High-altitude simulation cabin exhaust system pressure control method and system based on MPC-ESO-CBF

Abstract

The invention provides a high-altitude simulation cabin exhaust system pressure control method and system based on MPC-ESO-CBF. The high-altitude simulation cabin exhaust system pressure control method based on MPC-ESO-CBF comprises the following steps that 1, main equipment and process dynamic characteristic modeling is carried out; step 2, an MPC-ESO-CBF controller is designed; 3, dynamically adjusting an execution mechanism; and 4, performing closed-loop optimization and dynamic compensation. By applying the technical scheme, the problems of dynamic optimization control, flow change interference and safe operation constraint guarantee in exhaust pressure control can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of complex industrial process control, and in particular to a method and system for controlling the exhaust system pressure of a high-altitude simulation cabin based on MPC-ESO-CBF. Background Art

[0002] As a key device for aircraft engine testing, the exhaust pressure control system of the high-altitude environmental simulation chamber faces three major technical challenges: 1) The complex flow field characteristics inside the exhaust diffuser, including the sudden expansion flow at the bottom, the shock wave effect and the flow interference caused by it, all of which cause the system to exhibit significant nonlinear dynamic characteristics; 2) The drastic changes in exhaust flow caused by the rapid switching of engine operating conditions will produce strong transient disturbances; 3) The system operation must strictly meet the dual requirements of dynamic response performance and safety constraints.

[0003] Traditional control solutions have the following technical bottlenecks:

[0004] 1. Insufficient dynamic performance: The transition state adjustment time is long, making it difficult to cope with rapid flow changes

[0005] 2. Insufficient disturbance suppression: limited ability to compensate for nonlinear disturbances such as shock wave oscillations in the exhaust diffuser, turbulent mixing, and engine flow changes

[0006] 3. Lack of safety constraint mechanism: The valve control amount may exceed the safety range, exacerbating mechanical wear

[0007] In summary, traditional PID control and linear active disturbance rejection control (LADRC) have difficulty in balancing dynamic performance and safety constraints, leading to problems such as long adjustment time and large fluctuations. Summary of the Invention

[0008] In view of this, the purpose of the present invention is to provide a high-altitude simulation cabin exhaust system pressure control method and system based on MPC-ESO-CBF to solve the problems of dynamic optimization control, flow change interference and safe operation constraint guarantee in exhaust pressure control.

[0009] To achieve the above objectives, the present invention adopts the following technical solution: a high-altitude simulation cabin exhaust system pressure control method based on MPC-ESO-CBF, comprising the following steps:

[0010] Step 1: Modeling of main equipment and process dynamic characteristics;

[0011] Step 2: Design the MPC-ESO-CBF controller.

[0012] Step 3: Dynamically adjust the actuator;

[0013] Step 4: Closed-loop optimization and dynamic compensation.

[0014] In a preferred embodiment, step 1 includes the following steps:

[0015] Step 11: Control valve position closed-loop control model

[0016] The closed-loop control model of the regulating valve position is essentially a third-order system. The closed-loop control model of the regulating valve position is equivalently simplified to a first-order inertia link, that is,

[0017]

[0018] Where: F is the opening of the regulating valve, K F is the proportional coefficient, T θ is the time constant, u is the exhaust pressure control input, different types of valves K F 、T θ different;

[0019] Step 12: Control valve flow characteristic model

[0020] The regulating valve is the final actuator of the aircraft engine exhaust environmental pressure control system; the mass flow rate of the fluid medium flowing through the regulating valve is shown in formula (2).

