CN117212115B - Control system of air compressor inlet control valve - Google Patents
Control system of air compressor inlet control valve Download PDFInfo
- Publication number
- CN117212115B CN117212115B CN202311239369.3A CN202311239369A CN117212115B CN 117212115 B CN117212115 B CN 117212115B CN 202311239369 A CN202311239369 A CN 202311239369A CN 117212115 B CN117212115 B CN 117212115B
- Authority
- CN
- China
- Prior art keywords
- data
- microprocessor
- driving device
- electric driving
- temperature
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000009826 distribution Methods 0.000 claims abstract description 45
- 238000005457 optimization Methods 0.000 claims description 42
- 238000004364 calculation method Methods 0.000 claims description 19
- 238000000034 method Methods 0.000 claims description 12
- 238000012937 correction Methods 0.000 claims description 7
- 230000008859 change Effects 0.000 claims description 5
- 238000005265 energy consumption Methods 0.000 claims description 4
- 238000012544 monitoring process Methods 0.000 claims description 4
- 238000004458 analytical method Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000009975 flexible effect Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Landscapes
- Control Of Positive-Displacement Pumps (AREA)
Abstract
The invention relates to the technical field of air compressor control, in particular to a control system of an air compressor air inlet control valve, which comprises the following components: a microprocessor that receives data inputs from the plurality of sensors; an electric driving device for physically adjusting the opening of the intake control valve according to a control signal from the microprocessor; the temperature sensor is used for measuring the temperature of the air inlet and transmitting the temperature data to the microprocessor; and the dynamic load regulator is electrically connected with the microprocessor and calculates a load distribution value through the air inlet temperature data received from the microprocessor and the current state data of the electric driving device. The invention ensures that the optimal energy efficiency can be achieved on the premise of meeting all practical constraints, and greatly improves the performance and reliability of the whole air compressor air inlet control system.
Description
Technical Field
The invention relates to the technical field of air compressor control, in particular to a control system of an air compressor air inlet control valve.
Background
In industrial production and other various application scenarios, air compressors play a vital role. Air compressors are powered by compressed air, but their efficiency and performance are affected by a variety of factors, including intake air temperature, speed and position of the electric drive, and the like. Conventional air compressor intake control systems mostly use simple control algorithms and fixed system constraints, which often do not achieve optimal performance in complex and diverse operating environments.
Current techniques focus primarily on optimization of a single objective, such as reducing energy consumption or optimizing output efficiency, but often ignore other key factors and practical constraints. At the same time, these systems often lack efficient adaptation mechanisms and user interaction interfaces, resulting in a potential for degradation and even failure of the system as operating conditions change.
While some advanced control systems attempt to improve performance by integrating more complex algorithms and sensors, these systems are typically custom-built for a particular application, lacking versatility. Furthermore, the user interfaces of these systems are often not intuitive enough, lacking sufficient customizable and flexible properties, which further limit their applicability in different applications and environments.
Therefore, there is an urgent need to develop an air compressor intake control system that is highly adaptive, easy to operate, and highly integrated to solve the above-described problems and achieve multi-objective optimization.
Disclosure of Invention
Based on the above purpose, the invention provides a control system of an air compressor air inlet control valve.
A control system of an air compressor intake control valve, comprising:
A microprocessor that receives data inputs from the plurality of sensors;
the electric driving device is electrically connected with the microprocessor and is used for physically adjusting the opening of the air inlet control valve according to a control signal from the microprocessor;
the temperature sensor is configured at the air inlet of the air compressor and is used for measuring the temperature of the air inlet and transmitting the temperature data to the microprocessor;
The dynamic load regulator is electrically connected with the microprocessor, calculates a load distribution value through the air inlet temperature data received from the microprocessor and the current state data of the electric driving device, and sends the load distribution value back to the microprocessor to adjust the operation parameters of the electric driving device;
wherein,
The microprocessor receives temperature data from the temperature sensor, receives a final load distribution value from the dynamic load regulator, generates a corresponding control signal according to the temperature data and the final load distribution value, sends the control signal to the electric driving device, and forms a closed loop system for data and control among the microprocessor, the electric driving device, the temperature sensor and the dynamic load regulator.
