are the differentials of the controlled pressure, is the second-order differential of the controlled pressure, is the control input gain of the regulating valve, f p is the unknown total disturbance acting on the controlled system, It is determined by the following formula: .

[0008] According to a specific implementation of the embodiment of the present application, the first bipolar discrete tracking differential module and the second bipolar discrete tracking differential module adopt the same calculation strategy, and the implementation steps include: Construct the discrete implementation of the first-level tracking differential module as follows: , Among them, k is the time series step of the discretized system, r is the speed factor, h is the sampling step, v(k) is the signal input end at the current moment, v -1 is the value of v(k) at the previous moment, r 11 (k) is the initial filtered signal output by the first-stage tracking differential module, r 11-1 Both are r11 (k) the value at the previous moment, r 12 (k) is the differential signal output by the first-stage tracking differential module, r 12-1 、x 12 Both are r 12 (k) the value at the previous moment, c 01 is the smoothness coefficient of the first-level tracking differential module, x 11 is the output tracking error, out(k) is the final filtered output signal of the first-stage tracking differential module after correction, and danfast is the fastest control synthesis function; Construct the discrete implementation of the second-level tracking differential module as follows: , Among them, r 13 (k) is the initial filtered signal output by the second-stage tracking differential module, r 13-1 For r 13 (k) the value at the previous moment, r 14 (k) is the initial differential signal output by the second-stage tracking differential module, r 14-1 For r 14 (k) is the value of the previous moment, out2(k) is the final filtered output signal of the second-stage tracking differential module after compensation, dout2(k) is the final differential output signal of the second-stage tracking differential module after compensation, c 02 is the smoothness coefficient of the second-level tracking differential module; Set the pressure to the given value The actual value is transmitted to the v(k) signal input terminal of the first-stage tracking differential module in the discrete implementation form. The output signals out2(k) and dout2(k) of the second-stage tracking differential module in the discrete implementation form correspond to the pressure command filter signals and pressure command differential signal ; The noisy pressure measurement signal The actual value is passed to the v(k) signal input terminal of the first-stage tracking differential module discrete implementation form, and the output signals out2(k) and dout2(k) of the second-stage tracking differential module discrete implementation form correspond to the pressure feedback filter output And pressure feedback output differential signal .

[0009] According to a specific implementation of the embodiment of the present application, the expression of the fastest control comprehensive function danfast is:

[0010] (6) Among them, x1 and x2 are the states of the second-order series system, curve is the state sliding surface equation, is a symbolic function, t1 and t2 are time calculation parameters related to the state sliding surface, and d and d0 are intermediate variables.

[0011] According to a specific implementation of the embodiment of the present application, the expression of the discrete nonlinear extended state observation module is: , Where b0 is the estimated value of the control input gain of the regulating valve, w 01 、w 02 、w 03 is the observation module gain, lamda is the noise suppression coefficient, z1(k) is the state estimate of the second-order system output at the current moment, z 1-1 is the value of z1(k) at the previous moment, z2(k) is the estimate of the output differential of the second-order system at the current moment, and z 2-1 is the value of z2(k) at the previous moment, z3(k) is the estimate of the total disturbance of the second-order system at the current moment, and z 2-1 is the value of z3(k) at the previous moment, p and q are adjustable parameters, e(k) is the estimation error, u(k) is the control input at the current moment, y(k) is the feedback signal input at the current moment, and y 1-1 is the value of y(k) at the previous moment, is a nonlinear function.

[0012] According to a specific implementation of the embodiment of the present application, the expression of the nonlinear function is: , Among them, sign is the switch sign function.

[0013] According to a specific implementation of the embodiment of the present application, the total disturbance acting on the controlled object is obtained. Real-time observation estimates of , including: The actual control input and pressure feedback filter output They are respectively transmitted to the u(k) and y(k) signal input terminals of the discrete nonlinear extended state observation module, and the output signal z3(k) corresponds to the total disturbance acting on the controlled object. Real-time observation estimate of .

[0014] According to a specific implementation of the embodiment of the present application, the calculation strategy of the nonlinear fast convergence stabilization control module includes: Filter signal based on pressure command and pressure feedback filter output , obtain the pressure tracking error , based on the pressure command differential signal And pressure feedback output differential signal , obtain the pressure tracking differential error and , and The calculation formula is: , Based on pressure tracking error and pressure tracking differential error and , define the nonlinear sliding surface s, the expression of s is: , Where, is a nonlinear parameter, , is the preset time for the error to converge to 0 after the state reaches the sliding surface, is a preset time function; Based on the total disturbance acting on the controlled object and the nonlinear sliding surface s, the control module output u0 is obtained, and the expression of u0 is: , Where, is the second-order differential signal of the pressure command, Preset time for the state to reach the sliding surface, for The differential signal of Based on the control module output u0 and the control input gain estimation value b0 of the regulating valve, the actual control input acting on the controlled object is obtained. , The expression is: .