[0021]

[0022] Where: u' is the flow contraction coefficient, which is equal to the ratio of the minimum cross-sectional area of ​​the flow contraction to the cross-sectional area of ​​the throttle hole, m is the ratio of the cross-sectional area of ​​the throttle hole to the cross-sectional area of ​​the pipe, p1 and p2 are the pressure before and after the regulating valve, k is the gas adiabatic index, ρ1 is the gas density before the regulating valve, A is the effective cross-sectional area of ​​the regulating valve, Q m is the mass flow of the regulating valve; simplify the flow formula (2) and take the first two terms of formula (2) as It is called the flow coefficient of the regulating valve, and the simplified flow formula is obtained;

[0023]

[0024] Step 13: Exhaust Diffuser Flow Characteristics Model

[0025] The exhaust diffuser flow characteristics are considered as an external disturbance link f with partial known information. diffuser ;

[0026] Step 14: Pipe cavity dynamic characteristics model

[0027] The differential equation for the pressure in the exhaust pipe cavity is:

[0028]

[0029] Where: T is the temperature of the pipe cavity, p is the pressure of the pipe cavity, V is the volume of the pipe cavity, W in1 and W in2 are the first and second intake air flows, respectively, W out is the exhaust flow rate, H in1 and H in2 are the enthalpy of the first and second intake air, respectively, H out is the exhaust enthalpy, c p is the constant pressure specific heat capacity of the gas, C in1 is the flow rate of the guide gate after the first mixer inlet valve, C in2 is the flow rate of the guide gate after the inlet valve of the second mixer, is the heat exchanged between the pipe cavity and the outside world per unit time, R is the gas constant; the cavity heat change and the exhaust diffuser disturbance characteristics are regarded as the external disturbance link with partially known information f diffuser , then formula (4) has:

[0030]

[0031] Step 15: Test the engine air flow characteristic model

[0032] The simple air flow characteristic model of the test engine is expressed as

[0033]

[0034] Where: W ahs is the engine equivalent air flow, n c1 is the converted speed of the engine fan, p' is the engine intake pressure, T' is the engine intake temperature, H is the flight altitude, Ma is the flight Mach number, and n is the engine fan speed; f(*) represents n cl Related to H,Ma,n parameters;

[0035] Step 16: Controlled plant model

[0036] Combining the above main equipment characteristics and process characteristics, the exhaust environment pressure control object model is the following second-order differential equation

[0037]

[0038] Where: u is the exhaust environment pressure control input, y is the exhaust environment controlled pressure, d is the total disturbance of the system, a1 and a2 are model parameters, and b is the exhaust environment pressure control input gain;

[0039]

[0040] In a preferred embodiment, step 2 includes the following steps:

[0041] Step 21: Design the extended state observer

[0042] Expanding the total disturbance of the system into a new state variable, the state equation of system (7) is expressed as

[0043]

[0044] Where: x1 is the controlled pressure, x2 is the derivative of the controlled pressure, and x3 is the system disturbance. A linear extended state observer LESO is established for the system, and then

[0045]

[0046] β1, β2, β3 are observer gains, and the linear extended state observer LESO realizes the real-time estimation of each state variable of the system; at this time, let

[0047]

[0048] Then, system (7) is simplified to

[0049]

[0050] Step 22, MPC controller design

[0051] The simplified exhaust system formula (12) is expressed as the state equation:

[0052]

[0053] in:

[0054]

[0055] The zero-order hold method is used to discretize the above equation (13) into

[0056] x [k+1] =Ax [k] +Bu [k] (15)

[0057] Where: Where T s is the sampling period.

[0058] Define the incremental input as

[0059]

[0060] Substituting the above formula into formula (15) we get

[0061] x [k+1] =Ax [k] +BΔu [k] +Bu[k-1] (17)

[0062] At this time, the following optimization problem calculation system input is designed

[0063]

[0064] in:

[0065]

[0066] Where: N p is the prediction step length; e [k ] is the state error vector; Δu [k ] is the increment of the control input; S, Q, and R represent the weight matrices of the system’s terminal cost, operating cost, and control cost, respectively;

[0067] Step 23: CBF constraint introduction

[0068] Therefore, the input increment Δu is limited to the range of [-0.5, 0.5], taking into account the dynamic response capability and control performance of the actuator, thus improving the operational reliability while ensuring the stability of the system;

[0069] The control barrier function is designed to be

[0070] h(Δu(k))=0.25-Δu(k) 2 (20)