Further, the microprocessor receives temperature data of the temperature sensor and state data of the electric driving device;
Status data of the electric drive: the status data includes the current position, speed, torque of the electric drive.
Further, the electric drive device includes:
The stepping motor is electrically connected with the microprocessor and is used for receiving a control signal sent by the microprocessor;
the torque sensor is arranged on the stepping motor, and is used for measuring the torque applied by the stepping motor in real time and transmitting torque data to the microprocessor;
the position feedback device is also arranged on the stepping motor and is used for monitoring the current position of the stepping motor and transmitting the position data to the microprocessor;
The stepping motor, the torque sensor and the position feedback device are all electrically connected with the microprocessor to form a tightly integrated subsystem, and the microprocessor generates specific control signals according to torque data from the torque sensor, position data from the position feedback device and the optimized load distribution value provided by the dynamic load regulator, and the control signals are used for adjusting the operation parameters of the stepping motor so as to change the physical opening of the air inlet control valve.
Further, the dynamic load regulator includes:
a load calculation unit that receives temperature data and electric drive state data from the microprocessor;
And the load calculation unit performs preliminary processing, including denoising and standardization, on the received temperature data and the state data of the electric driving device, and the processed data is transmitted to the load optimization algorithm module for calculation.
Further, the load optimization algorithm module calculates an optimized load distribution value based on the following equation:
OptimizedLoadValue＝f(T，S，P)
Wherein T is temperature data, S is speed data of the electric driving device, and P is position data of the electric driving device.
Further, the load optimization algorithm module of the dynamic load regulator further comprises a multidimensional optimization submodule and a constraint satisfaction submodule;
Multidimensional optimization sub-module: receiving temperature data T, speed data S and position data P of an electric driving device from a load calculation unit, and carrying out optimization calculation in a multidimensional parameter space according to the data, wherein the multidimensional optimization adopts a Lagrangian multiplier method, and a load distribution value is solved through analysis;
Constraint satisfaction submodule: and the optimization load distribution value is used for checking whether the calculated optimization load distribution value meets the preset constraint condition, the preset constraint condition comprises the maximum moment limit and the minimum moment limit of the electric driving device and the safety range of the air inlet temperature of the air compressor, if the calculated value does not meet the preset constraint condition, the constraint meeting sub-module starts an iterative correction program, and the calculation is carried out again until the optimization load distribution value meeting the constraint meeting sub-module is obtained.
Further, the multidimensional optimization submodule algorithm is as follows:
The system is provided with three variables: intake temperature T, speed S of the electric drive and position P, optimization objective is to minimize energy consumption, expressed as:
E＝aT2+bS2+cP2
Wherein a, b, and c are system parameters;
Constructing a Lagrange function:
L(E，λ)＝aT2+bS2+cP2+λ(g(T，S，P)-h)
where g (T, S, P) is an equation describing the system constraints, h is a constant, and λ is the Lagrangian multiplier;
The multidimensional optimization sub-module finds the optimal solution by solving the partial derivative of the lagrangian function and setting it to zero:
solving an equation to obtain an optimized load distribution value;
the constraint satisfaction submodule checks whether the solution satisfies a predetermined constraint condition.
Further, the iterative correction procedure specifically includes:
A start point selection unit: the unit selects an initial point in the constraint space as a starting value of h in the Lagrangian function;
Constraint checking unit: the unit receives the load distribution value calculated from the multidimensional optimization submodule and checks whether the value meets all preset system constraints, including the maximum and minimum moments of the electric drive device and the safety range of the air inlet temperature;
Correction calculation means for: if the preliminary optimized load allocation value does not meet the constraint, the unit fine-tunes h in the Lagrangian function using the Newton-Lafreson method.