[0015] According to a specific implementation of the embodiment of the present application, the calculation strategy of the parameter self-tuning algorithm includes: Establish a control valve mass flow calculation model, the expression is: , in, is the regulating valve flow coefficient, is the actual flow area of ​​the plunger regulating valve, To adjust the density before the valve, It is the difference between the pressure before and after the regulating valve; Based on the control valve mass flow calculation model and the control valve angle, a preliminary update is performed to obtain the preliminary calculation value of the control valve control input gain. , the expression is:

[0016] in, 、 are the adjustment coefficients, is the differential of the mass flow rate of the regulating valve, To regulate the ratio of the pressure after the valve to the pressure before the valve; Based on the preliminary calculated value of the control input gain of the regulating valve, an adaptive update strategy for the estimated value b0 of the control input gain of the regulating valve is formed. The estimated value b0 of the control input gain of the regulating valve in the discrete nonlinear extended state observation module and the nonlinear fast convergence stabilization control module is adjusted in real time. The adaptive update strategy expression is: , in, for Parameter lower limit, for Upper limit of the parameter.

[0017] Beneficial effects: The strong anti-disturbance control method for the main pipe pressure of a large-scale exhaust compressor unit in the embodiment of the present application has the following beneficial effects: (1) Compared with the original PID-based exhaust manifold pressure control mode, the present invention significantly improves the dynamic response speed, robustness and anti-interference ability of the control system, and greatly reduces the system's adjustment time and dynamic deviation; (2) The overall control method proposed in this invention, based on a bipolar discrete tracking differential module, a discrete nonlinear extended state observation module, and a nonlinear fast convergence stability control law, realizes a robust control method and algorithm that integrates high-precision signal extraction under noise influence, dynamic estimation of interference information with small lag, and fast convergence control. The actual system verification results show that this invention has helped improve the ability of air environment simulation transition state test and effectively realize the limit boundary working performance of the exhaust unit; (3) The high-performance, strong anti-interference control quality achieved by the present invention broadens the safe operating range of the exhaust unit in actual testing, making it possible to efficiently and rationally optimize the configuration of experimental power resources. Furthermore, the present invention can serve as a technical foundation for application scenarios such as fully automated grid connection of units and coordinated strong anti-interference of multiple actuators at ultra-large flow rates, and has broad prospects for promotion and application. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0019] Figure 1A schematic diagram of the principle of a main exhaust pipe pressure control system according to an embodiment of the present invention; Figure 2 2. Schematic diagram of a method for controlling the main pipe pressure of a large-scale air extraction compressor unit with strong anti-disturbance according to one embodiment of the present invention; Figure 3 This is a simulation diagram of the anti-disturbance control effect under the working condition of 30kPa exhaust main pressure according to one embodiment of the present invention; Figure 4 This is a simulation diagram of the anti-disturbance control effect under the working condition of 12 kPa exhaust main pressure according to one embodiment of the present invention; Figure 5 This is a test result diagram of an actual system of PID anti-disturbance control under the working condition of 30kPa exhaust main pressure according to one embodiment of the present invention; Figure 6 This is a test result diagram of an actual system for strong anti-disturbance control under a working condition of 30 kPa exhaust main pressure according to an embodiment of the present invention; Figure 7 This is a test result diagram of an actual system with strong anti-disturbance control under the exhaust main pressure of 12.5 kPa according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0021] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, in the absence of conflict, the features in the following embodiments and embodiments can be combined with each other. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without making creative work are within the scope of protection of this application.

[0022] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on this application, it should be understood by those skilled in the art that an 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 described herein can be used to implement an apparatus and / or practice a method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement this apparatus and / or practice this method.

[0023] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application. The illustrations only show components related to the present application and are not drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component can be changed at will, and the component layout type may also be more complicated.

[0024] Additionally, in the following description, specific details are provided to provide a thorough understanding of the examples. However, one skilled in the art will appreciate that the aspects described can be practiced without these specific details.