[0071] The time derivative of the control barrier function (20) should satisfy

[0072]

[0073] Among them: α>0, is the design parameter

[0074] Since the input increment Δu is a discrete time variable, the differential form is used instead of the derivative; the CBF constraint is written as

[0075] h(Δu(k+1))-h(Δu(k))+αh(Δu(k))≥0 (22)

[0076] Substituting the control barrier function (20) into the above formula (22), we get

[0077] 0.25-Δu(k+1) 2 -(0.25-Δu(k) 2 )+α(0.25-Δu(k) 2 )≥0 (23)

[0078] After simplification, we get

[0079] Δu(k)2 -Δu(k+1) 2 +α(0.25-Δu(k) 2 )≥0 (24)

[0080] Further sorting, we get the final CBF constraint

[0081] Δu(k+1) 2 ≤(1+α)Δu(k) 2 -0.25α (25).

[0082] In a preferred embodiment, step 3 includes the following steps: the optimized control instruction acts on the actuator module and drives the valve model formula (1) to adjust the valve opening, and the actual gas flow is calculated through the flow model formula (2), and the change in gas volume acts on the pipeline cavity model formula (5); the thermodynamic state of the cavity changes under the drive of the control instruction, and after a series of dynamic adjustment processes, the actual pressure value is finally output stably.

[0083] In a preferred embodiment, step 4 includes the following steps: The output of the generalized controlled object is immediately transmitted back to the observation module and the MPC controller module, establishing a closed-loop control architecture. The model predictive control module dynamically adjusts the control parameters using a rolling optimization algorithm and disturbance compensation signals, ensuring high-precision target tracking despite valve response delays and internal and external interference.

[0084] The present invention also provides a high-altitude simulation cabin exhaust system pressure control system based on MPC-ESO-CBF, which adopts the high-altitude simulation cabin exhaust system pressure control method based on MPC-ESO-CBF, comprising:

[0085] Signal input and observation module: contains the pressure set value and pressure observation value, which are input into the MPC controller module with CBF constraints and the extended state observer module respectively;

[0086] Control decision module: The CBF-constrained MPC controller module is based on relevant theories and methods, and combines with the system to roll-optimize the control quantity trajectory in the future time domain. It generates the initial control quantity u0 according to the deviation between the pressure set value and the pressure observation value. Then, it combines the ESO disturbance estimate value to realize the dynamic correction of the control law to further obtain the control quantity u, so as to realize the control decision of the system.

[0087] Actuator and physical model: The control variable u drives the actuator to adjust the opening F. This opening is combined with the flow model to calculate the actual flow, which ultimately acts on the exhaust ambient pressure control system and outputs the actual pressure.

[0088] Disturbance Observation and Compensation Module: The Extended State Observer (ESO) module estimates system disturbances in real time and feeds the estimated disturbances back into the control loop to improve the system's anti-disturbance performance. The ESO estimates and compensates for possible disturbances, such as changes in internal system parameters and external unknown disturbances.

[0089] Compared with the prior art, the present invention has the following beneficial effects:

[0090] 1. Dynamic performance optimization: Compared with the traditional linear active disturbance rejection controller (LADRC), in the transient state test of thrust transient, the exhaust pressure control system has a faster response, better dynamic adjustment, significantly shortened adjustment time, significantly reduced instantaneous fluctuations, and can quickly stabilize at the target pressure value.

[0091] 2. Improved anti-interference capability: The synergistic effect of ESO and MPC significantly improves the system's disturbance suppression capability and enhances system robustness.

[0092] 3. Safe operation guarantee: Safety constraints are introduced through CBF to ensure that the system status is always in a safe range, effectively preventing system instability or equipment damage caused by excessive changes in control input.

[0093] 4. Engineering Applicability: The control architecture has excellent adaptability and can flexibly respond to sudden flow changes and pressure fluctuations in the high-altitude cabin exhaust pressure control system under different flight conditions, improving the system's reliability and control quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] Figure 1 This is a diagram of the overall control architecture of the system according to the preferred embodiment of the present invention;

[0095] Figure 2 This is a diagram showing the pressure control effect of a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0096] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0097] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present application belongs.