Further, the system further comprises:
and a data display sub-module: displaying relevant data from the dynamic load regulator and the electric driving device in real time, wherein the relevant data comprise air inlet temperature, speed and position of the electric driving device and current load distribution value;
control input submodule: the user is allowed to input or modify preset system constraint parameters, including maximum and minimum torque of the electric drive device, and safety range of air intake temperature, through a touch screen or other input device.
The invention has the beneficial effects that:
the invention realizes highly accurate and self-adaptive load control by integrating a plurality of innovative modules such as the dynamic load regulator, the electric driving device and the user interface module, in particular, the multidimensional optimization submodule and the constraint meeting submodule of the dynamic load regulator can reach optimal energy efficiency on the premise of meeting all practical constraints through advanced algorithms. This has improved the performance and the reliability of whole air compressor machine air inlet control system greatly.
According to the invention, the iterative correction program module in the constraint meeting sub-module increases the self-adaptive capacity of the system, and the module ensures that the system can quickly find a global or approximate global optimal solution in a complex multi-constraint and multi-target environment through continuous iteration and optimization, which has important significance for ensuring long-term stable operation of the system, especially under changing working conditions.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only of the invention and that other drawings can be obtained from them without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a control system module according to an embodiment of the present invention;
Fig. 2 is a schematic diagram of a dynamic load regulator according to an embodiment of the present invention.
Detailed Description
The present invention will be further described in detail with reference to specific embodiments in order to make the objects, technical solutions and advantages of the present invention more apparent.
It is to be noted that unless otherwise defined, technical or scientific terms used herein should be taken in a general sense as understood by one of ordinary skill in the art to which the present invention belongs. The terms "first," "second," and the like, as used herein, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.
As shown in fig. 1-2, a control system for an air compressor intake control valve includes:
A microprocessor that receives data inputs from the plurality of sensors;
the electric driving device is electrically connected with the microprocessor and is used for physically adjusting the opening of the air inlet control valve according to a control signal from the microprocessor;
the temperature sensor is configured at the air inlet of the air compressor and is used for measuring the temperature of the air inlet and transmitting the temperature data to the microprocessor;
The dynamic load regulator is electrically connected with the microprocessor, calculates a final optimized load distribution value through the air inlet temperature data received from the microprocessor and the current state data of the electric driving device, and sends the load distribution value back to the microprocessor to adjust the operation parameters of the electric driving device;
wherein,
The microprocessor receives temperature data from the temperature sensor, receives a final load distribution value from the dynamic load regulator, generates a corresponding control signal according to the temperature data and the final load distribution value, sends the control signal to the electric driving device, and forms a closed loop system for data and control among the microprocessor, the electric driving device, the temperature sensor and the dynamic load regulator;
The dynamic load regulator calculates an optimized load distribution value for indicating how much torque the electric drive applies within a specific time range to achieve the optimal opening degree of the intake control valve by a predetermined algorithm based on the intake air temperature data received from the microprocessor and the current state data of the electric drive.
The microprocessor receives temperature data of the temperature sensor and state data of the electric driving device;
Status data of the electric drive: the status data includes the current position, speed, torque of the electric drive (e.g., stepper motor or servo motor) which is also transmitted to the microprocessor and used to calculate the optimal load split value that the dynamic load regulator should output.