[0025] The embodiment of the present application provides a method for controlling the main pipe pressure of a large-scale exhaust compressor unit with strong anti-disturbance. Figures 1 to 7 The method comprises the following steps: Step 1: Establish a controlled object model of the main exhaust pipe pressure and obtain a state space model of the controlled object; Step 2: Build the first bipolar discrete tracking differential module (TD1-FastEx) to obtain the pressure setpoint Pressure command filter signal and pressure command differential signal ; Construct the second bipolar discrete tracking differential module (TD2-FastEx) to obtain the noisy pressure measurement signal Pressure feedback filter output And pressure feedback output differential signal ; Step 3: Based on the controlled object model, construct a discrete nonlinear extended state observation module (Fntl-ESO) to obtain the total disturbance acting on the controlled object. Real-time observation estimates of ; Step 4: Filter the pressure command signal , pressure command differential signal , pressure feedback filter output , pressure feedback output differential signal And the total disturbance acting on the controlled object Input to the nonlinear fast convergence stabilization control module (PTDRC) to generate the actual control input ; Step 5: Based on the parameter self-tuning algorithm (b0adapt), the control input gain estimation value b0 of the control valve in the discrete nonlinear extended state observation module (Fntl-ESO) and the nonlinear fast convergence stabilization control module (PTDRC) is updated in real time; Step 6. Encapsulate each module (TD1-FastEx, TD2-FastEx, Fntl-ESO, PTDRC) and the parameter self-tuning algorithm (b0adapt) into a functional module and download it to the actual PLC hardware controller. Complete the signal input and output connections according to the parameter transfer relationship determined in steps 2 to 5. Step 7: Repeat steps 2 to 6 to achieve strong anti-disturbance control of the main pipe pressure of the large-scale exhaust compressor unit.

[0026] During the specific implementation, the actual hardware discrete control step sizes of the TD1-FastEx, TD2-FastEx, Fntl-ESO, PTDRC, and b0adapt modules are set to 10ms, 1ms, 2ms, 10ms, and 10ms, respectively.

[0027] In this embodiment, a bipolar discrete tracking differential module is first designed to perform real-time filtering on the noisy output signal of the pressure sensor module, thereby obtaining effective filtering / differential information; secondly, a discrete nonlinear extended state observation module is designed based on the controlled object model to achieve real-time observation estimation of strong interference; then, a nonlinear fast convergence stable control law and parameter self-tuning algorithm based on state feedback information are designed to achieve rapid convergence of state error; finally, the above algorithm module is transplanted into the real PLC control module and the control cycle deployment is completed, ultimately making the exhaust main pressure control system have robust and strong anti-interference control quality, which helps to improve the ability of air environment simulation transition state test and effectively exert the limit boundary working performance of the exhaust unit.

[0028] In specific implementation, the schematic diagram of the main pipe pressure control system of a large-scale exhaust compressor unit is as follows: Figure 1 As shown in the figure, the controlled objects mainly include DN3600 large-diameter plunger-type regulating valve, exhaust main pipe network, electro-hydraulic servo actuator, etc. The system collects the measured pressure value of the exhaust main pipe in real time, solves the control instruction through the control module, drives the large-diameter regulating valve to adjust the air flow, and realizes high-precision control of the exhaust main pipe pressure, thereby achieving rapid convergence and stabilization of the controlled pressure of the exhaust system. The robust and strong anti-disturbance control method for exhaust main pipe pressure designed by the present invention has the following control structure principle: Figure 2As shown in Figure 1, the control architecture comprises the following core algorithmic modules: two bipolar nonlinear discrete tracking differential modules (TD1-fastEx and TD2-fastEx), a nonlinear fast convergence stabilization control module (PTDRC), a nonlinear extended state observation module (Fntl-ESO), and a parameter self-tuning module (b0adapt). TD1-fastEx generates a desired pressure command and a desired pressure command differential signal. TD2-fastEx generates a smooth pressure feedback output with minimal phase lag and a pressure feedback output differential signal based on a noisy pressure measurement signal. Fntl-ESO rapidly estimates the total disturbance acting on the system. The parameter self-tuning module (b0adapt) automatically adjusts the b0 parameter in Fntl-ESO and PTDRC. These algorithmic modules are independently designed and work together to implement robust, strong disturbance rejection control of the exhaust manifold pressure with high-precision smooth information extraction, fast disturbance estimation, and rapid convergence and stabilization, using the setpoint pressure as a given variable.

[0029] In one embodiment, establishing the controlled object model of the exhaust main pressure to obtain the state space model of the controlled object includes: Establish the controlled object model of the main exhaust pipe pressure, that is, the pipe network cavity pressure model; Since the actual control input u F There is a first-order inertia link to the regulating valve angle F, and the first relationship is established; Based on the controlled plant model and the first relation, a state space model of the second-order controlled plant is obtained.