[0098] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application; as used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form, and it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or their combinations.

[0099] A high altitude simulation cabin exhaust system pressure control system based on MPC-ESO-CBF, reference Figure 1-2 , including the following core modules:

[0100] Signal input and observation module: The left side contains the pressure set value and pressure observation value, which are input into the MPC controller (including CBF constraints) module and the extended state observer module respectively.

[0101] Control decision module: The MPC controller (including CBF constraints) module is based on relevant theories and methods, and combined with the system nominal model, to rollingly optimize the control quantity trajectory in the future time domain, generate the initial control quantity u0 according to the deviation between the pressure set value and the pressure observation value, and then combine the ESO disturbance estimation value to realize the dynamic correction of the control law to further obtain the control quantity u, so as to realize the control decision of the system.

[0102] Actuator and physical model: The control quantity u drives the actuator to adjust the opening F. This opening is combined with the flow model to calculate the actual flow, which ultimately acts on the exhaust ambient pressure control system and finally outputs the actual pressure.

[0103] Disturbance Observation and Compensation: The Extended State Observer (ESO) module estimates system disturbances in real time and feeds the estimated disturbances back into the control loop to improve the system's anti-disturbance performance. The ESO estimates and compensates for possible disturbances, such as changes in internal system parameters and external unknown disturbances.

[0104] Figure 2 The arrow in the middle indicates the signal flow, the dotted box marks the generalized controlled object (including the actuator and the exhaust ambient pressure control system), and the solid box is the controller and related control quantity generation module. The overall architecture intuitively reflects the MPC-based control optimization, disturbance suppression using ESO, and a collaborative control mechanism considering CBF constraints.

[0105] Figure 2 The superiority of the control strategy of the present invention is verified by comparing the time domain response characteristics, including the following technical information:

[0106] 1. Dynamic response analysis

[0107] The MPC-ESO-CBF composite control strategy allows the system curve to approach steady state more quickly during response, significantly limiting overshoot (compared to the traditional LADRC control strategy, the adjustment time is shortened by approximately 38.7% and the maximum instantaneous fluctuation is reduced by approximately 33.7%), allowing for rapid stabilization at the set pressure value. In contrast, the traditional LADRC control strategy exhibits significant overshoot and significantly prolongs the time required to reach stability.

[0108] 2. Comparison of Steady-State Anti-interference Capability

[0109] In terms of system steady-state performance, the MPC-ESO-CBF composite control strategy maintains a low steady-state average error (compared to the traditional LADRC control strategy, the average error is reduced by approximately 49.1%), highlighting its excellent steady-state control capability. The traditional LADRC control strategy has a relatively high steady-state average error and inferior steady-state performance.

[0110] Figure 2 The present invention intuitively reveals that the use of the MPC-ESO-CBF composite controller significantly improves the control system's dynamic response characteristics, steady-state control accuracy, and anti-interference performance. In particular, the control scheme demonstrates significant performance advantages when faced with complex operating conditions and external disturbances, with its rapid response capability, precise tracking characteristics, and robust stability fully demonstrated. A high-altitude simulation cabin exhaust system pressure control method based on MPC-ESO-CBF is as follows:

[0111] 1. Working condition deployment: Based on a typical engine thrust transient test mission, the flight Mach number, flight altitude, throttle lever thrust and other conditions are set in the exhaust pressure control system of the high-altitude simulation chamber, and the algorithm is run on this basis.

[0112] 2. Parameter adjustment:

[0113] (1)MPC controller module: Configure the prediction step size to 20 and the weight matrix setting

[0114]

[0115] (2) ESO module: Set the observer bandwidth ω0 to 10;

[0116] (3) CBF constraint module: Considering that the valve opening range is 0–90 degrees, Δu is limited to the range of [-0.5, 0.5] to avoid mechanical wear and system oscillation caused by large adjustments. At the same time, the dynamic response capability and control performance of the actuator are taken into account, thereby improving operational reliability while ensuring system stability.