The electric drive device includes:
The stepping motor is electrically connected with the microprocessor and is used for receiving a control signal sent by the microprocessor;
the torque sensor is arranged on the stepping motor, and is used for measuring the torque applied by the stepping motor in real time and transmitting torque data to the microprocessor;
the position feedback device is also arranged on the stepping motor and is used for monitoring the current position of the stepping motor and transmitting the position data to the microprocessor;
The stepping motor, the torque sensor and the position feedback device are all electrically connected with the microprocessor to form a tightly integrated subsystem, and the microprocessor generates specific control signals according to the torque data from the torque sensor, the position data from the position feedback device and the optimized load distribution value provided by the dynamic load regulator, and the control signals are used for adjusting the operation parameters of the stepping motor so as to change the physical opening of the air inlet control valve;
The data of the torque sensor and the position feedback device are used not only for generating the control signal but also for transmitting to the dynamic load regulator. Using these data, the dynamic load regulator, in combination with the intake air temperature data from the temperature sensor, further optimizes the load distribution value, which is then sent back to the microprocessor;
By such a configuration and operation, the electric drive apparatus can more precisely adjust the opening degree of the intake control valve, thereby achieving higher operation efficiency and lower power consumption, while at the same time, this design of the electric drive apparatus also improves the reliability and durability of the system because the tight integration of the stepping motor, the torque sensor, and the position feedback apparatus enables the system to respond more quickly to various operating conditions and failure states.
The dynamic load regulator includes:
a load calculation unit that receives temperature data and electric drive state data from the microprocessor;
And the load calculation unit performs preliminary processing, including denoising and standardization, on the received temperature data and the state data of the electric driving device, and the processed data is transmitted to the load optimization algorithm module for calculation.
The load optimization algorithm module calculates an optimized load distribution value based on the following equation:
OptimizedLoadValue＝f(T,S,P)
Wherein T is temperature data, S is speed data of the electric driving device, and P is position data of the electric driving device;
By such a configuration, the dynamic load regulator not only achieves more accurate and efficient load adjustment, but also operates based on deterministic algorithms, avoiding uncertainty and ambiguity, which ensures optimal load distribution under various operating conditions, including temperature fluctuations and motor drive state changes. This further improves the energy efficiency and reliability of the overall control system.
The load optimization algorithm module of the dynamic load regulator further comprises a multidimensional optimization submodule and a constraint satisfaction submodule;
Multidimensional optimization sub-module: receiving temperature data T, speed data S and position data P of an electric driving device from a load calculation unit, and carrying out optimization calculation in a multidimensional parameter space according to the data, wherein the multidimensional optimization adopts a Lagrangian multiplier method, and a load distribution value is solved through analysis;
Constraint satisfaction submodule: the method comprises the steps of checking whether a calculated optimal load distribution value meets preset constraint conditions, wherein the preset constraint conditions comprise maximum and minimum moment limits of an electric driving device and a safety range of air inlet temperature of an air compressor, if the calculated value does not meet the preset constraint conditions, starting an iterative correction program by a constraint meeting sub-module, and recalculating until the optimal load distribution value meeting the constraint meeting sub-module is obtained;
in this configuration, the multidimensional optimization sub-module first generates an initial optimized load allocation value, which is then passed to the constraint satisfaction sub-module for verification and possible correction, which is then sent back to the microprocessor and used to generate specific control signals to the electric drive.
The multidimensional optimization submodule algorithm is as follows:
The system is provided with three variables: intake temperature T, speed S of the electric drive and position P, optimization objective is to minimize energy consumption, expressed as:
E＝aT2+bS2+cP2
Wherein a, b, and c are system parameters;
Constructing a Lagrange function:
L(E，λ)＝aT2+bS2+cP2+λ(g(T，S，P)-h)
where g (T, S, P) is an equation describing the system constraints, h is a constant, and λ is the Lagrangian multiplier;
The multidimensional optimization sub-module finds the optimal solution by solving the partial derivative of the lagrangian function and setting it to zero:
solving an equation to obtain an optimized load distribution value;
The constraint satisfaction sub-module checks whether the solution satisfies a predetermined constraint, in particular, it ensures that the calculated load distribution value does not cause the torque of the electric drive to exceed its maximum or minimum limit, or the intake air temperature to exceed a safe range, and if not, the constraint satisfaction sub-module iteratively corrects h in the Lagrangian function and re-solves the above equation set until a solution is found that satisfies all constraints.