[0030] Furthermore, the expression of the controlled object model is: (1), Where T is the temperature in the cavity, p is the pressure in the cavity, V is the volume of the cavity, and W in Air mass flow of intake regulating valve, W eng is the air mass flow rate discharged from the simulation cabin, W out is the total air mass flow rate at the inlet of the exhaust unit, h in is the enthalpy of the air entering the regulating valve, h e is the exhaust enthalpy of the simulation chamber, h o is the inlet enthalpy of the exhaust unit, c p is the specific heat capacity of gas at constant pressure, is the heat exchanged between the cavity and the metal tube wall per unit time, R is the gas constant, h out is the enthalpy of the gas in the cavity; The first relational expression is: (2), Among them, F is the regulating valve angle, K is the differential of the regulating valve angle, mF K is the ratio of the air flow of the regulating valve to the angle of the regulating valve. θ1 is the proportional coefficient (value is 1), T θ1 is the inertia time constant of the regulating valve movement; The expression of the state space model is: (3), in, and are the differentials of the controlled pressure, is the second-order differential of the controlled pressure, is the control input gain of the regulating valve, f p is the unknown total disturbance acting on the controlled system, It is determined by the following formula: (4).

[0031] In one embodiment, the first bipolar discrete tracking differential module and the second bipolar discrete tracking differential module adopt the same calculation strategy, and the implementation steps include: Step 21: Construct the discrete implementation of the first-level tracking differential module as follows: (5), Where k is the time series step of the discretized system, k=0,1,..., r is the speed factor, h is the sampling step, v(k) is the signal input end at the current moment, v -1 is the value of v(k) at the previous moment, r 11 (k) is the initial filtered signal output by the first-stage tracking differential module, r 11-1 Both are r 11 (k) the value at the previous moment, r 12 (k) is the differential signal output by the first-stage tracking differential module, r 12-1 、x 12 Both are r 12 (k) the value at the previous moment, c 01 is the smoothness coefficient of the first-level tracking differential module, x 11 is the output tracking error, out(k) is the final filtered output signal of the first-stage tracking differential module after correction, which is fed into the second-stage tracking differential module, and danfast is the fastest control synthesis function; The purpose of the fastest control comprehensive function danfast is to make the states x1 and x2 of the second-order series system reach the origin quickly and stably. Its expression is:

[0032] (6) Among them, x1 and x2 are the states of the second-order series system, curve is the state sliding surface equation, is a symbolic function, t1 and t2 are time calculation parameters related to the state sliding surface, d and d0 are intermediate variables; Step 22: Construct the discrete implementation of the second-level tracking differential module as follows: (7), Among them, r 13 (k) is the initial filtered signal output by the second-stage tracking differential module, r 13-1 For r 13 (k) the value at the previous moment, r 14 (k) is the initial differential signal output by the second-stage tracking differential module, r 14-1 For r 14 (k) is the value of the previous moment, out2(k) is the final filtered output signal of the second-stage tracking differential module after compensation, dout2(k) is the final differential output signal of the second-stage tracking differential module after compensation, c 02 is the smoothness coefficient of the second-level tracking differential module; Step 23: Set the pressure to a given value The actual value is transmitted to the v(k) signal input terminal of the first-stage tracking differential module in the discrete implementation form. The output signals out2(k) and dout2(k) of the second-stage tracking differential module in the discrete implementation form correspond to the pressure command filter signals and pressure command differential signal ; Step 24: The noise pressure measurement signal The actual value is passed to the v(k) signal input terminal of the first-stage tracking differential module discrete implementation form, and the output signals out2(k) and dout2(k) of the second-stage tracking differential module discrete implementation form correspond to the pressure feedback filter output And pressure feedback output differential signal .

[0033] In one embodiment, the expression of the discrete nonlinear extended state observation module is: (8), Where b0 is the estimated value of the control input gain of the regulating valve, w 01 、w 02 、w 03 is the observation module gain, lamda is the noise suppression coefficient, z1(k) is the state estimate of the second-order system output at the current moment, z 1-1is the value of z1(k) at the previous moment, z2(k) is the estimate of the output differential of the second-order system at the current moment, and z 2-1 is the value of z2(k) at the previous moment, z3(k) is the estimate of the total disturbance of the second-order system at the current moment, and z 2-1 is the value of z3(k) at the previous moment, p and q are adjustable parameters, e(k) is the estimation error, u(k) is the control input at the current moment, y(k) is the feedback signal input at the current moment, and y 1-1 is the value of y(k) at the previous moment, is a nonlinear function.

[0034] Furthermore, the expression of the nonlinear function is: (9).

[0035] In formula (9), the nonlinear function It has the characteristics of full non-smoothness. By adjusting the parameters p and q, the nonlinear function can show good continuity and smoothness, so that the error e(k) converges quickly to the vicinity of the 0 domain, and the system estimation achieves ideal results. sign is the switch compliance function.

