Clean workshop regulation and control system based on multi-target multi-level model predictive control

Abstract

The invention relates to a clean workshop room pressure intelligent control system and a control method, and aims to realize accurate adjustment of room pressure and flow in a clean workshop and ensure the stability of room pressure difference through an automatic state space model and a multi-level model prediction control algorithm. The control system comprises a data processing module, a modeling prediction module, a control strategy selection module, a real-time feedback adjustment module, a prediction optimization distribution module, a control signal output module, an actual execution module and a physical signal feedback module. The control strategy selection module comprises a room pressure control module and an air valve flow control module which are respectively used for generating global and fine control signals based on constrained MPC and nonlinear MPC algorithms; the control signal output module transmits an optimization signal to the actual execution module to adjust the opening degree of an air supply valve and an air return and exhaust valve so as to achieve accurate pressure and flow control. The system provided by the invention has high precision and quick response capability, and meets high requirements of a clean workshop.

Description

Technical Field

[0001] The invention relates to an automatic control technology for a clean workshop, in particular to an intelligent control method for clean workshop pressure based on a multi-objective hierarchical model predictive control (MPC) algorithm. Background Art

[0002] Cleanrooms mostly use the traditional hierarchical control method, in which pressure control is usually divided into different areas or room levels, with the upper layer controlling the overall pressure difference of each room, and further controlling local equipment in the room to achieve precise pressure regulation. Although this traditional hierarchical control scheme can meet the clean requirements to a certain extent, it has obvious limitations when facing multi-variable coupling and nonlinear systems. For example, in a complex environment where multiple rooms influence each other, the response speed and adjustment accuracy of traditional hierarchical control are low, making it difficult to ensure the real-time stability of room pressure.

[0003] Clean room pressure control systems often involve the coupling of multiple variables, such as the pressure in the room, the opening of the air valve, and the air volume. In particular, there is a nonlinear relationship between the opening of the air valve and the air volume, which makes it difficult for traditional linear control methods to achieve precise control within the full opening range. In addition, clean rooms may be affected by changes in the external environment or other room coupling effects, requiring the control system to have adaptive capabilities to cope with dynamic changes. This complex nonlinear dynamic response makes it difficult for traditional PID or linear MPC control to adapt effectively, resulting in a decrease in the control accuracy of the system.

[0004] In recent years, model predictive control (MPC) has been introduced into the pressure control of cleanrooms to improve control accuracy and response speed. However, existing MPC methods mostly adopt a single-level control strategy in practical applications, that is, using constrained optimal control MPC in the overall control of room pressure, but often using simple linear methods in specific air valve control, failing to fully consider the nonlinear relationship between air valves and flow. This single-level MPC control scheme exhibits obvious control defects when facing nonlinear actuators, and it is difficult to achieve precise regulation of pressure and flow in a wide operating range. Summary of the invention

[0005] In order to solve the above problems, the clean room pressure control system urgently needs a multi-objective hierarchical control architecture to achieve the optimization of different control objectives. Specifically, it is necessary to use constrained MPC control at the upper level to ensure that the overall pressure of the room meets the clean requirements, and at the same time, perform nonlinear MPC control on the air valve opening and flow at the lower level to adapt to the characteristics of nonlinear actuators. This innovative multi-objective hierarchical MPC control architecture can not only provide higher response accuracy under multi-variable coupling conditions, but also ensure that the system maintains pressure stability in the clean environment during dynamic changes.

[0006] The present invention provides an intelligent control system for clean room pressure, which realizes precise control of pressure and flow in the clean room through a multi-level model predictive control (MPC) algorithm combined with automatic modeling and dynamic updating of a state space model, thereby improving control accuracy and response speed while maintaining a stable pressure difference in the clean room, and meeting the strict requirements of a clean environment.

[0007] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0008] A clean room pressure intelligent control system includes a data processing module, a modeling prediction module, a control strategy selection module, a prediction optimization allocation module, a control signal output module, an actual execution module, a physical signal feedback module and a real-time feedback adjustment module, wherein:

[0009] The data processing module includes an initialization module and a data acquisition module; the initialization module is used for the initial setting of the system and the acquisition of benchmark data; wherein the benchmark data acquisition refers to the acquisition of the initial environmental parameters and external factors of the clean room when the system is started, the establishment of the initial state space model and the setting of the initial control parameters; the data acquisition module is used to collect the dynamic environmental data of the clean room in real time, including the supply air volume, return air volume and room pressure;

[0010] The modeling and prediction module is used to generate a state space model based on the data of the data processing module, and dynamically update the state space model through an adaptive algorithm;

[0011] The control strategy selection module includes a room pressure control module and an air valve flow control module; the room pressure control module generates a global control signal for regulating the overall pressure of the room based on a constrained model predictive control algorithm, and the control signal directly acts on the air supply valve execution module and the return and exhaust air valve execution module to achieve overall pressure difference control of the room; the air valve flow control module generates a refined control signal for individually adjusting the opening and flow of the air supply valve based on a nonlinear model predictive control algorithm to ensure accurate regulation of the air supply volume in the room, thereby optimizing the stability of the room pressure and flow at a micro level;

[0012] The prediction optimization allocation module is used to receive the control signal from the control strategy selection module, and dynamically allocate the control signal to different actuators after optimization;

[0013] The real-time feedback adjustment module is used to monitor the real-time feedback data in the prediction optimization allocation module and dynamically adjust the control signal of the control strategy selection module so that the system can quickly respond to changes in the external environment and realize the real-time flow of feedback data and model update;

[0014] The control signal output module is used to transmit the optimized and allocated control signal to the actual execution module;

[0015] The actual execution module includes an air supply valve execution module and a return and exhaust air valve execution module; the air supply valve execution module is used to receive the global control signal from the room pressure control module and the refined control signal from the air valve flow control module, adjust the opening of the air supply valve, and realize the air supply control of the clean workshop; the return and exhaust air valve execution module is used to receive the global control signal from the room pressure control module, adjust the opening of the return and exhaust air valve, and realize the control of the overall pressure difference of the room;

[0016] The physical signal feedback module is used to collect physical feedback data generated after execution, including pressure feedback and flow feedback, and send the feedback data to the data processing module to adjust the control strategy in real time.

[0017] The specific control steps of the room pressure control module and the air valve flow control module of the control strategy selection module are as follows:

[0018] The room pressure control module generates a global control signal based on the constrained MPC algorithm, including obtaining the deviation between the current room pressure and the target pressure value, calculating the pressure adjustment amount, generating a global control signal, and sending the signal to the air supply valve execution module and the return and exhaust valve execution module for adjusting the supply and exhaust air volumes;

[0019] The air valve flow control module generates a refined control signal based on the nonlinear MPC algorithm, including obtaining the current opening of the air supply valve and the actual flow data, calculating the deviation between the target air supply flow and the actual flow, generating a refined flow control signal, and sending the signal to the air supply valve execution module for adjusting the opening of the air supply valve to accurately control the air supply flow.

[0020] The state space model generated by the modeling and prediction module consists of the following two parts:

[0021] The room pressure control model is used to describe the dynamic characteristics of the overall room pressure and adjust the global control quantity of the supply air valve and the return air valve. The state vector, control input and output vector of the room pressure control model are defined as follows:

[0022] The state vector x1(t), including the room pressure value (p(t)) and the pressure change rate It is expressed as:

[0023]

[0024] The control input u1(t) includes the air volume of the air supply system (q s (t)) and the air volume of the return air system (q r(t)), which are used to adjust the positive and negative pressures of the room, respectively, and are expressed as:

[0025] u1(t)=[q s (t) q r (t)] T

[0026] The output vector y1(t) is the real-time pressure value p(t) of the room, which is used to compare with the target pressure p ref (t) comparison, expressed as:

[0027] y1(t)=p(t)

[0028] Equation of state:

[0029] x1(t+1)=A1x1(t)+B1u1(t)+w1(t)

[0030] Among them: A1 is the system matrix, which is used to describe the dynamic characteristics of room pressure and its gradient changes; B1 is the input matrix, which is used to describe the regulating effect of supply and return air on room pressure; w1(t) is the system noise, including the instantaneous pressure fluctuation caused by the opening of doors and windows and the ambient pressure gradient fluctuation caused by the start and stop of the equipment fan;

[0031] Output equation:

[0032] y1(t)=C1x1(t)+v1(t)

[0033] Where: C1 is the output matrix, which is used to extract the real-time pressure value of the room; v1(t) is the measurement noise, which is mainly the measurement error of the pressure sensor;

[0034] The air valve flow control model is used to describe the dynamic characteristics of the air supply valve. By finely adjusting the opening of the air supply valve, the air supply flow rate can be accurately controlled. The state vector, control input and output vector of the air valve flow control model are defined as follows:

[0035] The state vector x2(t), including the air volume q of the air supply system s (t) and the opening of the air supply valve θ s (t), expressed as:

[0036] x2(t)=[q s (t)θ s (t)] T

[0037] Control input u2(t), represents the adjustment amount Δθ of the air supply valve opening s (t), expressed as:

[0038] u2(t)=Δθ s (t)

[0039] Output vector y2(t), representing the actual air flow q at the current moment s (t), used to match the target air supply volume q s , ref (t) comparison, expressed as:

[0040] y2(t)=q s (t)

[0041] Equation of state:

[0042] x2(t+1)=A2x2(t)+B2u2(t)+w2(t)

[0043] Among them: A2 is the system matrix, which is used to describe the dynamic characteristics of the air supply valve; B2 is the input matrix, which is used to describe the regulating effect of the air supply valve opening on the air supply volume; w2(t) is the system noise, including nonlinear interference such as air supply duct vibration and ambient temperature and humidity fluctuations;

[0044] Output equation:

[0045] y2(t)=C2x2(t)+v2(t)

[0046] Among them: C2 is the output matrix, which is used to extract the air supply volume; v2(t) is the measurement noise, which is mainly the measurement error of the air volume sensor.

[0047] The steps for the room pressure control module to generate the global control signal based on the constrained MPC algorithm are:

[0048] Establish a prediction model with constrained MPC algorithm:

[0049] x1(t+k+1|t)=A1·x1(t+k|t)+B1·u1(t+k|t)+w1(t+k|t)

[0050] y1(t+j|t)=C1·x1(t+k|t)+v1(t+k|t)

[0051] Where x1(t+k+1|t) represents the predicted state of the system at time t+k+1 given the system state and control input known at the current time t, k = 0, 1, ..., N p -1, N p represents the number of time steps for future prediction; u1(t+k|t), y1(t+k|t), w1(t+k|t) and v1(t+k|t) represent the control input, control output, system noise and measurement error of the system at time t+k, respectively, when the system state and control input are known at the current time t;

[0052] The optimization objective function J is:

[0053]

[0054] Among them, J1 is the optimization objective function value, which is the target to be minimized. Its value reflects the control performance of the system in the prediction time domain, and includes two parts: the output error term and the weight term of the control input; y1(t+k|t) is the predicted system output room pressure value at t+k when the current time t is known; y 1,ref is the target pressure value; Q is the output error weight matrix, which is used to weigh the deviation between the target pressure and the actual pressure. It is a positive semidefinite matrix. The larger the weight value, the more important the error in this dimension is. Q is adjusted according to the requirements for pressure stability in system design; u1(t+k|t) is the control input vector, which means that when t is known, the control variable at time t+k is predicted, which is the supply air volume and return air volume; R is the control input weight matrix, which is used to limit the range of control input changes. It is a positive definite matrix. The larger the value, the stricter the restriction on signal changes.

[0055] The constraints include pressure range constraints, input amplitude constraints, and input change rate constraints:

[0056] Pressure range constraints:

[0057] p min ≤y1(t+k|t)≤p max

[0058] Among them, p min and p max is the allowable room pressure range, indicating the minimum and maximum values ​​respectively;

[0059] Input amplitude constraint:

[0060] q s,min ≤q s (t+k|t)≤q s,max

[0061] q r,min ≤q r (t+k|t)≤q r,max

[0062] Among them, q s,min is the minimum value of air supply volume; q s,max is the maximum value of air supply volume; q s (t+k|t) is the predicted state of air supply volume at time t+k based on time t; q r,min is the minimum value of the return air volume; q r,max is the maximum value of the return air volume; q r (t+k|t) is the predicted state of the return air volume at time t+k based on time t;

[0063] Input change rate constraint:

[0064] Δq s,min ≤q s (t+k|t)-q s (t+k-1|t)≤Δq s,max

[0065] Δq r,min ≤q r (t+k|t)-q r (t+k-1|t)≤Δq r,max

[0066] Among them, Δq s,min To predict the minimum difference in air volume between two moments; Δq s,max To predict the maximum difference in air volume between two moments; Δq r,min To predict the minimum difference in return air volume between two moments; Δq r,max To predict the maximum difference in return air volume between two moments.

[0067] The steps for the air valve flow control module to generate refined control signals based on the nonlinear MPC algorithm are:

[0068] Establish a prediction model for the nonlinear MPC algorithm:

[0069] x2(t+1|t)=f(x2(t),u2(t))

[0070] y2(t)=h(x2(t))

[0071] Among them, x2(t+1|t) represents the predicted state of the system at time t+1 under the condition of the system state and control input known at the current time t; f(x2(t),u2(t)) represents the nonlinear function of state transfer, and the specific form is obtained by actual physical modeling or actual data-driven method, x2(t) is the internal pressure value and pressure change rate of the system at the current time t, u2(t) is the control input vector of the system, including the supply air volume and return air volume; y2(t) is the output vector of the system, which represents the measured output value of the system at the current time t, including the room pressure value; h(x2(t)) represents the nonlinear relationship between output and state;

[0072] Optimize the objective function J f for:

[0073]

[0074] Where J2 is the optimization objective function value, and the air supply volume is the target to be minimized; y2(t+k|t) is the predicted air supply volume value at the current time t at the future time t+k; y 2,refis the target reference value of air supply volume;

[0075] Q is the output error weight matrix, which is used to weigh the deviation between the predicted value and the target value of the air supply volume; Δ is the change, Δu2(t+k|t) is the change value of the air supply volume at the predicted moment; R is the control input weight matrix, which is used to limit the adjustment range of the air supply volume.

[0076] Use Newton's method to directly optimize the objective function J2;

[0077] The Newton method sets the convergence threshold first, establishes the objective function gradient, and defines the objective function according to the objective function. Use the Hessian matrix to calculate, and finally update the formula of u according to the convergence condition;

[0078] Constraints include flow range constraints and control input range constraints:

[0079] Flow range constraints:

[0080] q s,min ≤q s (t+k|t)≤q s,max

[0081] Where: q s,min is the minimum value of air supply volume; q s,max is the maximum value of air supply volume; q s (t+k|t) is the predicted state of air supply volume at t+k based on time t;

[0082] Control input range constraints:

[0083] θ s,min ≤θ s (t+k|t)≤θ s,max

[0084] Where: θ s,min is the minimum opening of the air supply valve; θ s,max is the maximum opening of the air supply valve;

[0085] θ s (t+k|t) is the predicted state of the air supply valve opening at t+k based on time t.

[0086] The prediction optimization allocation module integrates the global and refined control signals to generate the final control command, which acts on the air supply valve and return and exhaust valve execution modules. The final air valve control signal u final (t) is:

[0087] u final (t)=u1(t)+u2(t)

[0088] Among them, u1(t) is the global control signal generated by the pressure control module, and u2(t) is the refined control signal output by the air valve flow control module.

[0089] The global control signal u1(t) generated by the pressure control module and the refined control signal u2(t) output by the air valve flow control module are respectively:

[0090]

[0091] Among them, q s (t) is the global air supply volume; q r (t) is the global return air volume;

[0092] u2(t)=Δθ s (t)

[0093] Among them, Δθ s (t) is the air supply valve opening adjustment amount.

[0094] The data processing module further includes an external factor input interface for collecting external interference factor data in the clean room environment, including temperature, humidity and other room pressure differences.

[0095] The present invention also discloses a clean room pressure intelligent control method, which is based on the clean room pressure intelligent control system provided above and includes the following steps:

[0096] Step 1: Initialize the system through the data processing module and collect the benchmark data of the clean room;

[0097] Step 2: Generate the initial state space model using the modeling prediction module, and obtain real-time data through the data acquisition module to update the model;

[0098] Step 3: The room pressure control module generates a global control signal based on the constrained MPC algorithm, which specifically includes: obtaining the deviation between the current room pressure and the target pressure, calculating the adjustment amount and generating a global control signal, and sending the signal to the supply air valve execution module and the return and exhaust air valve execution module;

[0099] Step 4: The air valve flow control module generates a refined control signal based on the nonlinear MPC algorithm, specifically including: obtaining the current opening and actual flow of the air supply valve, calculating the flow deviation and generating a refined control signal, and sending it to the air supply valve execution module;

[0100] Step 5: The prediction optimization allocation module optimizes the control signal and allocates it to the corresponding actuator;

[0101] Step 6: The control signal output module transmits the optimized control signal to the actual execution module;

[0102] Step 7: The air supply valve execution module and the return and exhaust air valve execution module adjust the valve opening according to the control signal to adjust the supply air volume and the return air volume;

[0103] Step 8: The physical signal feedback module collects the feedback data of the room pressure and flow after execution, and sends it to the real-time feedback adjustment module to adjust the control strategy according to the real-time feedback;

[0104] Step 9: Return to step 2 for loop control to achieve adaptive control of clean room pressure and flow.

[0105] The control signal output module is used to transmit the optimization signal generated by the prediction optimization allocation module to the actual execution module, so as to realize the effective output of the signal and the accurate issuance of the control instruction.

[0106] The actual execution module includes the air supply valve execution module and the return and exhaust air valve execution module, which receive control signals from the control strategy selection module and the prediction optimization allocation module respectively. The air supply valve execution module adjusts the opening of the air supply valve according to the control signal to control the air supply volume; the return and exhaust air valve execution module adjusts the opening of the return and exhaust air valve according to the control signal to adjust the return air volume to ensure that the overall pressure control target of the room is achieved.

[0107] The physical signal feedback module is used to collect the pressure feedback data and flow feedback data in the room, and feed these data back to the data processing module and the real-time feedback adjustment module so as to continuously adjust and optimize during the execution of the control strategy.

[0108] The significant effect of the present invention is that through the combination of automated state space modeling and multi-level MPC control strategy, the pressure and flow of clean room rooms can be finely adjusted, while improving the system control accuracy and response speed, ensuring the stability of room pressure difference and meeting the strict requirements of clean environment. Compared with traditional control systems, the present invention can adapt to complex multivariable coupling environments and has excellent real-time and robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0109] Figure 1 It is a principle block diagram of the present invention. DETAILED DESCRIPTION

[0110] The specific implementation modes of the present invention are further described in detail below with reference to the accompanying drawings.

[0111] like Figure 1As shown, the clean room pressure intelligent control system of the present invention is composed of a data processing module, a modeling prediction module, a control strategy selection module, a real-time feedback adjustment module, a prediction optimization allocation module, a control signal output module, an actual execution module and a physical signal feedback module. The main function of the system is to accurately control the pressure and flow in the clean room through a multi-level model predictive control (MPC) algorithm and an automated state space model to ensure the stability of the room pressure difference and flow.

[0112] The data processing module includes an initialization module and a data acquisition module. The initialization module is used to collect the baseline data of the clean room when the system starts, and obtain the initial environmental parameters (such as the initial pressure, supply air volume and return air volume of the room) to provide basic data for subsequent control strategies. The data acquisition module is used for real-time data collection when the system is running, to obtain environmental data such as the supply air volume, return air volume, room pressure, temperature and humidity of the clean room, and pass these data to the modeling prediction module and the control strategy selection module for model prediction and control strategy calculation.

[0113] The modeling and prediction module is used to automatically build the state space model and dynamically update it according to real-time data. The automated state space model includes three parts: data collection and initialization, automated modeling, and state variable update:

[0114] Data collection and initialization: When the system starts, the data processing module collects the room's environmental data through the initialization module and the data collection module, including the room's initial pressure, target pressure, air supply volume, return air volume, and external disturbance factors that affect the room's pressure (such as the state of doors and windows, and the pressure coupling effect of other rooms). These data serve as the basis for subsequent state space modeling and control.

[0115] Automated modeling is a state-space model that the modeling prediction module automatically generates based on the collected data to describe the dynamic behavior of room pressure. The model uses room pressure and pressure change rate as core state variables, and automatically establishes a mapping relationship between state variables and control inputs by analyzing the impact of the supply and return air systems on pressure. The system analyzes the response relationship between the supply and return air valve openings and room pressure based on historical data, extracts dynamic characteristics, and constructs a state-space model. During operation, the modeling prediction module can dynamically update model parameters based on real-time data to ensure that the model accurately reflects the actual dynamic characteristics of the room.

[0116] The state variable is updated at each time step. The modeling and prediction module calculates the system state update value based on the latest room pressure, supply air volume and return air volume data, and provides the updated model to the control strategy selection module to provide support for the hierarchical MPC algorithm.

[0117] The control strategy selection module includes a room pressure control module and a damper flow control module, which respectively realize precise control of the overall room pressure and the opening of the air supply valve. The room pressure control module is based on a constrained MPC algorithm. It first obtains the deviation between the current room pressure and the target pressure, calculates the pressure adjustment amount, generates a global control signal, and sends the signal to the air supply valve execution module and the return and exhaust valve execution module to adjust the supply and exhaust air volumes to ensure that the overall room pressure is stable at the set value; the damper flow control module generates a refined control signal based on a nonlinear MPC algorithm, obtains the opening of the air supply valve and the actual flow data, calculates the deviation between the target air supply flow and the actual flow, and generates a flow control signal for adjusting the opening of the air supply valve to achieve precise control of the air supply volume. Through a hierarchical control strategy, the system can coordinate and adjust pressure and flow at the macro and micro levels.

[0118] The MPC algorithm prediction and control are based on the state space model. The system uses the MPC algorithm to predict the change trend of room pressure within the set prediction time domain (5 to 30 seconds in the future) and makes control decisions based on the target pressure setting value. It specifically includes the room pressure control module (global control, with constrained MPC) and the air valve flow control module (fine control, nonlinear MPC). The following describes the specific implementation of the two parts:

[0119] The room pressure control module calculates the global adjustment signal of the supply air valve and the return air valve through the constrained MPC algorithm to achieve the stability of the overall pressure of the room. The system generates a global control signal based on the target pressure value and real-time pressure data to adjust the supply and return air volume. The system sets the target pressure value through the user and determines the adjustment range in combination with the current pressure of the room. Then, the constraints are set according to the maximum supply air volume, return air volume and valve opening range allowed by the system to avoid excessive fluctuations or over-limit behavior during the control process. Finally, the optimization calculation is performed to obtain the best supply and return air adjustment signal. The optimization result generates a global control signal, which directly acts on the supply air valve and return air valve execution module.

[0120] The system collects historical pressure data of the room, including external interference factors (such as door and window switches, HVAC system working status, etc.) and pressure coupling effects of other rooms. The data is analyzed by machine learning algorithms to automatically generate a state space model that describes the room pressure changes.

[0121] In the process of model predictive control, the state space model is not fixed. The system continuously updates the state space model based on real-time pressure data and historical data, and uses online learning algorithms to dynamically modify model parameters to ensure that the model can always accurately reflect the dynamic changes of the current system. The automated model update mechanism ensures that the system can still maintain a high degree of control accuracy when facing dynamic environments and nonlinear changes.

[0122] The control strategy selection module includes a room pressure control module and a wind valve flow control module, which are responsible for different control objectives respectively.

[0123] The room pressure control module generates a global control signal based on the constrained MPC algorithm, which specifically includes obtaining the deviation between the current room pressure and the target pressure value, calculating the pressure adjustment amount, generating a global control signal, and sending the signal to the supply air valve execution module and the return air valve execution module to adjust the supply air volume and exhaust air volume to ensure that the overall pressure of the room is maintained at the set value.

[0124] MPC algorithm prediction and control are based on the updated state space model. The system uses the MPC algorithm to predict the future change trend of room pressure within a given prediction time domain (5 to 30 seconds in the future) and makes control decisions based on the preset pressure target value.

[0125] The specific process includes: generating a prediction model based on the state space model, the MPC algorithm predicts the pressure changes of the system in the future, and generates a prediction curve; by solving the constrained optimization problem, the optimal control input sequence u(t) in the future is determined, so that the system pressure gradually approaches and remains near the target pressure value. The optimization goal is to minimize the deviation between the target pressure and the actual pressure, while constraining the control input (such as the rate of change and size of the air supply volume) to remain within the acceptable range of the system. At each time step, the MPC algorithm will recalculate the control strategy based on the latest measurement data to ensure that the system can respond to changes in the external environment in real time.

[0126] The real-time feedback adjustment module is used to receive feedback data from the physical signal feedback module in real time, and dynamically adjust the control strategy according to the actual pressure and flow feedback data, including adjusting the intensity or response speed of the control signal, so as to quickly respond to environmental changes and execution errors, and ensure that the pressure difference and flow in the clean room are always maintained within the set target range.

[0127] The predictive optimization allocation module is used to receive the global and refined control signals from the control strategy selection module, allocate the control signals through the optimization algorithm, and transmit the optimized signals to the supply and return air actuators respectively to achieve comprehensive control of room pressure and flow.

[0128] The global control signal is mainly used to adjust the overall pressure of the room. It is solved by constrained MPC by combining the room target pressure setting value and the actual pressure state:

[0129]

[0130] Among them, q s (t) is the global air supply volume; q r(t) is the global return air volume;

[0131] The refined control signal is used to further refine the air supply control. Based on the optimization results of nonlinear MPC, the opening adjustment signal acting on the air supply valve is generated:

[0132] u2(t)=Δθ s (t)

[0133] Among them, Δθ s (t) is the air supply valve opening adjustment amount.

[0134] The mechanism for collaboratively generating control signals includes the fusion of global and refined signals and feedback adjustment;

[0135] Fusion of global and refined signals provides the basic adjustment quantity q for the global control signal s (t), the refined control signal u2(t) is further corrected to ensure the control accuracy; the final air valve control signal is u final (t) = u1(t) + u2(t);

[0136] Feedback adjustment adjusts global and refined control signals based on real-time feedback to ensure that the overall room pressure and air flow rate meet the target requirements at the same time;

[0137] The global control signal provides the basic adjustment amount, and the refined control signal is further corrected to ensure control accuracy and ultimately ensure the air valve control signal.

[0138] The air valve flow control module uses the nonlinear MPC algorithm to achieve refined control of the opening of the air supply valve, which is mainly used to solve the nonlinear problem of the air supply valve and further improve the accuracy of flow regulation. The flow control module calculates the deviation based on the real-time air supply flow data and the target air supply flow, and generates a refined control signal. The current opening and actual flow of the air supply valve are obtained through the data acquisition module, and the flow deviation is recorded. Then, based on the nonlinear relationship between the air supply flow and the opening of the air supply valve, the opening is refined and adjusted to ensure that the air supply flow meets the target value.

[0139] Implementation of hierarchical collaborative control: The global control signal is generated by the room pressure control module, which serves as the main regulation signal of the system to control the large-scale changes in the supply and return air volumes. The refined control signal is generated by the air valve flow control module to further optimize the regulation accuracy of the supply air valve. The system integrates the global and refined control signals through the predictive optimization allocation module, generates the final control command, and acts on the supply air valve and return and exhaust air valve execution modules.

[0140] The air valve flow control module generates a refined control signal based on the nonlinear MPC algorithm, which specifically includes obtaining the current opening of the air supply valve and the actual flow data, calculating the deviation between the target air supply flow and the actual flow, and generating a refined flow control signal for adjusting the opening of the air supply valve to achieve precise control of the air supply flow.

[0141] The steps for the air valve flow control module to generate refined control signals based on the nonlinear MPC algorithm are:

[0142] Establish a prediction model for the nonlinear MPC algorithm:

[0143] x2(t+1|t)=f(x2(t),u2(t))

[0144] y2(t)=h(x2(t))

[0145] Among them, x2(t+1|t) represents the predicted state of the system at time t+1 under the condition of the system state and control input known at the current time t; f(x2(t),u2(t)) represents the nonlinear function of state transfer, and the specific form is obtained by actual physical modeling or actual data-driven method, x2(t) is the internal pressure value and pressure change rate of the system at the current time t, u2(t) is the control input vector of the system, including the supply air volume and return air volume; y2(t) is the output vector of the system, which represents the measured output value of the system at the current time t, including the room pressure value; h(x2(t)) represents the nonlinear relationship between output and state;

[0146] Optimize the objective function J f for:

[0147]

[0148] Where J2 is the optimization objective function value, and the air supply volume is the target to be minimized; y2(t+k|t) is the predicted air supply volume value at the current time t at the future time t+k; y 2,ref is the target reference value of the air supply volume; Q is the output error weight matrix, which is used to weigh the deviation between the predicted value and the target value of the air supply volume; Δ is the change, Δu2(t+k|t) is the control input change at the prediction time (the change value of the air supply volume); R is the control input weight matrix, which is used to limit the adjustment range of the air supply volume;

[0149] The nonlinear processing method is numerical optimization, using Newton's method to directly optimize the objective function J2;

[0150] The Newton method sets the convergence threshold first, establishes the objective function gradient, and defines the objective function according to the objective function. Use the Hessian matrix to calculate, and finally update the formula of u according to the convergence condition.

[0151] Constraints include flow range constraints and control input range constraints:

[0152] Flow range constraints:

[0153] q s,min ≤q s (t+k|t)≤q s,max

[0154] Where: q s,min is the minimum value of air supply volume; q s,max is the maximum value of air supply volume; q s (t+k|t) is the predicted state of air supply volume at t+k based on time t;

[0155] Control input range constraints:

[0156] θ s,min ≤θ s (t+k|t)≤θ s,max

[0157] Where: θ s,min is the minimum opening of the air supply valve; θ s,max is the maximum opening of the air supply valve;

[0158] θ s (t+k|t) is the predicted state of the air supply valve opening at t+k based on time t.

[0159] The steps for the room pressure control module to generate the global control signal based on the constrained MPC algorithm are:

[0160] Establish a prediction model with constrained MPC algorithm:

[0161] x1(t+k+1|t)=A1·x1(t+k|t)+B1·u1(t+k|t)+w1(t+k|t)

[0162] y1(t+k|t)=C1·x1(t+k|t)+v1(t+k|t)

[0163] Where x1(t+k+1|t) represents the predicted state of the system at time t+k+1 given the system state and control input known at the current time t, k = 0, 1, ..., N p -1, N p represents the number of time steps for future prediction; u1(t+k|t), y1(t+k|t), w1(t+k|t), and v1(t+k|t) respectively represent the control input, control output, system noise, and measurement error of the system at time t+k when the system state and control input are known at the current time t.

[0164] The optimization objective function J1 is:

[0165]

[0166] Among them, J1 is the optimization objective function value, which is the target to be minimized. Its value reflects the control performance of the system in the prediction time domain, and includes two parts: the output error term and the weight term of the control input; y1(t+k|t) is the predicted system output room pressure value at t+k when the current time t is known; y 1,ref is the target pressure value; Q is the output error weight matrix, which is used to weigh the deviation between the target pressure and the actual pressure. It is a positive semidefinite matrix. The larger the weight value, the more important the error in this dimension is. Q is adjusted according to the requirements for pressure stability in system design; u1(t+k|t) is the control input vector, which means that when t is known, the control variable at time t+k is predicted, which is the supply air volume and return air volume; R is the control input weight matrix, which is used to limit the range of control input changes. It is a positive definite matrix. The larger the value, the stricter the restriction on signal changes.

[0167] The constraints include pressure range constraints, input amplitude constraints, and input change rate constraints:

[0168] Pressure range constraints:

[0169] p min ≤y q (t+k|t)≤p max

[0170] Among them, p min and p max is the allowable room pressure range, indicating the minimum and maximum values ​​respectively;

[0171] Input amplitude constraint:

[0172] q s,min ≤q s (t+k|t)≤q s,max

[0173] q r,min ≤q r (t+k|t)≤q r,max

[0174] Among them, q s,min is the minimum value of air supply volume; q s,max is the maximum value of air supply volume; q s (t+k|t) is the predicted state of air supply volume at time t+k based on time t; q r,min is the minimum value of the return air volume; q r,max is the maximum value of the return air volume; q r(t+k|t) is the predicted state of the return air volume at time t+k based on time t;

[0175] Input change rate constraint:

[0176] Δq s,min ≤q s (t+k|t)-q s (t+k-1|t)≤Δq s,max

[0177] Δq r,min ≤q r (t+k|t)-q r (t+k-1|t)≤Δq r,max

[0178] Among them, Δq s,min To predict the minimum difference in air volume between two moments; Δq s,max To predict the maximum difference in air volume between two moments; Δq r,min To predict the minimum difference in return air volume between two moments; Δq r,max To predict the maximum difference in return air volume between two moments.

[0179] The actual execution module includes the air supply valve execution module and the return and exhaust air valve execution module, which are used to control the supply air volume and the return air volume respectively. The air supply valve execution module receives the control signal from the control strategy selection module and the prediction optimization allocation module, adjusts the opening of the air supply valve, and realizes the precise control of the supply air volume; the return and exhaust air valve execution module adjusts the opening of the return and exhaust air valve according to the control signal to realize the adjustment of the return air volume, so as to ensure the realization of the overall pressure control target of the room.

[0180] The control system in this embodiment achieves precise control of the pressure and flow rate of the clean room through the automated state space model, MPC predictive control and real-time feedback adjustment. Compared with the traditional control method, the present invention can dynamically adapt to complex environmental changes and nonlinear interference, and maintain high-precision and stable control of the pressure and flow rate of the clean room under the condition of multi-variable coupling. This system not only has high control accuracy, but also has good real-time response capabilities, providing an efficient and reliable solution for the strict control of the clean environment.

[0181] The above is a preferred embodiment of the present invention. It should be noted that, for those skilled in the art, various improvements can be made to the present invention without departing from the principle of the present invention, and these improvements should also be regarded as the protection scope of the present invention.

Claims

1. A clean room room pressure intelligent control system, characterized by: It includes data processing module, modeling prediction module, control strategy selection module, prediction optimization allocation module, control signal output module, actual execution module, physical signal feedback module and real-time feedback adjustment module, among which: The data processing module includes an initialization module and a data acquisition module; the initialization module is used for the initial setting of the system and the acquisition of benchmark data; wherein the benchmark data acquisition refers to the acquisition of the initial environmental parameters and external factors of the clean room when the system is started, the establishment of the initial state space model and the setting of the initial control parameters; the data acquisition module is used to collect the dynamic environmental data of the clean room in real time, including the supply air volume, return air volume and room pressure; The modeling and prediction module is used to generate a state space model based on the data of the data processing module, and dynamically update the state space model through an adaptive algorithm; The control strategy selection module includes a room pressure control module and an air valve flow control module; the room pressure control module generates a global control signal for regulating the overall pressure of the room based on a constrained model predictive control algorithm, and the control signal directly acts on the air supply valve execution module and the return and exhaust air valve execution module to achieve overall pressure difference control of the room; the air valve flow control module generates a refined control signal for individually adjusting the opening and flow of the air supply valve based on a nonlinear model predictive control algorithm to ensure accurate regulation of the air supply volume in the room, thereby optimizing the stability of the room pressure and flow at a micro level; The prediction optimization allocation module is used to receive the control signal from the control strategy selection module, and dynamically allocate the control signal to different actuators after optimization; The real-time feedback adjustment module is used to monitor the real-time feedback data in the prediction optimization allocation module and dynamically adjust the control signal of the control strategy selection module so that the system can quickly respond to changes in the external environment and realize the real-time flow of feedback data and model update; The control signal output module is used to transmit the optimized and allocated control signal to the actual execution module; The actual execution module includes an air supply valve execution module and a return and exhaust air valve execution module; the air supply valve execution module is used to receive the global control signal from the room pressure control module and the refined control signal from the air valve flow control module, adjust the opening of the air supply valve, and realize the air supply control of the clean workshop; the return and exhaust air valve execution module is used to receive the global control signal from the room pressure control module, adjust the opening of the return and exhaust air valve, and realize the control of the overall pressure difference of the room; The physical signal feedback module is used to collect physical feedback data generated after execution, including pressure feedback and flow feedback, and send the feedback data to the data processing module to adjust the control strategy in real time.

2. The clean room pressure intelligent control system according to claim 1 is characterized by: The specific control steps of the room pressure control module and the air valve flow control module of the control strategy selection module are as follows: The room pressure control module generates a global control signal based on the constrained MPC algorithm, including obtaining the deviation between the current room pressure and the target pressure value, calculating the pressure adjustment amount, generating a global control signal, and sending the signal to the air supply valve execution module and the return and exhaust valve execution module for adjusting the supply and exhaust air volumes; The air valve flow control module generates a refined control signal based on the nonlinear MPC algorithm, including obtaining the current opening of the air supply valve and the actual flow data, calculating the deviation between the target air supply flow and the actual flow, generating a refined flow control signal, and sending the signal to the air supply valve execution module for adjusting the opening of the air supply valve to accurately control the air supply flow.

3. The clean room pressure intelligent control system according to claim 2 is characterized in that ,The state space model generated by the modeling and prediction ,module consists of the following two parts: The room pressure control model is used to describe the dynamic characteristics of the overall room pressure and adjust the global control quantity of the supply air valve and the return air valve. The state vector, control input and output vector of the room pressure control model are defined as follows: The state vector x1(t), including the room pressure value (p(t)) and the pressure change rate It is expressed as: The control input u1(t) includes the air volume of the air supply system (q s (r)) and the air volume of the return air system (q r (t)), which are used to adjust the positive and negative pressures of the room, respectively, and are expressed as: u1(t)=[q s (t)q r (t)] T The output vector y1(t) is the real-time pressure value p(t) of the room, which is used to compare with the target pressure p ref (t) comparison, expressed as: y1(t)=p(t) Equation of state: x1(t+1)=A1x1(t)+B1u1(t)+w1(t) Among them: A1 is the system matrix, which is used to describe the dynamic characteristics of room pressure and its gradient changes; B1 is the input matrix, which is used to describe the regulating effect of supply and return air on room pressure; w1(t) is the system noise, including the instantaneous pressure fluctuation caused by the opening of doors and windows and the ambient pressure gradient fluctuation caused by the start and stop of the equipment fan; Output equation: y1(t)=C1x1(t)+v1(t) Where: C1 is the output matrix, which is used to extract the real-time pressure value of the room; v1(t) is the measurement noise, which is mainly the measurement error of the pressure sensor; The air valve flow control model is used to describe the dynamic characteristics of the air supply valve. By finely adjusting the opening of the air supply valve, the air supply flow rate can be accurately controlled. The state vector, control input and output vector of the air valve flow control model are defined as follows: The state vector x2(t), including the air volume q of the air supply system s (t) and the opening of the air supply valve θ s (t), expressed as: x2(t)=[q s (t)θ s (t)] T Control input u2(t), represents the adjustment amount Δθ of the air supply valve opening s (t), expressed as: u2(t)=Δθ s (t) Output vector y2(t), representing the actual air flow q at the current moment s (t), used to match the target air supply volume q s , ref (t) comparison, expressed as: y2(t)=q s (t) Equation of state: x2(t+1)=A2x2(t)+B2u2(t)+w2(t) Among them: A2 is the system matrix, which is used to describe the dynamic characteristics of the air supply valve; B2 is the input matrix, which is used to describe the regulating effect of the air supply valve opening on the air supply volume; w2(t) is the system noise, including nonlinear interference such as air supply duct vibration and ambient temperature and humidity fluctuations; Output equation: y2(t)=C2x2(t)+v2(t) Among them: C2 is the output matrix, which is used to extract the air supply volume; v2(t) is the measurement noise, which is mainly the measurement error of the air volume sensor.

4. The clean room pressure intelligent control system according to claim 3 is characterized by: The steps for the room pressure control module to generate the global control signal based on the constrained MPC algorithm are: Establish a prediction model with constrained MPC algorithm: x1(t+k+1|t)=A1·x1(t+k|t)+B1·u1(t+k|t)+w1(t+k|t) y1(t+k|t)=C1·x1(t+k|t)+v1(t+k|t) Where x1(t+k+1|t) represents the predicted state of the system at time t+k+1 given the system state and control input known at the current time t, k = 0, 1, ..., N p -1, N p represents the number of time steps for future prediction; u1(t+k|t), y1(t+k|t), w1(t+k|t) and v1(t+k|t) represent the control input, control output, system noise and measurement error of the system at time t+k, respectively, when the system state and control input are known at the current time t; The optimization objective function J1 is: Among them, J1 is the optimization objective function value, which is the target to be minimized. Its value reflects the control performance of the system in the prediction time domain, and includes two parts: the output error term and the weight term of the control input; y1(t+k|t) is the predicted system output room pressure value at t+k when the current time t is known; y 1,ref is the target pressure value; Q is the output error weight matrix, which is used to weigh the deviation between the target pressure and the actual pressure. It is a positive semidefinite matrix. The larger the weight value, the more important the error in this dimension is. Q is adjusted according to the requirements for pressure stability in system design; u1(t+k|t) is the control input vector, which means that when t is known, the control variable at time t+k is predicted, which is the supply air volume and return air volume; R is the control input weight matrix, which is used to limit the range of control input changes. It is a positive definite matrix. The larger the value, the stricter the restriction on signal changes. The constraints include pressure range constraints, input amplitude constraints, and input change rate constraints: Pressure range constraints: p min ≤y1(t+k|t)≤p max Among them, p min and p max is the allowable room pressure range, indicating the minimum and maximum values ​​respectively; Input amplitude constraint: q s,min ≤q s (t+k|t)≤q s,max q r,min ≤q r (t+k|t)≤q r,max Among them, q s,min is the minimum value of air supply volume; q s,max is the maximum value of air supply volume; q s (t+k|t) is the predicted state of air supply volume at time t+k based on time t; q r,min is the minimum value of the return air volume; q r,max is the maximum return air volume; q r (t+k|t) is the predicted state of the return air volume at time t+k based on time t; Input change rate constraint: Δq s,min ≤q s (t+k|t)-q s (t+k-1|t)≤Δq s,max Δq r,min ≤q r (t+k|t)-q r (t+k-1|t)≤Δq r,max Among them, Δq s,min To predict the minimum difference in air volume between two moments; Δq s,max To predict the maximum difference in air volume between two moments; Δq r,min To predict the minimum difference in return air volume between two moments; Δq r,max To predict the maximum difference in return air volume between two moments.

5. The clean room pressure intelligent control system according to claim 2 is characterized in that: The steps for the air valve flow control module to generate refined control signals based on the nonlinear MPC algorithm are: Establish a prediction model for the nonlinear MPC algorithm: x2(t+1|t)=f(x2(t),u2(t)) y2(t)=h(x2(t)) Among them, x2(t+1|t) represents the predicted state of the system at time t+1 under the condition of the system state and control input known at the current time t; f(x2(t),u2(t)) represents the nonlinear function of state transfer, and the specific form is obtained by actual physical modeling or actual data-driven method, x2(t) is the internal pressure value and pressure change rate of the system at the current time t, u2(t) is the control input vector of the system, including the supply air volume and return air volume; y2(t) is the output vector of the system, which represents the measured output value of the system at the current time t, including the room pressure value; h(x2(t)) represents the nonlinear relationship between output and state; Optimize the objective function J f for: Where J2 is the optimization objective function value, and the air supply volume is the target to be minimized; y2(t+k|t) is the predicted air supply volume value at the current time t at the future time t+k; y 2,ref is the target reference value of the air supply volume; Q is the output error weight matrix, which is used to weigh the deviation between the predicted value and the target value of the air supply volume; Δ is the change, Δu2(t+k|t) is the change value of the air supply volume at the predicted moment; R is the control input weight matrix, which is used to limit the adjustment range of the air supply volume.

6. The clean room pressure intelligent control system according to claim 5 is characterized by: Use Newton's method to directly optimize the objective function J2; The Newton method sets the convergence threshold first, establishes the objective function gradient, and defines the objective function according to the objective function. Use the Hessian matrix to calculate, and finally update the formula of u according to the convergence condition; Constraints include flow range constraints and control input range constraints: Flow range constraints: q s,min ≤q s (t+k|t)≤q s,max Where: q s,min is the minimum value of air supply volume; q s,max is the maximum value of air supply volume; q s (t+k|t) is the predicted state of air supply volume at t+k based on time t; Control input range constraints: θ s,min ≤θ s (t+k|t)≤θ s,max Where: θ s,min is the minimum opening of the air supply valve; θ s,max is the maximum opening of the air supply valve; θ s (t+k|t) is the predicted state of the air supply valve opening at t+k based on time t.

7. The clean room pressure intelligent control system according to claim 3 is characterized by: The prediction optimization allocation module integrates the global and refined control signals to generate the final control command, which acts on the air supply valve and return and exhaust valve execution modules. The final air valve control signal u final (t) is: u final (P)=u1(P)+u2(t) Among them, u1(t) is the global control signal generated by the pressure control module, and u2(t) is the refined control signal output by the air valve flow control module.

8. The clean room pressure intelligent control system according to claim 7 is characterized by: The global control signal u1(t) generated by the pressure control module and the refined control signal i2(t) output by the air valve flow control module are respectively: Among them, q s (t) is the global air supply volume; q r (t) is the global return air volume; u2(t)=Δθ s (t) Among them, Δθ s (t) is the air supply valve opening adjustment amount.

9. The clean room pressure intelligent control system according to claim 1 is characterized by: The data processing module further includes an external factor input interface for collecting external interference factor data in the clean room environment, including temperature, humidity and other room pressure differences.

10. A method for intelligent control of room pressure in a clean room, characterized in that: Based on the clean room pressure intelligent control system according to any one of claims 1 to 9, the control method comprises the following steps: Step 1: Initialize the system through the data processing module and collect the benchmark data of the clean room; Step 2: Generate the initial state space model using the modeling prediction module, and obtain real-time data through the data acquisition module to update the model; Step 3: The room pressure control module generates a global control signal based on the constrained MPC algorithm, which specifically includes: obtaining the deviation between the current room pressure and the target pressure, calculating the adjustment amount and generating a global control signal, and sending the signal to the supply air valve execution module and the return and exhaust air valve execution module; Step 4: The air valve flow control module generates a refined control signal based on the nonlinear MPC algorithm, specifically including: obtaining the current opening and actual flow of the air supply valve, calculating the flow deviation and generating a refined control signal, and sending it to the air supply valve execution module; Step 5: The prediction optimization allocation module optimizes the control signal and allocates it to the corresponding actuator; Step 6: The control signal output module transmits the optimized control signal to the actual execution module; Step 7: The air supply valve execution module and the return and exhaust air valve execution module adjust the valve opening according to the control signal to adjust the supply air volume and the return air volume; Step 8: The physical signal feedback module collects the feedback data of the room pressure and flow after execution, and sends it to the real-time feedback adjustment module to adjust the control strategy according to the real-time feedback; Step 9: Return to step 2 for loop control to achieve adaptive control of clean room pressure and flow.

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