The iterative correction procedure is specifically as follows:
A start point selection unit: the unit selects an initial point in the constraint space as a starting value of h in the Lagrangian function;
Constraint checking unit: the unit receives the load distribution value calculated from the multidimensional optimization submodule and checks whether the value meets all preset system constraints, including the maximum and minimum moments of the electric drive device and the safety range of the air inlet temperature;
correction calculation means for: if the preliminary optimized load distribution value does not meet the constraint, the unit uses a Newton-Lafreson method to fine tune h in the Lagrangian function;
Under the configuration, the iterative correction program module realizes a highly self-adaptive and accurate constraint satisfaction mechanism, can correct the single condition of unsatisfied constraint, and can quickly find a global or approximate global optimal solution under the complex system environment of multiple constraints and multiple targets, thereby greatly improving the performance and reliability of the whole control system.
The system further comprises:
and a data display sub-module: displaying relevant data from the dynamic load regulator and the electric driving device in real time, wherein the relevant data comprise air inlet temperature, speed and position of the electric driving device and current load distribution value;
Control input submodule: allowing a user to input or change preset system constraint parameters including maximum and minimum moments of the electric driving device and a safety range of air inlet temperature through a touch screen or other input devices;
in this configuration, the user interface module implements a highly interactive and customizable system control platform. The system not only provides an intuitive way for monitoring and controlling the state of the system, but also can carry out tight data interaction with the core modules such as the dynamic load regulator, the electric driving device and the like, and ensures that the whole control system can realize optimal performance under various running environments and user requirements.
Those of ordinary skill in the art will appreciate that: the discussion of any of the embodiments above is merely exemplary and is not intended to suggest that the scope of the invention is limited to these examples; the technical features of the above embodiments or in the different embodiments may also be combined within the idea of the invention, the steps may be implemented in any order and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity.
The present invention is intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Therefore, any omission, modification, equivalent replacement, improvement, etc. of the present invention should be included in the scope of the present invention.
Claims (4)
1. A control system of an air compressor intake control valve, comprising:
A microprocessor that receives data inputs from the plurality of sensors;
the electric driving device is electrically connected with the microprocessor and is used for physically adjusting the opening of the air inlet control valve according to a control signal from the microprocessor;
the temperature sensor is configured at the air inlet of the air compressor and is used for measuring the temperature of the air inlet and transmitting the temperature data to the microprocessor;
The dynamic load regulator is electrically connected with the microprocessor, calculates a load distribution value through the air inlet temperature data received from the microprocessor and the current state data of the electric driving device, and sends the load distribution value back to the microprocessor to adjust the operation parameters of the electric driving device;
wherein,
The microprocessor receives temperature data from the temperature sensor, receives a final load distribution value from the dynamic load regulator, generates a corresponding control signal according to the temperature data and the final load distribution value, sends the control signal to the electric driving device, and forms a closed loop system for data and control among the microprocessor, the electric driving device, the temperature sensor and the dynamic load regulator;
The dynamic load regulator includes:
A load calculation unit that receives temperature data and state data of the electric drive device;
The load optimization algorithm module calculates a final optimized load distribution value according to the received temperature data and the state data of the electric driving device, wherein the optimized load distribution value is expressed as a number in a range of 0 to 100 percent and is used for specifying the relative moment which the electric driving device should apply, the load calculation unit carries out preliminary processing on the received temperature data and the state data of the electric driving device, including denoising and standardization, and the processed data is transmitted to the load optimization algorithm module for calculation;