[0117] 3. Online update: Get sensor data in real time and update the control output every 20ms.

[0118] The working steps of the present invention are as follows:

[0119] Step 1: Modeling of key equipment and process dynamic characteristics

[0120] Step 11: Control valve position closed-loop control model

[0121] The closed-loop control model of the regulating valve position is essentially a third-order system. With the help of appropriate control methods, the closed-loop control model of the regulating valve position can be equivalently simplified to a first-order inertia link, that is,

[0122]

[0123] Where: F is the opening of the regulating valve, K F is the proportional coefficient, T θ is the time constant, u is the control input, and K for different types of valves F 、T θ different.

[0124] Step 12: Control valve flow characteristic model

[0125] The regulating valve is the final actuator of the aircraft engine exhaust pressure control system. The mass flow rate of the fluid medium flowing through the regulating valve is shown in formula (2).

[0126]

[0127] Where: u is the flow contraction coefficient, which is equal to the ratio of the minimum cross-sectional area of ​​the flow contraction to the cross-sectional area of ​​the throttle hole, m is the ratio of the cross-sectional area of ​​the throttle hole to the cross-sectional area of ​​the pipe, p1 and p2 are the pressure before and after the regulating valve, k is the gas adiabatic index, ρ1 is the gas density before the regulating valve, A is the effective cross-sectional area of ​​the regulating valve, Q m is the mass flow of the regulating valve. The flow formula (2) can be simplified by taking the first two terms of formula (2) as It is called the flow coefficient of the control valve, and the simplified flow formula is obtained.

[0128]

[0129] Step 13: Exhaust Diffuser Flow Characteristics Model

[0130] The flow characteristics of the exhaust diffuser are complex. In actual experiments, its ejection coefficient, diffuser efficiency, and compression ratio will change significantly, making it difficult to establish an accurate model that meets the actual engineering requirements. To facilitate the control system simulation study, the exhaust diffuser flow characteristics are regarded as an external disturbance link with partially known information (denoted as f diffuser ).

[0131] Step 14: Pipe cavity dynamic characteristics model

[0132] The differential equation for the pressure in the exhaust pipe cavity is:

[0133]

[0134] Where: T is the temperature of the pipe cavity, p is the pressure of the pipe cavity, V is the volume of the pipe cavity, W in1 and W in2 are the first and second intake air flows, respectively, W out is the exhaust flow rate, H in1 and H in2 are the enthalpy of the first and second intake air, respectively, H out is the exhaust enthalpy, c p is the constant pressure specific heat capacity of the gas, C in1 C is the flow rate of the guide gate after the mixer inlet valve 1, in2 is the flow rate of the guide gate after the mixer inlet valve 2, is the heat exchanged between the pipe cavity and the outside world per unit time, and R is the gas constant. To facilitate the simulation study of the control system, the cavity heat change and the exhaust diffuser disturbance characteristics are regarded as external disturbance links with partially known information (denoted as f diffuser ), then formula (4) has.

[0135]

[0136] Step 15: Test the engine air flow characteristic model

[0137] The simple air flow characteristic model of the test engine can be expressed as

[0138]

[0139] Where: W ahs is the engine equivalent air flow, n c1 is the converted speed of the engine fan, p is the engine intake pressure, T is the engine intake temperature, H is the flight altitude, Ma is the flight Mach number, and n is the engine fan speed.

[0140] Step 16: Controlled plant model

[0141] Combining the above main equipment characteristics and process characteristics, the exhaust environment pressure control object model is the following second-order differential equation

[0142]

[0143] Where: u is the exhaust environment pressure control input, y is the exhaust environment controlled pressure, d is the total disturbance of the system, a1 and a2 are model parameters, and b is the exhaust environment pressure control input gain.

[0144]

[0145] Step 2: MPC-ESO-CBF controller design

[0146] Step 21, design of extended state observer

[0147] Expanding the total disturbance of the system into a new state variable x3, the state equation of system (7) can be expressed as

[0148]

[0149] Where: x1 is the controlled pressure, x2 is the derivative of the controlled pressure, and x3 is the system disturbance. A linear extended state observer (LESO) is established for the system, and we have

[0150]

[0151] As long as the observer gains β1, β2, and β3 are selected appropriately, LESO can achieve real-time estimation of each state variable of the system.

[0152]

[0153] Therefore, system (7) can be simplified to

[0154]

[0155] Step 22, MPC controller design

[0156] The simplified exhaust system formula (12) is expressed as the state equation:

[0157]

[0158] in:

[0159]

[0160] The zero-order hold method is used to discretize the above equation (13) into

[0161] x [k+1] =Ax [k] +Bu [k] (15)

[0162] Where: Where Ts is the sampling period.

[0163] Define the incremental input as

[0164]

[0165] Substituting the above formula into formula (15) we can get

[0166] x [k+1] =Ax [k] +BΔu [k] +Bu [k-1] (17)

[0167] At this time, the following optimization problem calculation system input is designed

[0168]

[0169] in:

[0170]

[0171] Where: N p is the prediction step length; e [k] is the state error vector; Δu [k] is the increment of the control input; S, Q, and R represent the weight matrices of the system's terminal cost, operating cost, and control cost, respectively.

[0172] Step 23: CBF constraint introduction

[0173] The control barrier function (CBF) is introduced to ensure valve jitter remains within a safe range, preventing system instability or equipment damage caused by large changes in control input. Valve jitter is directly related to the input increment Δu, and the magnitude of Δu affects the frequency and amplitude of valve operation. Given the valve opening range of 0–90 degrees, Δu is limited to the range of [-0.5, 0.5] to avoid mechanical wear and system oscillation caused by large adjustments. This also takes into account the actuator's dynamic response and control performance, ensuring system stability while improving operational reliability.

[0174] The control barrier function is designed to be

[0175] h(Δu(k))=0.25-Δu(k) 2 (20)

[0176] In order to ensure that the control input increment Δu is always within the safe range, the time derivative of the control barrier function (20) should satisfy

[0177]

[0178] Among them: α>0, is the design parameter

[0179] Since Δu is a discrete time variable, the differential form is used instead of the derivative. The CBF constraint is written as

[0180] h(Δu(k+1))-h(Δu(k))+αh(Δu(k))≥0 (22)

[0181] Substituting the control barrier function (20) into the above formula (22), we get

[0182] 0.25-Δu(k+1) 2 -(0.25-Δu(k) 2 )+α(0.25-Δu(k) 2 )≥0 (23)

[0183] After simplification, we get

[0184] Δu(k) 2 -Δu(k+1) 2 +α(0.25-Δu(k) 2 )≥0 (24)

[0185] Further sorting, we get the final CBF constraint

[0186] Δu(k+1) 2 ≤(1+α)Δu(k) 2 -0.25α (25)

[0187] Step 3: Dynamic adjustment of actuators

[0188] The optimized control instructions (processed by MPC-ESO-CBF) act on the actuator module and drive the valve model (1) to adjust the valve opening. The actual gas flow is calculated through the flow model (2). This gas flow change acts on the pipeline cavity model (5). The thermodynamic state of the cavity changes under the control instruction, and after a series of dynamic adjustment processes, the actual pressure value is finally output stably.

[0189] Step 4: Closed-loop optimization and dynamic compensation

[0190] The output of the generalized controlled object (including the actuator and exhaust ambient pressure control system) is instantly fed back to the observation module and the MPC controller module, establishing a closed-loop control architecture. The model predictive control module leverages a rolling optimization algorithm and, in conjunction with disturbance compensation signals, dynamically adjusts control parameters to ensure high-precision target tracking despite valve response delays and internal and external interference.

