An adaptive collaborative control method for large-diameter plunger valves in large-scale air extraction test equipment

Abstract

The present application provides an adaptive collaborative control method for large-diameter plunger valves in a large-scale air extraction test device, which belongs to the technical field of aviation power plants. The method includes obtaining a state space model of a controlled object, giving a pressure target value to a tracking differentiator, generating a pressure command filter signal and a pressure command differential signal, inputting a measurement signal of a noisy pressure sensor into the tracking differentiator, and generating a filtered output signal; based on first and second nonlinear sliding mode fixed-time expanded state observers, outputting pressure state estimation values, pressure differential state estimation values, and total disturbance estimation values ​​of corresponding first and second regulating valve control loops respectively; utilizing first and second nonlinear fast convergence stabilization controllers to generate corresponding first and second regulating valve control inputs; adopting an adaptive adjustment update strategy to obtain the control input gains of the first and second regulating valves in real time, realize parameter adjustment, and further realize adaptive collaborative control, thereby improving anti-interference capability, versatility, and robustness.

Description

Technical Field

[0001] The present application relates to the technical field of aviation power plants, and in particular to an adaptive collaborative control method for a large-diameter plunger valve in a large-scale air extraction test device. Background Art

[0002] Large-scale exhaust clusters are essential, complex test equipment for airborne environmental simulation testing of aviation power plants and are the driving force behind flight altitude simulation. Currently, the scale of large-scale continuous exhaust system construction has rapidly expanded to several times the capacity of the original system. Accordingly, exhaust manifold pressure control systems must not only fully cover the requirements of testing from low to ultra-high flow rates, but also meet the high-quality regulation requirements under a wide range of engine flow variations and the strong impact of instantaneous large flow rate changes. However, in actual system control, the transient operating characteristics of the engine, the operating characteristics of large equipment, and the strong coupling characteristics of the rapid motion of large-diameter control valves all vary significantly with changing test conditions. This places particularly stringent requirements and severe challenges on the robust and anti-interference control capabilities of the main manifold pressure of large exhaust clusters. How to ensure the safe and coordinated operation of the expanded exhaust cluster under drastic changes in engine flow rates, and how to ensure the agile and efficient operation of the large-capacity exhaust cluster while achieving rapid convergence and stabilization of the controlled pressure ratio of the exhaust units are key issues that must be addressed by exhaust manifold pressure control systems in the new era.

[0003] At present, there is a relative lack of research on the main pipe pressure control technology of large-scale gas extraction test equipment. In the implementation process, the main methods used are still the single-variable PID feedback + manual experience control method or the multi-control valve control method based on linear anti-disturbance. Since the above methods cannot solve the rapid suppression of various strong uncertain interferences and the strong coupling problem of the actuator during rapid movement, it is difficult to meet the high-quality regulation requirements under a wide flow change range and the impact of instantaneous large flow change rate. Summary of the Invention

[0004] In view of this, an embodiment of the present application provides an adaptive collaborative control method for large-diameter plunger valves in a large-scale air extraction test device, which solves the problems of transient pressure regulation and voltage stabilization control of large-scale air extraction test systems in transitional test scenarios for large-flow engines, and meets the high-quality regulation requirements under a wide flow change range and instantaneous large flow change rate impact.

[0005] The present application provides an adaptive coordinated control method for a large-diameter plunger valve of a large-scale air extraction test device, the method comprising:

[0006] Step 1: Establish a mathematical model of a dual-input single-output system of the main exhaust pipe pressure controlled object to obtain a state space model of the controlled object;

[0007] Step 2: Based on the state space model, the pressure target setting value p is given set Input to the first fast discrete tracking differentiator to calculate and generate the pressure command filter signal x 1d and pressure command differential signal x 2d , the noise pressure sensor measurement signal p act Input to the second fast discrete tracking differentiator to calculate and generate the filtered output signal y;

[0008] Step 3: Control the first regulating valve input u F1 The filtered output signal y is input to the first nonlinear sliding mode fixed time extended state observer, and the pressure state estimation value z of the first regulating valve control loop is output. 11 , the pressure differential state estimation value z of the first regulating valve control loop 12 , the total disturbance estimate z of the first regulating valve control loop 13 ; The second regulating valve control input u F2 The filtered output signal y is input to the second nonlinear sliding mode fixed time extended state observer, and the pressure state estimation value z of the second regulating valve control loop is output. 21 , the pressure differential state estimation value z of the second regulating valve control loop 22 , the total disturbance estimate z of the second regulating valve control loop 23 ;

[0009] Step 4: Filter the pressure command signal x 1d , pressure command differential signal x 2d , filtered output signal y, pressure state estimation value z of the first regulating valve control loop 11 , the pressure differential state estimation value z of the first regulating valve control loop 12 and the total disturbance estimate z of the first regulating valve control loop 13 Input to the input end of the first nonlinear fast convergence stabilization controller to generate the first regulating valve control input u F1 ;Filter the pressure command signal x 1d , pressure command differential signal x 2d , filtered output signal y, pressure state estimation value z of the second regulating valve control loop 21 , the pressure differential state estimation value z of the second regulating valve control loop 22 and the total disturbance estimate z of the second regulating valve control loop 23 Input to the input end of the second nonlinear fast convergence stabilization controller to generate the second regulating valve control input u F2 ;

[0010] Step 5: Use the adaptive adjustment update strategy based on the control valve angle / flow model to calculate the first control valve control input gain b in real time. F10 And the second regulating valve control input gain b F20 , realize the b in the first nonlinear sliding mode fixed time extended state observer and the first nonlinear fast convergence stabilization controller F10 Parameter adjustment and implementation of the second nonlinear sliding mode fixed time extended state observer and the second nonlinear fast convergence stabilization controller b F20 Parameter adjustment;

[0011] Step 6: Encapsulate the first fast discrete tracking differentiator, the second fast discrete tracking differentiator, the first nonlinear sliding mode fixed-time dilated state observer, the second nonlinear sliding mode fixed-time dilated state observer, the first nonlinear fast convergence stabilization controller, the second nonlinear fast convergence stabilization controller, and the adaptive adjustment update algorithm for the control input gain into an algorithm function module and download it to the PLC hardware controller, and complete the signal input and output connection based on the parameter transfer relationship of steps 2 to 6;

[0012] Step 7: Repeat steps 2 to 6 to achieve adaptive coordinated control of the large-diameter plunger valve of the air extraction test device.

[0013] According to a specific implementation of the embodiment of the present application, establishing a dual-input single-output system mathematical model of the exhaust main pressure controlled object to obtain a state space model of the controlled object includes:

[0014] Establish a pipe network cavity pressure model;

[0015] Based on the existence of a first-order inertia link from the control input of the regulating valve to the opening of the inlet regulating valve in the actual system and the relationship between the regulating valve opening and the flow rate, a first relationship is established;

[0016] Based on the pipe network cavity pressure model and the first relationship, a state space model of the controlled object is obtained.