the load optimization algorithm module calculates an optimized load distribution value based on the following equation:
OptimizedLoadValue＝f(T,S,P)
Wherein T is temperature data, S is speed data of the electric driving device, and P is position data of the electric driving device;
The load optimization algorithm module of the dynamic load regulator further comprises a multidimensional optimization submodule and a constraint satisfaction submodule;
Multidimensional optimization sub-module: receiving temperature data T, speed data S and position data P of an electric driving device from a load calculation unit, and carrying out optimization calculation in a multidimensional parameter space according to the data, wherein the multidimensional optimization adopts a Lagrangian multiplier method, and a load distribution value is solved through analysis;
Constraint satisfaction submodule: the method comprises the steps of checking whether a calculated optimal load distribution value meets preset constraint conditions, wherein the preset constraint conditions comprise maximum and minimum moment limits of an electric driving device and a safety range of air inlet temperature of an air compressor, if the calculated value does not meet the preset constraint conditions, starting an iterative correction program by a constraint meeting sub-module, and recalculating until the optimal load distribution value meeting the constraint meeting sub-module is obtained;
the multidimensional optimization submodule algorithm is as follows:
the system is provided with three variables: intake temperature T, speed S of the electric drive and position P, optimization objective is to minimize energy consumption, expressed as:
E＝aT2+bS2+cP2
Wherein a, b, and c are system parameters;
Constructing a Lagrange function:
L(E,λ)＝aT2+bS2+cP2+λ(g(T,S,P)-h)
where g (T, S, P) is an equation describing the system constraints, h is a constant, and λ is the Lagrangian multiplier;
The multidimensional optimization sub-module finds the optimal solution by solving the partial derivative of the lagrangian function and setting it to zero:
solving an equation to obtain an optimized load distribution value;
the constraint satisfaction sub-module will check whether the solution satisfies a predetermined constraint condition;
The iterative correction procedure specifically comprises the following steps:
A start point selection unit: the unit selects an initial point in the constraint space as a starting value of h in the Lagrangian function;
Constraint checking unit: the unit receives the load distribution value calculated from the multidimensional optimization submodule and checks whether the value meets all preset system constraints, including the maximum and minimum moments of the electric drive device and the safety range of the air inlet temperature;
Correction calculation means for: if the preliminary optimized load allocation value does not meet the constraint, the unit fine-tunes h in the Lagrangian function using the Newton-Lafreson method.
2. The control system of an air compressor intake control valve of claim 1, wherein the microprocessor receives temperature data from a temperature sensor and status data from an electric drive;
Status data of the electric drive: the status data includes the current position, speed, torque of the electric drive.
3. The control system of an air compressor intake control valve according to claim 2, wherein the electric driving means includes:
The stepping motor is electrically connected with the microprocessor and is used for receiving a control signal sent by the microprocessor;
the torque sensor is arranged on the stepping motor, and is used for measuring the torque applied by the stepping motor in real time and transmitting torque data to the microprocessor;
the position feedback device is also arranged on the stepping motor and is used for monitoring the current position of the stepping motor and transmitting the position data to the microprocessor;
The stepping motor, the torque sensor and the position feedback device are all electrically connected with the microprocessor to form a tightly integrated subsystem, and the microprocessor generates specific control signals according to torque data from the torque sensor, position data from the position feedback device and the optimized load distribution value provided by the dynamic load regulator, and the control signals are used for adjusting the operation parameters of the stepping motor so as to change the physical opening of the air inlet control valve.