Claims

1. A high altitude simulation cabin exhaust system pressure control method based on MPC-ESO-CBF, characterized in that: The following steps are involved: Step 1: Modeling of main equipment and process dynamic characteristics; Step 2: Design the MPC-ESO-CBF controller. Step 3: Dynamically adjust the actuator; Step 4: Closed-loop optimization and dynamic compensation.

2. The high altitude simulation cabin exhaust system pressure control method based on MPC-ESO-CBF according to claim 1 is characterized in that: The step 1 comprises the following steps: Step 11: Control valve position closed-loop control model The closed-loop control model of the regulating valve position is essentially a third-order system. The closed-loop control model of the regulating valve position is equivalently simplified to a first-order inertia link, that is, Where: F is the opening of the regulating valve, K F is the proportional coefficient, T θ is the time constant, u is the exhaust pressure control input, different types of valves K F 、T θ different; Step 12: Control valve flow characteristic model The regulating valve is the final actuator of the aircraft engine exhaust environmental pressure control system; the mass flow rate of the fluid medium flowing through the regulating valve is shown in formula (2). Where: u' is the flow contraction coefficient, which is equal to the ratio of the minimum cross-sectional area of ​​the flow contraction to the cross-sectional area of ​​the throttle hole, m is the ratio of the cross-sectional area of ​​the throttle hole to the cross-sectional area of ​​the pipe, p1 and p2 are the pressure before and after the regulating valve, k is the gas adiabatic index, ρ1 is the gas density before the regulating valve, A0 is the effective cross-sectional area of ​​the regulating valve, Q m is the mass flow of the regulating valve; simplify the flow formula (2) and take the first two terms of formula (2) as It is called the flow coefficient of the regulating valve, and the simplified flow formula is obtained; Step 13: Exhaust Diffuser Flow Characteristics Model The exhaust diffuser flow characteristics are considered as an external disturbance link f with partial known information. diffuser ; Step 14: Pipe cavity dynamic characteristics model The differential equation for the pressure in the exhaust pipe cavity is: Where: T is the temperature of the pipe cavity, p is the pressure of the pipe cavity, V is the volume of the pipe cavity, W in1 and W in2 are the first and second intake air flows, respectively, W out is the exhaust flow rate, H in1 and H in2 are the enthalpy of the first and second intake air, respectively, H out is the exhaust enthalpy, c p is the constant pressure specific heat capacity of the gas, C in1 is the flow rate of the guide gate after the first mixer inlet valve, C in2 is the flow rate of the guide gate after the inlet valve of the second mixer, is the heat exchanged between the pipe cavity and the outside world per unit time, R is the gas constant; the cavity heat change and the exhaust diffuser disturbance characteristics are regarded as the external disturbance link with partially known information f diffuser , then formula (4) has: Step 15: Test the engine air flow characteristic model The simple air flow characteristic model of the test engine is expressed as Where: W ahs is the engine equivalent air flow, n c1 is the converted speed of the engine fan, p' j is the engine intake pressure, T' is the engine intake temperature, H is the flight altitude, Ma is the flight Mach number, and n is the engine fan speed; f(*) represents n cl Related to H,Ma,n parameters; Step 16: Controlled plant model Combining the above main equipment characteristics and process characteristics, the exhaust environment pressure control object model is the following second-order differential equation Where: u is the exhaust environment pressure control input, y is the exhaust environment controlled pressure, d is the total disturbance of the system, a1 and a2 are model parameters, and b is the exhaust environment pressure control input gain; 3. The high altitude simulation cabin exhaust system pressure control method based on MPC-ESO-CBF according to claim 1 is characterized in that: The step 2 comprises the following steps: Step 21: Design the extended state observer Expanding the total disturbance of the system into a new state variable, the state equation of system (7) is expressed as Where: x1 is the controlled pressure, x2 is the derivative of the controlled pressure, and x3 is the system disturbance. A linear extended state observer LESO is established for the system, and then β1, β2, β3 are observer gains, and the linear extended state observer LESO realizes the real-time estimation of each state variable of the system; at this time, let Then, system (7) is simplified to Step 22, MPC controller design The simplified exhaust system formula (12) is expressed as the state equation: in: The zero-order hold method is used to discretize the above equation (13) into x [k+1] =Ax [k] +Bu [k] (15) Where: Where T s is the sampling period. Define the incremental input as Substituting the above formula into formula (15) we get x [k+1] =Ax [k] +BΔu [k] +Bu [k-1] (17) At this time, the following optimization problem calculation system input is designed in: Where: N p is the prediction step length; e k ] is the state error vector; Δu [k ] is the increment of the control input; S, Q, and R represent the weight matrices of the system’s terminal cost, operating cost, and control cost, respectively; Step 23: CBF constraint introduction Therefore, the input increment Δu is limited to the range of [-0.5, 0.5], taking into account the dynamic response capability and control performance of the actuator, thus improving the operational reliability while ensuring the stability of the system; The control barrier function is designed to be h(Δu(k))=0.25-Δu(k) 2 (20) The time derivative of the control barrier function (20) should satisfy Among them: α>0, is the design parameter Since the input increment Δu is a discrete time variable, the differential form is used instead of the derivative; the CBF constraint is written as h(Δu(k+1))-h(Δu(k))+αh(Δu(k))≥0 (22) Substituting the control barrier function (20) into the above formula (22), we get 0.25-Δu(k+1) 2 -(0.25-Δu(k) 2 )+α(0.25-Δu(k) 2 )≥0 (23) After simplification, we get Δu(k) 2 -Δu(k+1) 2 +α(0.25-Δu(k) 2 )≥0 (24) Further sorting, we get the final CBF constraint Δu(k+1) 2 ≤(1+α)Δu(k) 2 -0.25a (25).

4. The high altitude simulation cabin exhaust system pressure control method based on MPC-ESO-CBF according to claim 1 is characterized in that: The step 3 includes the following steps: the optimized control instruction acts on the actuator module and drives the valve model formula (1) to adjust the valve opening, the actual gas flow is calculated through the flow model formula (2), and the change in gas volume acts on the pipeline cavity model formula (5); the thermodynamic state of the cavity changes under the drive of the control instruction, and after a series of dynamic adjustment processes, the actual pressure value is finally output stably.

5. The high altitude simulation cabin exhaust system pressure control method based on MPC-ESO-CBF according to claim 1 is characterized in that: Step 4 includes the following steps: The output of the generalized controlled object is immediately transmitted back to the observation module and the MPC controller module, establishing a closed-loop control architecture. The model predictive control module uses a rolling optimization algorithm and coordinates the disturbance compensation signal to dynamically modify the control parameters, ensuring high-precision target value tracking despite valve response delays and internal and external interference factors.

6. A high altitude simulation cabin exhaust system pressure control system based on MPC-ESO-CBF, characterized by A method for controlling the exhaust system pressure of a high-altitude simulation cabin based on MPC-ESO-CBF according to any one of claims 1 to 5 is adopted, comprising: Signal input and observation module: contains the pressure set value and pressure observation value, which are input into the MPC controller module with CBF constraints and the extended state observer module respectively; Control decision module: The CBF-constrained MPC controller module is based on relevant theories and methods, and combines with the system to roll-optimize the control quantity trajectory in the future time domain. It generates the initial control quantity u0 according to the deviation between the pressure set value and the pressure observation value. Then, it combines the ESO disturbance estimate value to realize the dynamic correction of the control law to further obtain the control quantity u, so as to realize the control decision of the system. Actuator and physical model: The control variable u drives the actuator to adjust the opening F. This opening is combined with the flow model to calculate the actual flow, which ultimately acts on the exhaust ambient pressure control system and outputs the actual pressure. Disturbance Observation and Compensation Module: The Extended State Observer (ESO) module estimates system disturbances in real time and feeds the estimated disturbances back into the control loop to improve the system's anti-disturbance performance. The ESO estimates and compensates for possible disturbances, such as changes in internal system parameters and external unknown disturbances.

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