[0017] According to a specific implementation of the embodiment of the present application, the expression of the pipe network cavity pressure model is:

[0018] ,

[0019] Where T is the temperature in the cavity, p is the pressure in the cavity, V is the volume of the cavity, and W in1 is the air mass flow rate of the first intake regulating valve, W in2 is the air mass flow rate of the second 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 in1is the enthalpy of the air entering the first regulating valve, h in2 is the enthalpy of the air entering the second 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;

[0020] The first relational expression is:

[0021] ,

[0022] Among them, F1 is the first regulating valve angle, F2 is the second regulating valve angle, is the differential of the first regulating valve angle, is the differential of the first regulating valve angle, K mF1 K is the ratio of the air flow of the first regulating valve to the corresponding regulating valve angle, mF2 K is the ratio of the air flow of the second regulating valve to the corresponding regulating valve angle, θ1 is the first proportional coefficient, K θ2 is the second proportional coefficient, T θ1 is the inertia time constant of the first regulating valve, T θ2 is the inertia time constant of the second regulating valve;

[0023] The expression of the state space model is:

[0024] ,

[0025] in, and are the differentials of the controlled pressure, is the second-order differential of the controlled pressure, b F1 is the first regulating valve control input gain, b F2 is the second regulating valve control input gain, f p is the unknown total disturbance acting on the controlled system, b F1 and b F2 Determined by the following formula:

[0026] .

[0027] According to a specific implementation of the embodiment of the present application, the first fast discrete tracking differentiator and the second fast discrete tracking differentiator adopt the same calculation strategy, and the implementation steps include:

[0028] ,

[0029] Where k is the time series step of the discretized system, r is the speed factor, h is the sampling step, v(k) is the input signal at the current moment, and v -1 is the value of v(k) at the previous moment, r 11 (k) is the initial filtered signal output by the tracking differentiator, r 11-1 For r 11 (k) the value at the previous moment, r 12 (k) is the differential signal output by the tracking differentiator, r 12-1 、x 12 r 12 (k) the value at the previous moment, c 01 is the smoothness coefficient, x 11 is the output tracking error, out(k) is the final filtered output signal of the tracking differentiator after correction, and danfast is the sliding mode maximum speed control integrated function;

[0030] For the first fast discrete tracking differentiator, v(k), r 11 (k), r 12 (k) corresponds to p respectively set 、x 1d 、x 2d ; For the second fast discrete tracking differentiator, v(k), r 11 (k) corresponds to p respectively act 、y.

[0031] According to a specific implementation of the embodiment of the present application, the discrete form of the sliding mode maximum speed control comprehensive function is:

[0032]

[0033] ,

[0034] Where x1 and x2 are the states of the second-order series system, curve is the state sliding surface equation, m=sign(curve) is the sign function, t1 and t2 are the time calculation parameters related to the state sliding surface, and d and d0 are intermediate variables.

[0035] According to a specific implementation of the embodiment of the present application, the first nonlinear sliding mode fixed-time extended state observer and the second nonlinear sliding mode fixed-time extended state observer adopt the same calculation strategy, and the implementation steps are as follows:

[0036] ,

[0037] Where c is the observer gain coefficient, and c>1.0, β is the observer bandwidth parameter, z1(k) is the pressure estimate output at the current moment, and z 1-1is the value of z1(k) at the previous moment, z2(k) is the estimated value of the output pressure differential at the current moment, and z 2-1 is the value of z2(k) at the previous moment, z3(k) is the estimated value of the total disturbance of the control loop at the current moment, and z 3-1 is the value of z3(k) at the last moment, e1(k) is the estimation error, e 1-1 is the value of e1(k) at the previous moment, d(k) is the differential of the estimated error, u(k) is the control input, x(k) is the filtered output of the feedback signal at the current moment after the tracking differentiator, and x -1 is the value of x(k) at the previous moment, b Fm0 is the first regulating valve control input gain estimation value or the second regulating valve control input gain estimation value, is the sliding mode nonlinear fixed time convergence function;

[0038] For the first nonlinear sliding mode fixed-time extended state observer, x(k), z1(k), z2(k), z3(k), u(k), Corresponding parameters respectively 、 、 、 、 、 ; For the second nonlinear sliding mode fixed-time extended state observer, x(k), z1(k), z2(k), z3(k), u(k), Corresponding parameters respectively 、 、 、 、 、 .

[0039] According to a specific implementation of the embodiment of the present application, the expression of the sliding mode nonlinear fixed time convergence function is:

[0040] ,

[0041] Among them, a is the exponential parameter, is the approach parameter, , e is a natural constant, is the parameter adjustment, sgn() is the sign function, s is the fixed time convergent non-singular sliding surface, is a function related to the fixed time convergence law, s and The expressions are:

[0042] ,

[0043] in, is the adjustment coefficient, .

[0044] According to a specific implementation of the embodiment of the present application, the first regulating valve control input u is generated F1 ,include:

[0045] Filter signal x according to pressure command 1d and the pressure state estimation value z of the first regulating valve control circuit 11 Calculate the first pressure tracking error e 11 , according to the pressure command differential signal x 2d and the pressure differential state estimation value z of the first regulating valve control loop 12 The first pressure tracking differential error e is calculated 12 , e 11 and e 12 The expression is:

[0046] ;

[0047] Based on the first pressure tracking error e 11 and the first pressure tracking differential error e 12 , define the first nonlinear sliding surface s1, the expression of s1 is:

[0048] ;

[0049] Based on the total disturbance estimate z of the first regulating valve control loop 13 and the first nonlinear sliding surface s1, and obtain the first controller output u 01 ,u 01 The expression is:

[0050] ;

[0051] Based on the first controller output u 01 and the first regulating valve control input gain b F10 , generating the first regulating valve control input u F1 ,u F1 The expression is:

[0052] ;

[0053] in, is the first preset time function, is the first nonlinear parameter, , T c11 is the first preset time when the error converges to 0 after the state reaches the sliding surface, is the second-order differential signal of the pressure command, T c12 The first preset time for the state to reach the sliding surface, for The differential signal.

[0054] According to a specific implementation of the embodiment of the present application, the second regulating valve control input u is generated F2 ,include:

[0055] Filter signal x according to pressure command 1d and the pressure state estimation value z of the second regulating valve control loop 21 The second pressure tracking error e is calculated 21 , according to the pressure command differential signal x 2d and the pressure differential state estimation value z of the second regulating valve control loop 22 The second pressure tracking differential error e is calculated 22 , e 21 and e 22 The expression is:

[0056] ;

[0057] Based on the second pressure tracking error e 21 and the second pressure tracking differential error e 22 , define the second nonlinear sliding surface s2, the expression of s2 is:

[0058] ;

[0059] Based on the total disturbance estimate z of the second regulating valve control loop 23 and the second nonlinear sliding surface s2, and obtain the second controller output u 02 ,u 02 The expression is:

[0060] ;

[0061] Based on the second controller output u 02 and the second regulating valve control input gain b F20 , generating the second regulating valve control input u F2 ,u F2 The expression is:

[0062] ;

[0063] in, is the second preset time function, is the second nonlinear parameter, , T c21 T is the second preset time for the error to converge to 0 after the state reaches the sliding surface. c22 The state reaches the second preset time of the sliding surface, for The differential signal.

[0064] According to a specific implementation of an embodiment of the present application, the adaptive adjustment update strategy based on the control valve angle / flow model guidance includes a first control valve adaptive adjustment update strategy and a second control valve adaptive adjustment update strategy, wherein the first control valve adaptive adjustment update strategy includes:

[0065] The flow calculation model of the first regulating valve is established, and the model expression is:

[0066] ;

[0067] A preliminary update is performed based on the first regulating valve flow calculation model. The preliminary update expression is:

[0068] ;

[0069] Based on the preliminary updated expression, the first regulating valve control input gain b is formed F10 The first adaptive update strategy is to adjust the b in the first nonlinear sliding mode fixed time extended state observer and the first nonlinear fast convergence stabilization controller in real time. F10 Parameters, the first adaptive update strategy expression is;

[0070] ;

[0071] The second regulating valve adaptive adjustment update strategy includes:

[0072] The flow calculation model of the second regulating valve is established, and the model expression is:

[0073] ;

[0074] A preliminary update is performed based on the flow calculation model of the second regulating valve. The preliminary update expression is:

[0075] ;

[0076] Based on the preliminary updated expression, the second regulating valve control input gain b is formed F20 The second adaptive update strategy is to adjust the b in the second nonlinear sliding mode fixed-time extended state observer and the second nonlinear fast convergence stabilization controller in real time. F20 Parameters, the second adaptive update strategy expression is;

[0077] ;

[0078] in, is the flow coefficient of the first regulating valve, A 01 is the effective flow area of ​​the first regulating valve, To adjust the density before the valve, is the difference between the pressure before and after the regulating valve, kb1 is the first regulating coefficient, b F10cal is the preliminary calculated value of the control input gain of the first regulating valve, b min1 for b F10 Parameter lower limit, b max1 for b F10 Parameter upper limit value, is the flow coefficient of the second regulating valve, A 02 is the effective flow area of ​​the second regulating valve, kb2 is the second regulating coefficient, b F20cal is the preliminary calculated value of the second regulating valve control input gain, b min2 for b F20 Parameter lower limit, b max2 for b F20 Upper limit of the parameter.

[0079] Beneficial effects:

[0080] The adaptive collaborative control method for a large-caliber plunger valve in a large-scale air extraction test device in the embodiment of the present application has the following beneficial effects:

[0081] (1) The present invention achieves rapid convergence and stabilization of the controlled pressure ratio of the exhaust unit under a wide flow rate change range and the impact of a large flow rate change rate, solves the difficult problem of joint matching of a large flow engine and a large exhaust unit cluster in an air environment simulation test, and effectively guarantees the progress of the large flow engine test development and the efficient and safe implementation of the air environment simulation test.

[0082] (2) The present invention greatly reduces the manpower cost of operating the gas source unit. The robust adaptive parameter adjustment technology adopted can achieve high-quality control effects without manual intervention by operators, greatly reducing the labor intensity of gas source system control personnel and effectively avoiding the risk of misoperation. The number of personnel is reduced to one-third of the original number.

[0083] (3) The present invention demonstrates excellent performance, such as low overshoot, small dynamic deviation under strong disturbances, and fast convergence. This broadens the safe test utilization range of compressor units, provides a guarantee for optimizing unit resource matching under some boundary point tests, and significantly reduces test energy consumption and economic costs. Furthermore, the present invention can serve as a technical foundation for application scenarios such as fully automated grid connection of large compressor unit clusters and coordinated strong anti-interference of multiple actuators with ultra-large flow rates, and has broad prospects for promotion and application. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] 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.

[0085] Figure 1 This is a schematic diagram of a large compressor cluster main pipe pressure control system according to an embodiment;

[0086] Figure 2 This is a schematic diagram of a method for adaptive coordinated control of a large-diameter plunger valve in a large-scale air extraction test device according to one embodiment;

[0087] Figure 3 This is a model of a large-diameter plunger valve with dual input and single output controlled object in one embodiment;

[0088] Figure 4 Schematic diagram of rapid flow interference changes under simulated working conditions with a main exhaust pipe pressure of 20-30 kPa in one embodiment;

[0089] Figure 5 This is a schematic diagram of the control effect of a simulated working condition with a main exhaust pipe pressure of 20-30 kPa according to an embodiment;

[0090] Figure 6 Schematic diagram of the adaptive adjustment process of the simulated working condition parameters of the exhaust main pipe pressure of 20-30 kPa in one embodiment;

[0091] Figure 7 Schematic diagram of the movement angle of a large-diameter plunger valve under a simulated working condition with a main exhaust pipe pressure of 20 to 30 kPa according to an embodiment;

[0092] Figure 8 Schematic diagram of the engine flow interference change process under actual working conditions with an exhaust manifold pressure of 33 kPa according to an embodiment;

[0093] Figure 9 This is a schematic diagram of the actual working condition control effect of an exhaust main pipe pressure of 33kPa in one embodiment;

[0094] Figure 10 Schematic diagram of the adaptive adjustment process of actual working condition parameters of the exhaust main pressure of 33kPa in one embodiment;

[0095] Figure 11 Schematic diagram of the movement angle of a large-diameter plunger valve under actual working conditions with a main exhaust pipe pressure of 33 kPa according to an embodiment. DETAILED DESCRIPTION

[0096] The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] This application aims to solve the problem of high-quality control of the main pipe pressure of a large-scale gas extraction test device under the impact of a large flow change range and a large instantaneous flow change rate, and develops an adaptive collaborative control method for large-diameter plunger valves in a large-scale gas extraction test device. It adopts a composite control strategy of self-anti-disturbance + sliding mode and an overall control technology architecture of rapid adaptive adjustment of core parameters, and designs a set of nonlinear robust control methods and algorithms that integrate high-precision state information extraction under the influence of noise, rapid interference observation and estimation, nonlinear sliding mode rapid convergence control and model-guided rapid parameter adaptive adjustment. It can not only effectively cope with the influence of strong noise and strong uncertain interference on the control system, but also efficiently solve the strong coupling problem of the actuator during rapid movement, and can meet the high-quality adjustment requirements under a wide flow change range and the impact of a large instantaneous flow change rate.

[0102] This method can automatically and effectively extract state information, rapidly suppress various types of strong, uncertain interference, and achieve real-time decoupling of actuators during rapid motion, without human intervention. It boasts strong anti-interference capabilities, high versatility, excellent robustness, and simple and efficient implementation. It addresses the challenges of transient voltage regulation and stabilization control for large-scale exhaust unit clusters in high-flow engine transient state testing scenarios. This invention broadens the safe test utilization range of compressor units, ensures optimal unit resource matching under certain boundary point tests, and significantly reduces test energy consumption and economic costs.

[0103] Refer to the following Figures 1 to 11 The adaptive collaborative control method of the large-diameter plunger valve of the large-scale air extraction test device of the present application is described in detail.

[0104] In one embodiment, the principle of the large compressor cluster main pipe pressure control system is as follows: Figure 1 As shown, the controlled objects mainly include sensors, two DN3600 large-diameter plunger-type regulating valves (the first regulating valve and the second regulating valve), a large-volume exhaust system pipe network, an electro-hydraulic servo actuator, etc. The interference sources are mainly the ultra-large-scale flow change interference of the test engine, the measurement noise interference, and various uncertain interferences of large equipment. The control system hardware collects the measured value of the exhaust main pressure in real time, solves the control instructions through the controller, and drives the two large-diameter regulating valves to complete the rapid adjustment of the air flow to realize strong anti-interference control of the exhaust main pressure. The principle of the adaptive collaborative control method of large-diameter plunger valves in large-scale exhaust devices of this application is as follows Figure 2As shown in the figure, the control structure includes the following core algorithm modules: a first fast discrete tracking differentiator (Supfast-1), a second fast discrete tracking differentiator (Supfast-2), a first nonlinear sliding mode fixed-time extended state observer (SMCESO-1), a second nonlinear sliding mode fixed-time extended state observer (SMCESO-2), two specified time fast convergence stabilization controllers (first nonlinear fast convergence stabilization controller (PTDRC-1) and first nonlinear fast convergence stabilization controller (PTDRC-2)), and a set of control input gains and fast adaptive adjustment update algorithm strategies based on the control valve angle / flow model guidance. Among them, the function of Supfast-1 is to generate a reference pressure command and a pressure command differential signal, and the function of Supfast-2 is to generate a smooth and small-lag pressure feedback filter signal; the main function of SMCESO-1 is to observe and estimate the total disturbance excluding the control input of the first control valve, and the main function of SMCESO-2 is to observe and estimate the total disturbance excluding the control input of the second control valve; the main functions of PTDRC-1 and PTDRC-2 are to generate control quantities through state feedback to drive the actual controlled object to operate according to a predetermined rule; the main function of the fast adaptive adjustment update algorithm is to achieve fast real-time decoupling between the two control loops; the above algorithm modules are downloaded to the actual controller hardware and completed in different control cycle deployments. All algorithm modules work together, with the set pressure as the given quantity, to achieve rapid convergence of the controlled pressure ratio of the exhaust unit under a wide flow change range and large flow change rate impact. The specific implementation process is explained in detail below.

[0105] The present application provides an adaptive coordinated control method for a large-diameter plunger valve of a large-scale air extraction test device, the method comprising:

[0106] Step 1: Establish a mathematical model of a dual-input single-output system of the main exhaust pipe pressure controlled object to obtain a state space model of the controlled object;

[0107] Step 2: Based on the state space model, the pressure target setting value p is given set Input to the first fast discrete tracking differentiator to calculate and generate the pressure command filter signal x 1d and pressure command differential signal x 2d , the noise pressure sensor measurement signal p act Input to the second fast discrete tracking differentiator to calculate and generate the filtered output signal y;

[0108] Step 3: Control the first regulating valve input u F1 The filtered output signal y is input to the first nonlinear sliding mode fixed time extended state observer, and the pressure state estimation value z of the first regulating valve control loop is output. 11, the pressure differential state estimation value z of the first regulating valve control loop 12 , the total disturbance estimate z of the first regulating valve control loop 13 ; The second regulating valve control input u F2 The filtered output signal y is input to the second nonlinear sliding mode fixed time extended state observer, and the pressure state estimation value z of the second regulating valve control loop is output. 21 , the pressure differential state estimation value z of the second regulating valve control loop 22 , the total disturbance estimate z of the second regulating valve control loop 23 ;

[0109] Step 4: Filter the pressure command signal x 1d , pressure command differential signal x 2d , filtered output signal y, pressure state estimation value z of the first regulating valve control loop 11 , the pressure differential state estimation value z of the first regulating valve control loop 12 and the total disturbance estimate z of the first regulating valve control loop 13 Input to the input end of the first nonlinear fast convergence stabilization controller to generate the first regulating valve control input u F1 ;Filter the pressure command signal x 1d , pressure command differential signal x 2d , filtered output signal y, pressure state estimation value z of the second regulating valve control loop 21 , the pressure differential state estimation value z of the second regulating valve control loop 22 and the total disturbance estimate z of the second regulating valve control loop 23 Input to the input end of the second nonlinear fast convergence stabilization controller to generate the second regulating valve control input u F2 ;

[0110] Step 5: Use the adaptive adjustment update strategy based on the control valve angle / flow model to calculate the first control valve control input gain b in real time. F10 And the second regulating valve control input gain b F20 , realize the b in the first nonlinear sliding mode fixed time extended state observer and the first nonlinear fast convergence stabilization controller F10 Parameter adjustment and implementation of the second nonlinear sliding mode fixed time extended state observer and the second nonlinear fast convergence stabilization controller b F20 Parameter adjustment;

[0111] Step 6: Encapsulate the first fast discrete tracking differentiator, the second fast discrete tracking differentiator, the first nonlinear sliding mode fixed-time dilated state observer, the second nonlinear sliding mode fixed-time dilated state observer, the first nonlinear fast convergence stabilization controller, the second nonlinear fast convergence stabilization controller, and the adaptive adjustment update algorithm for the control input gain into an algorithm function module and download it to the PLC hardware controller, and complete the signal input and output connection based on the parameter transfer relationship of steps 2 to 6;

[0112] Step 7: Repeat steps 2 to 6 to achieve adaptive collaborative control of the large-diameter plunger valve of the air extraction test device with high precision, strong anti-interference and fast convergence and stable control capabilities.

[0113] In one embodiment, establishing a dual-input single-output system mathematical model of the main exhaust pressure controlled object to obtain a state space model of the controlled object includes:

[0114] Establish a pipe network cavity pressure model;

[0115] Based on the existence of a first-order inertia link from the control input of the regulating valve to the opening of the inlet regulating valve in the actual system and the relationship between the regulating valve opening and the flow rate, a first relationship is established;

[0116] Based on the pipe network cavity pressure model and the first relationship, a state space model of the controlled object is obtained.

[0117] Specifically, the expression of the pipe network cavity pressure model is:

[0118] (1),

[0119] Where T is the temperature in the cavity, p is the pressure in the cavity, V is the volume of the cavity, and W in1 is the air mass flow rate of the first intake regulating valve, W in2 is the air mass flow rate of the second 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 in1 is the enthalpy of the air entering the first regulating valve, h in2 is the enthalpy of the air entering the second 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;

[0120] The first relational expression is:

[0121] (2),

[0122] Among them, F1 is the first regulating valve angle, F2 is the second regulating valve angle, is the differential of the first regulating valve angle, is the differential of the first regulating valve angle, K mF1 K is the ratio of the air flow of the first regulating valve to the corresponding regulating valve angle, mF2 K is the ratio of the air flow of the second regulating valve to the corresponding regulating valve angle, θ1 is the first proportional coefficient, K θ2 is the second proportional coefficient, T θ1 is the inertia time constant of the first regulating valve, T θ2 is the inertia time constant of the second regulating valve;

[0123] The expression of the state space model is:

[0124] (3),

[0125] in, and are the differentials of the controlled pressure, is the second-order differential of the controlled pressure, b F1 is the first regulating valve control input gain, b F2 is the second regulating valve control input gain, f p is the unknown total disturbance acting on the controlled system, b F1 and b F2 Determined by the following formula:

[0126] (4).

[0127] In specific implementation, the control input u F1 All other effects are attributed to disturbances, and the control input gain b is considered F1 The uncertainty of , the state space model can be written as:

[0128] (5),

[0129] Where, is the total system disturbance when the first regulating valve is used as the control input, It mainly includes model parameter uncertainty, control input gain uncertainty, external strong disturbance and coupling disturbance from the second regulating valve.

[0130] Control input u F2 All other effects are attributed to disturbances, and the control input gain b is considered F2 The uncertainty of , the state space model can be written as:

[0131] (6),

[0132] Where, is the total system disturbance when the second regulating valve is used as the control input, It mainly includes model parameter uncertainty, control input gain uncertainty, external strong disturbance and coupling disturbance from the first regulating valve.

[0133] From equations (5) and (6), we can see that the controlled object can be regarded as a typical second-order integrator series system. The controlled pressure output is the control input u F1 and , control input u F2 and As a result of the combined effect, the model of the dual-input single-output controlled object is as follows Figure 3 shown.

[0134] In one embodiment, the first fast discrete tracking differentiator Supfast-1 and the second fast discrete tracking differentiator Supfast-2 adopt the same calculation strategy, which is implemented by the following steps:

[0135] (7),

[0136] 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 input signal 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 tracking differentiator, r 11-1 For r 11 (k) the value at the previous moment, r 12 (k) is the differential signal output by the tracking differentiator, r 12-1 、x 12 r 12 (k) the value at the previous moment, c 01 is the smoothness coefficient, x 11 is the output tracking error, out(k) is the final filtered output signal of the tracking differentiator after correction, and danfast is the sliding mode maximum speed control integrated function;

[0137] For the first fast discrete tracking differentiator, v(k), r in formula (7) 11 (k), r 12 (k) respectively correspond to the actual parameters p set 、x 1d 、x 2d ; For the second fast discrete tracking differentiator, v(k), r in formula (7) 11(k) respectively correspond to the actual parameters p act 、y.

[0138] Furthermore, the sliding mode maximum speed control comprehensive function aims to make the states x1 and x2 of the second-order series system reach the origin quickly and stably, and its discrete form is:

[0139]

[0140] (8),

[0141] Where x1 and x2 are the states of the second-order series system, curve is the state sliding surface equation, m=sign(curve) is the sign function, t1 and t2 are the time calculation parameters related to the state sliding surface, and d and d0 are intermediate variables.

[0142] In one embodiment, the first nonlinear sliding mode fixed-time extended state observer and the second nonlinear sliding mode fixed-time extended state observer adopt the same calculation strategy, which is implemented as follows:

[0143] (9),

[0144] Where c is the observer gain coefficient, and c>1.0. Increasing c can effectively improve the tracking accuracy of the observer. β is the observer bandwidth parameter. Increasing β can effectively improve the tracking speed of the observer. z1(k) is the pressure estimate output at the current moment. 1-1 is the value of z1(k) at the previous moment, z2(k) is the estimated value of the output pressure differential at the current moment, and z 2-1 is the value of z2(k) at the previous moment, z3(k) is the estimated value of the total disturbance of the control loop at the current moment, and z 3-1 is the value of z3(k) at the last moment, e1(k) is the estimation error, e 1-1 is the value of e1(k) at the previous moment, d(k) is the differential of the estimated error, u(k) is the control input, x(k) is the filtered output of the feedback signal at the current moment after the tracking differentiator, and x -1 is the value of x(k) at the previous moment, b Fm0 is the first regulating valve control input gain estimation value or the second regulating valve control input gain estimation value, is the sliding mode nonlinear fixed time convergence function;

[0145] For the first nonlinear sliding mode fixed-time extended state observer, x(k), z1(k), z2(k), z3(k), u(k), Corresponding to the actual parameters respectively 、 、 、 、 、 ; For the second nonlinear sliding mode fixed time extended state observer, x(k), z1(k), z2(k), z3(k), u(k), Corresponding to the actual parameters respectively 、 、 、 、 、 .

[0146] Furthermore, the expression of the sliding mode nonlinear fixed time convergence function is:

[0147] (10),

[0148] Among them, a is an exponential parameter. The value of the exponential parameter a has little effect on the observer. Usually a is set to a fixed value of 1.2. is the approach parameter, , e is a natural constant, To adjust the parameters, , sgn() is the sign function, s is the fixed time convergent non-singular sliding mode surface, is a function related to the fixed time convergence law, s and The expressions are:

[0149] (11),

[0150] in, is the adjustment coefficient, .

[0151] In specific implementation, the nonlinear sliding mode fixed-time extended state observer corresponding to Equation (9) can make the observation error e1(k) converge quickly to the vicinity of 0 by simply adjusting the parameters c and β, thereby achieving ideal system estimation results.

[0152] In one embodiment, the generating of the first regulating valve control input u F1 ,include:

[0153] Filter signal x according to pressure command 1d and the pressure state estimation value z of the first regulating valve control circuit 11 Calculate the first pressure tracking error e 11 , according to the pressure command differential signal x 2d and the pressure differential state estimation value z of the first regulating valve control loop 12 The first pressure tracking differential error e is calculated 12, e 11 and e 12 The expression is:

[0154] (12);

[0155] Based on the first pressure tracking error e 11 and the first pressure tracking differential error e 12 , define the first nonlinear sliding surface s1, the expression of s1 is:

[0156] (13);

[0157] Based on the total disturbance estimate z of the first regulating valve control loop 13 and the first nonlinear sliding surface s1, and obtain the first controller output u 01 ,u 01 The expression is:

[0158] (14);

[0159] Based on the first controller output u 01 and the first regulating valve control input gain b F10 , generating the first regulating valve control input u F1 ,u F1 The expression is:

[0160] (15);

[0161] in, is the first preset time function, is the first nonlinear parameter, , T c11 is the first preset time when the error converges to 0 after the state reaches the sliding surface, is the second-order differential signal of the pressure command, T c12 The first preset time for the state to reach the sliding surface, for The differential signal of Obtained by the tracking differentiator Supfast in step 2.

[0162] In one embodiment, the generating of the second regulating valve control input u F2 ,include:

[0163] Filter signal x according to pressure command 1d and the pressure state estimation value z of the second regulating valve control loop 21 The second pressure tracking error e is calculated 21 , according to the pressure command differential signal x 2dand the pressure differential state estimation value z of the second regulating valve control loop 22 The second pressure tracking differential error e is calculated 22 , e 21 and e 22 The expression is:

[0164] (16);

[0165] Based on the second pressure tracking error e 21 and the second pressure tracking differential error e 22 , define the second nonlinear sliding surface s2, the expression of s2 is:

[0166] (17);

[0167] Based on the total disturbance estimate z of the second regulating valve control loop 23 and the second nonlinear sliding surface s2, and obtain the second controller output u 02 ,u 02 The expression is:

[0168] (18);

[0169] Based on the second controller output u 02 and the second regulating valve control input gain b F20 , generating the second regulating valve control input u F2 ,u F2 The expression is:

[0170] (19);

[0171] in, is the second preset time function, is the second nonlinear parameter, , T c21 T is the second preset time for the error to converge to 0 after the state reaches the sliding surface. c22 The state reaches the second preset time of the sliding surface, for The differential signal of Obtained by the tracking differentiator Supfast in step 2.

[0172] In one embodiment, in order to overcome the strong coupling interference in the rapid motion of the regulating valve and further enhance the interference observation capability of the extended state observer, an adaptive adjustment update algorithm based on the regulating valve angle / flow model guidance is used to calculate the first regulating valve control input gain b in real time. F10 And the second regulating valve control input gain b F20, realize b in SMCESO-1 and PTDRC-1 F10 Parameter adjustment, and b in SMCESO-2 and PTDRC-2 F20 Parameter adjustment, the adaptive adjustment update strategy based on the control valve angle / flow model guidance includes a first control valve adaptive adjustment update strategy and a second control valve adaptive adjustment update strategy, and its implementation steps are as follows:

[0173] The first regulating valve adaptive adjustment update strategy includes:

[0174] The flow calculation model of the first regulating valve is established, and the model expression is:

[0175] (20),

[0176] Where, is the flow coefficient of the first regulating valve, Determined from Table 1, A 01 is the effective flow area of ​​the first regulating valve, To adjust the density before the valve, It is the difference between the pressure before and after the regulating valve;

[0177] A preliminary update is performed based on the first regulating valve flow calculation model. The preliminary update expression is:

[0178] (twenty one),

[0179] In the formula, kb1 is the first adjustment coefficient, b F10cal A preliminary calculated value of the control input gain of the first regulating valve;

[0180] Based on the preliminary updated expression, the first regulating valve control input gain b is finally formed F10 The first adaptive update strategy is to adjust the b in the first nonlinear sliding mode fixed time extended state observer and the first nonlinear fast convergence stabilization controller in real time according to the following formula (22): F10 Parameters, the first adaptive update strategy expression is;

[0181] (twenty two);

[0182] Where b min1 for b F10 Parameter lower limit, b max1 for b F10 Upper limit of the parameter.

[0183] The second regulating valve adaptive adjustment update strategy includes:

[0184] The flow calculation model of the second regulating valve is established, and the model expression is:

[0185] (twenty three),

[0186] Where, is the flow coefficient of the second regulating valve, Determined from Table 2, A 02 is the effective flow area of ​​the second regulating valve;

[0187] A preliminary update is performed based on the flow calculation model of the second regulating valve. The preliminary update expression is:

[0188] (twenty four),

[0189] In the formula, kb2 is the second adjustment coefficient, b F20cal A preliminary calculated value of the control input gain of the second regulating valve;

[0190] Based on the preliminary updated expression, the second regulating valve control input gain b is formed F20 The second adaptive update strategy is to adjust the b in the second nonlinear sliding mode fixed time extended state observer and the second nonlinear fast convergence stabilization controller in real time according to formula (25). F20 Parameters, the second adaptive update strategy expression is;

[0191] (25);

[0192] Among them, b min2 for b F20 Parameter lower limit, b max2 for b F20 Upper limit of the parameter.

[0193] Table 1 Flow coefficient of the first regulating valve

[0194]

[0195] In Table 1, F1 is the real-time opening of the first regulating valve, Pr1 is the ratio of the pressure after the first regulating valve to the pressure before the valve, and m1 is the ratio of the effective flow area of ​​the first regulating valve to the total flow area.

[0196] Table 2 Flow coefficient of the second regulating valve

[0197]

[0198] In Table 2, F2 is the real-time opening of the second regulating valve, Pr2 is the ratio of the pressure after the second regulating valve to the pressure before the valve, and m2 is the ratio of the effective flow area of ​​the second regulating valve to the total flow area.

[0199] In one embodiment, for step 6, the actual hardware discrete control step sizes of the supfast-1, supfast-2, SMCESO-1, and SMCESO-2 modules are set to 1 ms and 2 ms, respectively. The actual hardware discrete control step sizes of the PTDRC-1, PTDRC-2, and control input gain fast adaptive adjustment update module are set to 10 ms and 5 ms, respectively.

[0200] In one embodiment, the effect of implementing the method of the present application is described with a detailed embodiment.

[0201] 1) Implementation effect in simulation environment

[0202] The typical working condition of exhaust main pressure of 20kPa to 30kPa was selected on the simulation platform to carry out simulation verification of adaptive collaborative anti-disturbance control of large-diameter plunger valve.

[0203] The simulation process is as follows:

[0204] 1) First, a stable operating condition with an exhaust manifold pressure of 20 kPa was established. Simulating the engine transient flow rate disturbances 1 and 2, where the flow rate changes from 40 kg / s to 100 kg / s and then from 100 kg / s to 40 kg / s, the exhaust manifold target pressure was subjected to collaborative anti-disturbance control under transient large flow disturbances.

[0205] 2) Generate a 20kPa ↗ 30kPa set pressure value ramp input trajectory and perform target value trajectory tracking control on the exhaust main pressure.

[0206] 3) Under stable exhaust manifold pressure of 30 kPa, simulate large-scale disturbances within engine transient flow rates 1 and 2: 40 kg / s to 130 kg / s, 130 kg / s to 40 kg / s, 40 kg / s to 160 kg / s, and 160 kg / s to 40 kg / s. Coordinated anti-disturbance control was performed on the exhaust manifold target pressure under transient large flow disturbances.

[0207] Rapid engine flow disturbance changes such as Figure 4 As shown, the implementation effect is as follows Figure 5 shown.

[0208] from Figure 5 As can be seen, whether the exhaust manifold pressure is 20 kPa or 30 kPa, when subjected to instantaneous, large flow disturbances, the maximum instantaneous fluctuation of the controlled pressure is ≤ 0.5 kPa, and the adjustment time is ≤ 4 seconds. The adaptive collaborative control technology for large-diameter plunger valves achieves ideal control results. It also achieves high-performance target value trajectory tracking control.

[0209] Figure 6It adopts a fast adaptive adjustment update process guided by the control valve angle / flow model, which realizes the rapid decoupling of large-diameter plunger valves during rapid coordinated movement.

[0210] Figure 7 It is the angle adjustment process of the large-diameter plunger valve driven by the adaptive collaborative control method, and its operation trajectory reflects a good collaborative control process.

[0211] 2) Actual system test verification effect

[0212] After completing the transplantation of the real PLC controller algorithm and implementing the algorithm control cycle deployment, a typical operating condition of 33kPa was established on the exhaust system to test and verify the actual system under short-term ultra-large flow interference.

[0213] Depend on Figure 8 As can be seen, the actual engine flow disturbance first changes from 50 kg / s to 450 kg / s within 8 seconds, and then changes from 450 kg / s to 50 kg / s within 8 seconds. During this process, the actual engine flow disturbance changes by approximately 400 kg / s, indicating that the control system was impacted by a short, strong disturbance.

[0214] Figure 9 The control effect test under the above-mentioned large flow and strong interference was carried out under the working condition of 33kPa. It can be seen that when the large-diameter plunger valve is driven by the adaptive collaborative control method, the maximum fluctuation of the controlled pressure is ≯2.0kPa, and the dynamic response is fast, the adjustment accuracy is high, and the controlled pressure can quickly converge to the target pressure value.

[0215] Figure 10 The method adopts a fast adaptive adjustment update process guided by the control valve angle / flow model, which realizes the fast decoupling of the large-diameter plunger valve during the rapid coordinated movement in the actual system.

[0216] Figure 11 It is the angle adjustment process of a large-diameter plunger valve driven by an adaptive collaborative control method during actual system testing and verification.

[0217] In summary, the adaptive collaborative control method for large-diameter plunger valves in large-scale air extraction test equipment achieves interference observation, adaptive decoupling, and rapid convergence and stabilization of the exhaust system main pipe pressure under a wide flow rate variation range and high flow rate change impact, thereby realizing the robustness of the control system and its adaptive parameter adjustment capabilities. This technology not only realizes the control process without human intervention, significantly reducing the operator's labor intensity and the risk of misoperation. It also solves the difficult problem of jointly matching high-flow engines and large-capacity exhaust unit clusters in air environment simulation tests, effectively ensuring the efficient and safe implementation of high-flow engine tests.

[0218] 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. An adaptive collaborative control method for a large-caliber plunger valve in a large-scale air extraction test device, characterized in that: The method comprises: Step 1: Establish a mathematical model of a dual-input single-output system of the main exhaust pipe pressure controlled object to obtain a state space model of the controlled object; Step 2: Based on the state space model, the pressure target setting value p is given set Input to the first fast discrete tracking differentiator to calculate and generate the pressure command filter signal x 1d and pressure command differential signal x 2d , the noise pressure sensor measurement signal p act Input to the second fast discrete tracking differentiator to calculate and generate the filtered output signal y; Step 3: Control the first regulating valve input u F1 The filtered output signal y is input to the first nonlinear sliding mode fixed time extended state observer, and the pressure state estimation value z of the first regulating valve control loop is output. 11 , the pressure differential state estimation value z of the first regulating valve control loop 12 , the total disturbance estimate z of the first regulating valve control loop 13 ; The second regulating valve control input u F2 The filtered output signal y is input to the second nonlinear sliding mode fixed time extended state observer, and the pressure state estimation value z of the second regulating valve control loop is output. 21 , the pressure differential state estimation value z of the second regulating valve control loop 22 , the total disturbance estimate z of the second regulating valve control loop 23 ; Step 4: Filter the pressure command signal x 1d , pressure command differential signal x 2d , filtered output signal y, pressure state estimation value z of the first regulating valve control loop 11 , the pressure differential state estimation value z of the first regulating valve control loop 12 and the total disturbance estimate z of the first regulating valve control loop 13 Input to the input end of the first nonlinear fast convergence stabilization controller to generate the first regulating valve control input u F1 ;Filter the pressure command signal x 1d , pressure command differential signal x 2d , filtered output signal y, pressure state estimation value z of the second regulating valve control loop 21 , the pressure differential state estimation value z of the second regulating valve control loop 22 and the total disturbance estimate z of the second regulating valve control loop 23 Input to the input end of the second nonlinear fast convergence stabilization controller to generate the second regulating valve control input u F2 ; Step 5: Use the adaptive adjustment update strategy based on the control valve angle / flow model to calculate the first control valve control input gain b in real time. F10 And the second regulating valve control input gain b F20 , realize the b in the first nonlinear sliding mode fixed time extended state observer and the first nonlinear fast convergence stabilization controller F10 Parameter adjustment and implementation of the second nonlinear sliding mode fixed time extended state observer and the second nonlinear fast convergence stabilization controller b F20 Parameter adjustment; Step 6: Encapsulate the first fast discrete tracking differentiator, the second fast discrete tracking differentiator, the first nonlinear sliding mode fixed-time dilated state observer, the second nonlinear sliding mode fixed-time dilated state observer, the first nonlinear fast convergence stabilization controller, the second nonlinear fast convergence stabilization controller, and the adaptive adjustment update algorithm for the control input gain into an algorithm function module and download it to the PLC hardware controller, and complete the signal input and output connection based on the parameter transfer relationship of steps 2 to 6; Step 7: Repeat steps 2 to 6 to achieve adaptive coordinated control of the large-diameter plunger valve of the air extraction test device.

2. The adaptive collaborative control method for a large-diameter plunger valve of a large-scale air extraction test device according to claim 1 is characterized in that: The method of establishing a dual-input single-output system mathematical model of the main exhaust pipe pressure controlled object and obtaining a state space model of the controlled object includes: Establish a pipe network cavity pressure model; Based on the existence of a first-order inertia link from the control input of the regulating valve to the opening of the inlet regulating valve in the actual system and the relationship between the regulating valve opening and the flow rate, a first relationship is established; Based on the pipe network cavity pressure model and the first relationship, a state space model of the controlled object is obtained.

3. The adaptive collaborative control method for a large-diameter plunger valve of a large-scale air extraction test device according to claim 2 is characterized in that: The expression of the pipe network cavity pressure 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 in1 is the air mass flow rate of the first intake regulating valve, W in2 is the air mass flow rate of the second 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 in1 is the enthalpy of the air entering the first regulating valve, h in2 is the enthalpy of the air entering the second 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, F1 is the first regulating valve angle, F2 is the second regulating valve angle, is the differential of the first regulating valve angle, is the differential of the first regulating valve angle, K mF1 K is the ratio of the air flow of the first regulating valve to the corresponding regulating valve angle, mF2 K is the ratio of the air flow of the second regulating valve to the corresponding regulating valve angle, θ1 is the first proportional coefficient, K θ2 is the second proportional coefficient, T θ1 is the inertia time constant of the first regulating valve, T θ2 is the inertia time constant of the second regulating valve; 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, b F1 is the first regulating valve control input gain, b F2 is the second regulating valve control input gain, f p is the unknown total disturbance acting on the controlled system, b F1 and b F2 Determined by the following formula: 。 4. The adaptive collaborative control method for a large-diameter plunger valve of a large-scale air extraction test device according to claim 1 is characterized in that: The first fast discrete tracking differentiator and the second fast discrete tracking differentiator adopt the same calculation strategy, and the implementation steps include: ， Where k is the time series step of the discretized system, r is the speed factor, h is the sampling step, v(k) is the input signal at the current moment, and v -1 is the value of v(k) at the previous moment, r 11 (k) is the initial filtered signal output by the tracking differentiator, r 11-1 For r 11 (k) the value at the previous moment, r 12 (k) is the differential signal output by the tracking differentiator, r 12-1 、x 12 r 12 (k) the value at the previous moment, c 01 is the smoothness coefficient, x 11 is the output tracking error, out(k) is the final filtered output signal of the tracking differentiator after correction, and danfast is the sliding mode maximum speed control integrated function; For the first fast discrete tracking differentiator, v(k), r 11 (k), r 12 (k) corresponds to p respectively set 、x 1d 、x 2d ; For the second fast discrete tracking differentiator, v(k), r 11 (k) corresponds to p respectively act 、y.

5. The adaptive collaborative control method for a large-diameter plunger valve of a large-scale air extraction test device according to claim 4 is characterized in that: The discrete form of the sliding mode maximum speed control comprehensive function is: ， Where x1 and x2 are the states of the second-order series system, curve is the state sliding surface equation, m=sign(curve) is the sign function, t1 and t2 are the time calculation parameters related to the state sliding surface, and d and d0 are intermediate variables.

6. The adaptive collaborative control method for a large-diameter plunger valve of a large-scale air extraction test device according to claim 5 is characterized in that: The first nonlinear sliding mode fixed-time extended state observer and the second nonlinear sliding mode fixed-time extended state observer adopt the same calculation strategy, and the implementation steps are as follows: ， Where c is the observer gain coefficient, and c>1.0, β is the observer bandwidth parameter, z1(k) is the pressure estimate output at the current moment, and z 1-1 is the value of z1(k) at the previous moment, z2(k) is the estimated value of the output pressure differential at the current moment, and z 2-1 is the value of z2(k) at the previous moment, z3(k) is the estimated value of the total disturbance of the control loop at the current moment, and z 3-1 is the value of z3(k) at the last moment, e1(k) is the estimation error, e 1-1 is the value of e1(k) at the previous moment, d(k) is the differential of the estimated error, u(k) is the control input, x(k) is the filtered output of the feedback signal at the current moment after the tracking differentiator, and x -1 is the value of x(k) at the previous moment, b Fm0 is the first regulating valve control input gain estimation value or the second regulating valve control input gain estimation value, is the sliding mode nonlinear fixed time convergence function; For the first nonlinear sliding mode fixed-time extended state observer, x(k), z1(k), z2(k), z3(k), u(k), Corresponding parameters respectively 、 、 、 、 、 ; For the second nonlinear sliding mode fixed-time extended state observer, x(k), z1(k), z2(k), z3(k), u(k), Corresponding parameters respectively 、 、 、 、 、 .

7. The adaptive coordinated control method for a large-diameter plunger valve of a large-scale air extraction test device according to claim 6 is characterized in that: The expression of the sliding mode nonlinear fixed time convergence function is: ， Among them, a is the exponential parameter, is the approach parameter, , e is a natural constant, is the parameter adjustment, sgn() is the sign function, s is the fixed time convergent non-singular sliding surface, is a function related to the fixed time convergence law, s and The expressions are: ， in, is the adjustment coefficient, .

8. The adaptive coordinated control method for a large-diameter plunger valve of a large-scale air extraction test device according to claim 1 is characterized in that: The first regulating valve control input u is generated F1 ,include: Filter the signal x according to the pressure command 1d and the pressure state estimation value z of the first regulating valve control circuit 11 Calculate the first pressure tracking error e 11 , according to the pressure command differential signal x 2d and the pressure differential state estimation value z of the first regulating valve control loop 12 The first pressure tracking differential error e is calculated 12 , e 11 and e 12 The expression is: ； Based on the first pressure tracking error e 11 and the first pressure tracking differential error e 12 , define the first nonlinear sliding surface s1, the expression of s1 is: ； Based on the total disturbance estimate z of the first regulating valve control loop 13 and the first nonlinear sliding surface s1, and obtain the first controller output u 01 ,u 01 The expression is: ； Based on the first controller output u 01 and the first regulating valve control input gain b F10 , generating the first regulating valve control input u F1 ,u F1 The expression is: ； in, is the first preset time function, is the first nonlinear parameter, , T c11 is the first preset time when the error converges to 0 after the state reaches the sliding surface, is the second-order differential signal of the pressure command, T c12 The first preset time for the state to reach the sliding surface, for The differential signal.

9. The adaptive coordinated control method for a large-diameter plunger valve of a large-scale air extraction test device according to claim 8 is characterized in that: The second regulating valve control input u is generated F2 ,include: Filter the signal x according to the pressure command 1d and the pressure state estimation value z of the second regulating valve control loop 21 The second pressure tracking error e is calculated 21 , according to the pressure command differential signal x 2d and the pressure differential state estimation value z of the second regulating valve control loop 22 The second pressure tracking differential error e is calculated 22 , e 21 and e 22 The expression is: ； Based on the second pressure tracking error e 21 and the second pressure tracking differential error e 22 , define the second nonlinear sliding surface s2, the expression of s2 is: ； Based on the total disturbance estimate z of the second regulating valve control loop 23 and the second nonlinear sliding surface s2, to obtain the second controller output u 02 ,u 02 The expression is: ； Based on the second controller output u 02 and the second regulating valve control input gain b F20 , generating the second regulating valve control input u F2 ,u F2 The expression is: ； in, is the second preset time function, is the second nonlinear parameter, , T c21 T is the second preset time for the error to converge to 0 after the state reaches the sliding surface. c22 The state reaches the second preset time of the sliding surface, for The differential signal.

10. The adaptive coordinated control method for a large-diameter plunger valve of a large-scale air extraction test device according to claim 3 is characterized in that: The adaptive adjustment update strategy based on the control valve angle / flow model guidance includes a first control valve adaptive adjustment update strategy and a second control valve adaptive adjustment update strategy. The first control valve adaptive adjustment update strategy includes: The flow calculation model of the first regulating valve is established, and the model expression is: ； A preliminary update is performed based on the first regulating valve flow calculation model. The preliminary update expression is: ； Based on the preliminary updated expression, the first regulating valve control input gain b is formed F10 The first adaptive update strategy is to adjust the b in the first nonlinear sliding mode fixed time extended state observer and the first nonlinear fast convergence stabilization controller in real time. F10 Parameters, the first adaptive update strategy expression is; ； The second regulating valve adaptive adjustment update strategy includes: The flow calculation model of the second regulating valve is established, and the model expression is: ； A preliminary update is performed based on the flow calculation model of the second regulating valve. The preliminary update expression is: ； Based on the preliminary updated expression, the second regulating valve control input gain b is formed F20 The second adaptive update strategy is to adjust the b in the second nonlinear sliding mode fixed-time extended state observer and the second nonlinear fast convergence stabilization controller in real time. F20 Parameters, the second adaptive update strategy expression is; ； in, is the flow coefficient of the first regulating valve, A 01 is the effective flow area of ​​the first regulating valve, To adjust the density before the valve, is the difference between the pressure before and after the regulating valve, kb1 is the first regulating coefficient, b F10cal is the preliminary calculated value of the control input gain of the first regulating valve, b min1 for b F10 Parameter lower limit, b max1 for b F10 Parameter upper limit value, is the flow coefficient of the second regulating valve, A 02 is the effective flow area of ​​the second regulating valve, kb2 is the second regulating coefficient, b F20cal is the preliminary calculated value of the second regulating valve control input gain, b min2 for b F20 Parameter lower limit, b max2 for b F20 Upper limit of the parameter.

Images