[0036] Furthermore, the total disturbance acting on the controlled object is obtained Real-time observation estimates of , including: The actual control input and pressure feedback filter output They are respectively transmitted to the u(k) and y(k) signal input terminals of the discrete nonlinear extended state observation module, and the output signal z3(k) corresponds to the total disturbance acting on the controlled object. Real-time observation estimate of .

[0037] In one embodiment, the calculation strategy of the nonlinear fast convergence stabilization control module includes: Filter signal based on pressure command and pressure feedback filter output , obtain the pressure tracking error , based on the pressure command differential signal And pressure feedback output differential signal , obtain the pressure tracking differential error and , and The calculation formula is: (10), Based on pressure tracking error and pressure tracking differential error and , define the nonlinear sliding surface s, the expression of s is: (11), Where, is a nonlinear parameter, , is the preset time for the error to converge to 0 after the state reaches the sliding surface, is a preset time function; Based on the total disturbance acting on the controlled object and the nonlinear sliding surface s, the control module output u0 is obtained, and the expression of u0 is: (12), Where, is the second-order differential signal of the pressure command, Preset time for the state to reach the sliding surface, for The differential signal is obtained by the tracking differential module TD2-FastEx; Based on the control module output u0 and the control input gain estimation value b0 of the regulating valve, the actual control input acting on the controlled object is obtained. , The expression is: (13).

[0038] In one embodiment, the calculation strategy of the parameter self-tuning algorithm includes: Establish a mass flow calculation model for the control valve. The control valve can be a large-diameter plunger control valve. The model expression is: (14), in, is the regulating valve flow coefficient, which is determined from Table 1. is the actual flow area of ​​the plunger regulating valve, To adjust the density before the valve, It is the difference between the pressure before and after the regulating valve; Based on the control valve mass flow calculation model and the control valve angle, a preliminary update is performed to obtain the preliminary calculation value of the control valve control input gain. , the expression is: (15), in, 、 are the adjustment coefficients, is the differential of the mass flow rate of the regulating valve, To regulate the ratio of the pressure after the valve to the pressure before the valve; Based on the preliminary calculated value of the control input gain of the regulating valve, an adaptive update strategy for the estimated value b0 of the control input gain of the regulating valve is formed. The estimated value b0 of the control input gain of the regulating valve in the discrete nonlinear extended state observation module and the nonlinear fast convergence stabilization control module is adjusted in real time. The adaptive update strategy expression is: (16), in, for Parameter lower limit, for Upper limit of the parameter.

[0039] Table 1 Flow coefficient table of large diameter plunger type regulating valve

[0040] Table 1 To adjust the valve angle, To adjust the ratio of the pressure after the valve to the pressure before the valve, It is the ratio of the actual flow area of ​​the regulating valve to the fully open area.

[0041] The content of the embodiments of this application has the following advantages: (1) Compared with the original PID-based exhaust manifold pressure control mode, the present invention significantly improves the dynamic response speed, robustness and anti-interference ability of the control system, and greatly reduces the system's adjustment time and dynamic deviation; (2) The overall control method proposed in this invention, based on a bipolar discrete tracking differential module, a discrete nonlinear extended state observation module, and a nonlinear fast convergence stability control law, realizes a robust control method and algorithm that integrates high-precision signal extraction under noise influence, dynamic estimation of interference information with small lag, and fast convergence control. The actual system verification results show that this invention has helped improve the ability of air environment simulation transition state test and effectively realize the limit boundary working performance of the exhaust unit; (3) The high-performance, strong anti-interference control quality achieved by the present invention broadens the safe operating range of the exhaust unit in actual testing, making it possible to efficiently and rationally optimize the configuration of experimental power resources. Furthermore, the present invention can serve as a technical foundation for application scenarios such as fully automated grid connection of units and coordinated strong anti-interference of multiple actuators at ultra-large flow rates, and has broad prospects for promotion and application.

[0042] The following simulation test is conducted on the strong anti-disturbance control method for the main pipe pressure of the large-scale exhaust compressor unit of the present application, which specifically includes the following contents: (1) Implementation effect in simulation environment Two typical working conditions of the exhaust system at 30kPa and 12kPa were selected on the simulation platform for simulation verification, and the control methods were tested using conventional PID control method and strong anti-disturbance control method respectively.

[0043] a) Anti-disturbance simulation verification under the working condition of 30kPa exhaust main pressure Simulation process: simulate the flow rate change of the engine transient test. At 10s, the engine flow rate increases from 30kg / s to 100kg / s within 5s. At 30s, the engine flow rate decreases from 100kg / s to 30kg / s within 5s. Figure 3 shown.

[0044] from Figure 3 It can be seen that when the main pipe pressure is subject to instantaneous, large flow disturbances (70 kg / s change within 5 seconds), the conventional PID control method is used, the maximum pressure fluctuation amplitude is 2.7 kPa, and the adjustment time is 20 seconds; using the strong anti-disturbance control method, the maximum pressure fluctuation amplitude is 0.5 kPa, and the adjustment time is 7 seconds. The anti-disturbance control effect is better than the conventional PID control method, and the dynamic response is fast.

[0045] b) Anti-disturbance simulation verification of the working condition with a pumping pressure of 12 kPa Simulation process: simulate the flow rate change of the engine transient test. At 10s, the engine flow rate increases from 10kg / s to 40kg / s within 3s. At 30s, the engine flow rate decreases from 40kg / s to 10kg / s within 3s. Figure 4 shown.

[0046] from Figure 4 It can be seen that when the main pipe pressure is subject to instantaneous, large flow disturbance (30kg / s change within 3s), the conventional PID control method is adopted, the maximum pressure fluctuation amplitude is 1.3kPa, and the adjustment time is 18s; using the active anti-disturbance control method, the maximum pressure fluctuation amplitude is 0.3kPa, and the adjustment time is 7s, the anti-disturbance effect is better, the transition time is short, and the pressure overshoot is small.

[0047] Through the anti-interference tests of the above two working conditions, different types and amplitudes, it is fully demonstrated that the strong anti-interference control method has significantly improved anti-interference ability than the conventional control method, has a shorter adjustment time, is suitable for the pressure control of the exhaust system, and can effectively solve the problems of slow pressure response of the transition state test main pipe and poor anti-interference effect.

[0048] 2) Actual system test verification effect The air extraction system was adaptively modified and the real PLC controller algorithm was transplanted. Two typical operating conditions of the air extraction system, 30kPa and 12.5kPa, were established on the air extraction system to conduct actual system testing and verification of the strong anti-disturbance control method, and large disturbance testing was performed.

[0049] a) Anti-interference test verification under typical working conditions with a suction pressure of 30 kPa When the exhaust main pressure is set at 30kPa, the original PID control method is used for anti-disturbance control, and the control effect is shown in Figure 5.

[0050] from Figure 5 It can be seen that when the flow change interference test was carried out under the 30kPa working condition, the flow interference amount changed by 40kg / s within 2 seconds, the exhaust main pressure fluctuated by 7kPa, the pressure adjustment time was long, the overshoot was large, and the dynamic response was slow.

[0051] With the exhaust manifold pressure set at 30 kPa, a strong control method was used for anti-disturbance control. By adjusting the opening of the 992 plunger valve to simulate a rapid transient flow rate change, the disturbance flow rate increased from 0 kg / s to 75 kg / s within 2 seconds. The control effect is shown in Figure 6.

[0052] from Figure 6 It can be seen that when the exhaust main pressure is subject to instantaneous, large flow disturbances (maximum change of 75kg / s within 2s), the strong anti-disturbance control method is adopted, the maximum fluctuation amplitude of the exhaust pressure is no more than 1.5kPa, and the pressure adjustment time is basically synchronized with the disturbance time, with fast dynamic response and high adjustment accuracy. The control effect is far superior to the original PID control method.

[0053] b) Anti-interference test verification under typical working conditions with a pumping pressure of 12.5 kPa Since the exhaust main pressure is 12.5kPa, which is close to the working limit of the equipment, the performance of the control system is particularly important for the safety of the equipment.

[0054] With the exhaust manifold pressure set at 12.5 kPa, a strong control method was used for anti-disturbance control. By adjusting the opening of the 992 plunger valve, a rapid change in transient flow rate was simulated. The disturbance flow rate increased from 0 kg / s to 23 kg / s within 1 second. The control effect is shown in Figure 7.

[0055] Figure 7 It can be seen that when the main pipe pressure is subject to instantaneous, large flow disturbances (maximum change of 23kg within 1s), a strong anti-disturbance control method is adopted, the maximum pressure fluctuation amplitude is ≯1.2kPa, the adjustment time is fast and basically synchronized with the step disturbance time, the anti-disturbance effect is good, the transition time is short, and the pressure overshoot is small.

[0056] The test results demonstrate that the strong anti-disturbance control method offers significantly improved anti-disturbance capabilities compared to the existing PID control method, shortens the adjustment time, and is suitable for high-quality pressure control in the exhaust system. It effectively addresses challenges such as slow main pipe pressure response and poor anti-disturbance performance during transient state tests. This significantly enhances the capabilities of transient state tests for simulated aerial environments and effectively maximizes the performance of exhaust units at their extreme limits.

[0057] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for strong anti-disturbance control of the main pipe pressure of a large-scale exhaust compressor unit, characterized in that: The method comprises: Step 1: Establish a controlled object model of the main exhaust pipe pressure and obtain a state space model of the controlled object; Step 2: Build the first bipolar discrete tracking differential module to obtain the pressure set value Pressure command filter signal and pressure command differential signal ; Build the second bipolar discrete tracking differential module to obtain the noisy pressure measurement signal Pressure feedback filter output And pressure feedback output differential signal ; Step 3: Based on the controlled object model, construct a discrete nonlinear extended state observation module to obtain the total disturbance acting on the controlled object. Real-time observation estimates of ; Step 4: Filter the pressure command signal , pressure command differential signal , pressure feedback filter output , pressure feedback output differential signal And the total disturbance acting on the controlled object Input to the nonlinear fast convergence stabilization control module to generate the actual control input ; Step 5: Based on the parameter self-tuning algorithm, the control input gain estimation value b0 of the regulating valve in the discrete nonlinear extended state observation module and the nonlinear fast convergence stabilization control module is updated in real time; Step 6: Encapsulate each module and the parameter self-tuning algorithm into a functional module and download it to the actual PLC hardware controller, and complete the signal input and output connection according to the parameter transfer relationship determined in steps 2 to 5; Step 7: Repeat steps 2 to 6 to achieve strong anti-disturbance control of the main pipe pressure of the large-scale exhaust compressor unit.

2. The method for controlling the main pipe pressure of a large-scale air extraction compressor unit with strong anti-disturbance according to claim 1 is characterized in that: The method of establishing the controlled object model of the main exhaust pipe pressure and obtaining the state space model of the controlled object includes: Establish the controlled object model of exhaust main pressure; Since the actual control input u F There is a first-order inertia link to the regulating valve angle F, and the first relationship is established; Based on the controlled plant model and the first relation, a state space model of the second-order controlled plant is obtained.

3. The method for controlling the main pipe pressure of a large-scale air extraction compressor unit with strong anti-disturbance according to claim 2, characterized in that: The expression of the controlled object model is: ， Where T is the temperature in the cavity, p is the pressure in the cavity, V is the volume of the cavity, and W in Air mass flow of intake regulating valve, W eng is the air mass flow rate discharged from the simulation cabin, W out is the total air mass flow rate at the inlet of the exhaust unit, h in is the enthalpy of the air entering the regulating valve, h e is the exhaust enthalpy of the simulation chamber, h o is the inlet enthalpy of the exhaust unit, c p is the specific heat capacity of gas at constant pressure, is the heat exchanged between the cavity and the metal tube wall per unit time, R is the gas constant, h out is the enthalpy of the gas in the cavity; The first relational expression is: ， Among them, F is the regulating valve angle, K is the differential of the regulating valve angle, mF K is the ratio of the air flow of the regulating valve to the angle of the regulating valve. θ1 is the proportional coefficient, T θ1 is the inertia time constant of the regulating valve movement; The expression of the state space model is: ， in, and are the differentials of the controlled pressure, is the second-order differential of the controlled pressure, is the control input gain of the regulating valve, f p is the unknown total disturbance acting on the controlled system, It is determined by the following formula: 。 4. The method for controlling the main pipe pressure of a large-scale air extraction compressor unit with strong anti-disturbance according to claim 1, characterized in that: The first bipolar discrete tracking differential module and the second bipolar discrete tracking differential module adopt the same calculation strategy, and the implementation steps include: Construct the discrete implementation of the first-level tracking differential module as follows: ， Among them, k is the time series step of the discretized system, r is the speed factor, h is the sampling step, v(k) is the signal input end at the current moment, v -1 is the value of v(k) at the previous moment, r 11 (k) is the initial filtered signal output by the first-stage tracking differential module, r 11-1 Both are r 11 (k) the value at the previous moment, r 12 (k) is the differential signal output by the first-stage tracking differential module, r 12-1 、x 12 Both are r 12 (k) the value at the previous moment, c 01 is the smoothness coefficient of the first-level tracking differential module, x 11 is the output tracking error, out(k) is the final filtered output signal of the first-stage tracking differential module after correction, and danfast is the fastest control synthesis function; Construct the discrete implementation of the second-level tracking differential module as follows: ， Among them, r 13 (k) is the initial filtered signal output by the second-stage tracking differential module, r 13-1 For r 13 (k) the value at the previous moment, r 14 (k) is the initial differential signal output by the second-stage tracking differential module, r 14-1 For r 14 (k) is the value of the previous moment, out2(k) is the final filtered output signal of the second-stage tracking differential module after compensation, dout2(k) is the final differential output signal of the second-stage tracking differential module after compensation, c 02 is the smoothness coefficient of the second-level tracking differential module; Set the pressure to the given value The actual value is transmitted to the v(k) signal input terminal of the first-stage tracking differential module in the discrete implementation form. The output signals out2(k) and dout2(k) of the second-stage tracking differential module in the discrete implementation form correspond to the pressure command filter signals and pressure command differential signal ; The noisy pressure measurement signal The actual value is passed to the v(k) signal input terminal of the first-stage tracking differential module discrete implementation form, and the output signals out2(k) and dout2(k) of the second-stage tracking differential module discrete implementation form correspond to the pressure feedback filter output And pressure feedback output differential signal .

5. The method for controlling the main pipe pressure of a large-scale air extraction compressor unit with strong anti-disturbance according to claim 4 is characterized in that: The expression of the fastest control comprehensive function danfast is: （6） Among them, x1 and x2 are the states of the second-order series system, curve is the state sliding surface equation, is a symbolic function, t1 and t2 are time calculation parameters related to the state sliding surface, and d and d0 are intermediate variables.

6. The method for controlling the main pipe pressure of a large-scale air extraction compressor unit with strong anti-disturbance according to claim 4, characterized in that: The expression of the discrete nonlinear extended state observation module is: ， Where b0 is the estimated value of the control input gain of the regulating valve, w 01 、w 02 、w 03 is the observation module gain, lamda is the noise suppression coefficient, z1(k) is the state estimate of the second-order system output at the current moment, z 1-1 is the value of z1(k) at the previous moment, z2(k) is the estimate of the output differential of the second-order system at the current moment, and z 2-1 is the value of z2(k) at the previous moment, z3(k) is the estimate of the total disturbance of the second-order system at the current moment, and z 2-1 is the value of z3(k) at the previous moment, p and q are adjustable parameters, e(k) is the estimation error, u(k) is the control input at the current moment, y(k) is the feedback signal input at the current moment, and y 1-1 is the value of y(k) at the previous moment, is a nonlinear function.

7. The method for controlling the main pipe pressure of a large-scale air extraction compressor unit with strong anti-disturbance according to claim 6, characterized in that: The expression of the nonlinear function is: ， Among them, sign is the switch sign function.

8. The method for controlling the main pipe pressure of a large-scale air extraction compressor unit with strong anti-disturbance according to claim 6, characterized in that: The total disturbance acting on the controlled object is obtained Real-time observation estimates of , including: The actual control input and pressure feedback filter output They are respectively transmitted to the u(k) and y(k) signal input terminals of the discrete nonlinear extended state observation module, and the output signal z3(k) corresponds to the total disturbance acting on the controlled object. Real-time observation estimate of .

9. The method for controlling the main pipe pressure of a large-scale air extraction compressor unit with strong anti-disturbance according to claim 1, characterized in that: The calculation strategy of the nonlinear fast convergence stabilization control module includes: Filter signal based on pressure command and pressure feedback filter output , obtain the pressure tracking error , based on the pressure command differential signal And pressure feedback output differential signal , obtain the pressure tracking differential error and , and The calculation formula is: ， Based on pressure tracking error and pressure tracking differential error and , define the nonlinear sliding surface s, the expression of s is: ， Where, is a nonlinear parameter, , is the preset time for the error to converge to 0 after the state reaches the sliding surface, is a preset time function; Based on the total disturbance acting on the controlled object and the nonlinear sliding surface s, the control module output u0 is obtained, and the expression of u0 is: ， Where, is the second-order differential signal of the pressure command, Preset time for the state to reach the sliding surface, for The differential signal of Based on the control module output u0 and the control input gain estimation value b0 of the regulating valve, the actual control input acting on the controlled object is obtained. , The expression is: 。 10. The method for controlling the main pipe pressure of a large-scale air extraction compressor unit with strong anti-disturbance according to claim 3, characterized in that: The calculation strategy of the parameter self-tuning algorithm includes: Establish a control valve mass flow calculation model, the expression is: ， in, is the regulating valve flow coefficient, is the actual flow area of ​​the plunger regulating valve, To adjust the density before the valve, It is the difference between the pressure before and after the regulating valve; Based on the control valve mass flow calculation model and the control valve angle, a preliminary update is performed to obtain the preliminary calculation value of the control valve control input gain. , the expression is: in, 、 are the adjustment coefficients, is the differential of the mass flow rate of the regulating valve, To regulate the ratio of the pressure after the valve to the pressure before the valve; Based on the preliminary calculated value of the control input gain of the regulating valve, an adaptive update strategy for the estimated value b0 of the control input gain of the regulating valve is formed. The estimated value b0 of the control input gain of the regulating valve in the discrete nonlinear extended state observation module and the nonlinear fast convergence stabilization control module is adjusted in real time. The adaptive update strategy expression is: ， in, for Parameter lower limit, for Upper limit of the parameter.

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