4. A control system for an air compressor inlet control valve according to claim 3, further comprising:
and a data display sub-module: displaying relevant data from the dynamic load regulator and the electric driving device in real time, wherein the relevant data comprise air inlet temperature, speed and position of the electric driving device and current load distribution value;
control input submodule: the user is allowed to input or modify preset system constraint parameters, including maximum and minimum torque of the electric drive device, and safety range of air intake temperature, through a touch screen or other input device.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311239369.3A CN117212115B (en) | 2023-09-25 | 2023-09-25 | Control system of air compressor inlet control valve |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311239369.3A CN117212115B (en) | 2023-09-25 | 2023-09-25 | Control system of air compressor inlet control valve |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117212115A CN117212115A (en) | 2023-12-12 |
| CN117212115B true CN117212115B (en) | 2024-04-19 |
Family
ID=89038739
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311239369.3A Active CN117212115B (en) | 2023-09-25 | 2023-09-25 | Control system of air compressor inlet control valve |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117212115B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103573584A (en) * | 2013-09-23 | 2014-02-12 | 杭州山立净化设备股份有限公司 | Compressed air heat energy recovery and control system |
| CN206439196U (en) * | 2016-12-31 | 2017-08-25 | 德蒙（上海）压缩机械有限公司 | Air compressor machine with current limliting intake valve |
| CN112558552A (en) * | 2020-11-03 | 2021-03-26 | 中核陕西铀浓缩有限公司 | Open-loop stepping servo controller, servo control system and servo control method |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8177016B2 (en) * | 2007-01-18 | 2012-05-15 | Mack Trucks, Inc. | Hybrid internal combustion engine and air motor system and method |
| US20190154029A1 (en) * | 2017-11-17 | 2019-05-23 | Illinois Tool Works Inc. | Methods and systems for air compressor with electric inlet valve control |
-
2023
- 2023-09-25 CN CN202311239369.3A patent/CN117212115B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103573584A (en) * | 2013-09-23 | 2014-02-12 | 杭州山立净化设备股份有限公司 | Compressed air heat energy recovery and control system |
| CN206439196U (en) * | 2016-12-31 | 2017-08-25 | 德蒙（上海）压缩机械有限公司 | Air compressor machine with current limliting intake valve |
| CN112558552A (en) * | 2020-11-03 | 2021-03-26 | 中核陕西铀浓缩有限公司 | Open-loop stepping servo controller, servo control system and servo control method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117212115A (en) | 2023-12-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Mattingley et al. | Receding horizon control | |
| US9625196B2 (en) | System and method for controlling of vapor compression system | |
| KR102009450B1 (en) | Method for constant air volume control by direct power control of pm motor and hvac system applying same | |
| JP6930150B2 (en) | Control devices, control programs and control systems | |
| CN109323398B (en) | Air conditioner control method and device and air conditioner | |
| US8825184B2 (en) | Multivariable optimization of operation of vapor compression systems | |
| WO2019001378A1 (en) | Method for detecting operating power of air conditioner compressor, and air conditioner | |
| JP2014508266A (en) | Method and optimization controller for controlling operation of a vapor compression system | |
| CN110940059B (en) | Air conditioning equipment control method, device and equipment | |
| JP2015121087A (en) | Vehicle control system and method for construction machine | |
| CN108923128A (en) | A kind of electricity tune method and apparatus of antenna for base station | |
| CN117212115B (en) | Control system of air compressor inlet control valve | |
| JP2002287804A (en) | Reference model adaptive control system and reference model adaptive control method | |
| CN117311419A (en) | Temperature control method, device, equipment and readable storage medium | |
| CN111523656B (en) | Processing device and method | |
| CN108983598B (en) | PID (proportion integration differentiation) adjusting method, system and storage device | |
| Xie et al. | Design of fuzzy PID temperature control system | |
| CN112736915B (en) | Demand response control system, method, device and equipment for regional equipment cluster | |
| CN115609343A (en) | Movement magnification adjusting method and device, computer equipment and storage medium | |
| US10481598B2 (en) | Motion system with sensor outputs and haptic controls | |
| CN113448379A (en) | Method, apparatus and computer-readable storage medium for frequency modulation of a chip | |
| CN111722636A (en) | Self-balancing vehicle design scheme based on multi-innovation Kalman filtering | |
| CN113819569B (en) | Heating process APF automatic debugging control method and device, computer equipment and computer readable storage medium | |
| CN104950917A (en) | Position-self-calibration ascending and descending stage control system and control method | |
| CN114562452B (en) | Method, system, device and medium for controlling industrial equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |