CN110470039A - A kind of air conditioner water valve regulation method based on the theory of optimal control - Google Patents
A kind of air conditioner water valve regulation method based on the theory of optimal control Download PDFInfo
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- CN110470039A CN110470039A CN201910843130.4A CN201910843130A CN110470039A CN 110470039 A CN110470039 A CN 110470039A CN 201910843130 A CN201910843130 A CN 201910843130A CN 110470039 A CN110470039 A CN 110470039A
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/56—Remote control
- F24F11/58—Remote control using Internet communication
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
- F24F11/64—Electronic processing using pre-stored data
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/89—Arrangement or mounting of control or safety devices
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- Combustion & Propulsion (AREA)
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- General Engineering & Computer Science (AREA)
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Abstract
The present invention provides a kind of air conditioner water valve regulation method based on the theory of optimal control, include: S1) it acquires data and carries out curve fitting, its data acquired includes: the state variable of system, i.e. sensor temperature x (t), x (t) ∈ Rn, n is the number of sensor；The control variable of system, i.e. air conditioner water valve opening u (t), u (t) ∈ Rm, m is the number of air-conditioning；System reaches control x (t) in range of set value by setting u (t)；S2) Optimized model is established according to the data that step S1) is acquired；S3 optimum control path) is solved.System improves the efficiency of precision air conditioner within given time in this method, reduce the energy consumption of the refrigeration system relevant to water valve aperture, and the tracking error of multiple sensor temperatures and target temperature is reduced, realize the optimum control path that multiple water valves of the optimization aim are adjusted.
Description
Technical field
The present invention relates to data center machine room power management technique fields more particularly to a kind of based on the theory of optimal control
Air conditioner water valve regulation method.
Background technique
In the air-conditioning system of data center machine room, air-conditioning water inlet end water valve opening size affects the cold water for participating in heat exchange
Flow and heat exchange cooling capacity, to influence the temperature in data center machine room.The temperature that many places are arranged in data center machine room passes
The temperature of sensor induction is codetermined by the precision air conditioner for the multiple refrigerations being distributed in outside computer room.When one or more empty
When water transfer valve opening changes, the refrigerating capacity of the precision air conditioner outside computer room accordingly changes, the sensor in data center machine room
Size corresponding change of the temperature by refrigerating capacity, this is the process of a consecutive variations.Scene is, by adjusting an air conditioner water
Valve or multiple air conditioner water valve groups are closed, so that each sensor temperature maintains in reasonable range of set value in computer room, and can
It keeps stablizing in this temperature range for a long time.
Existing technology and there are the problem of it is as follows:
Influence of the air-conditioning water valve variation to computer room each point temperature is studied by way of constructing heat transfer physical model.So
And each computer room is influenced by building structure, internal gas flow variation, number of servers and rack distribution etc., it is difficult to construct accurate object
Manage model, and model parameter rule it is fixed on there is also certain difficulty.
Using discrete time control method.It is influenced in practice by the default that data store, between the data point sampling time
Too long every possibility, the system change between sampling time interval can not capture.
Usual control system assume control amount be one it is unconfined freely measure, i.e., feasible zone is whole solution spaces.However,
The hollow water transfer valve opening of the scene has threshold restriction, is generally set in theoretically between 0%-100%, in practical operation smaller
In the range of.
Summary of the invention
The invention proposes a kind of method that air-conditioner water system water valve is adjusted using the theory of optimal control, system exists
It can reach the optimization aim for improving precision air conditioner efficiency in given time, reduce the energy of refrigeration system relevant to water valve aperture
Consumption, and the tracking error of multiple sensor temperatures and target temperature is reduced, realize what multiple water valves of the optimization aim were adjusted
Optimum control path.
In order to solve the above technical problems, the technical scheme is that
A kind of air conditioner water valve regulation method based on the theory of optimal control, comprising the following steps:
S1 data) are acquired and are carried out curve fitting, the data of acquisition include:
The state variable of system, i.e. sensor temperature x (t), x (t) ∈ Rn, n is the number of sensor；
The control variable of system, i.e. air conditioner water valve opening u (t), u (t) ∈ Rm, m is the number of air-conditioning；
System reaches control x (t) in range of set value by setting u (t)；
S2) Optimized model is established according to the data that step S1) is acquired；
S3 optimum control path) is solved.
In above-mentioned technical proposal, the step S1) specifically includes the following steps:
S11) pass through experiment acquisition discrete time point data: in t=t0, t1, t2..., tnMoment, acquisition control amount u (t)
With quantity of state x (t), constantly adjustment control amount, the x (t) under different u (t) is obtained, respectively obtains u (t) and x (t) in time series
Under sample point；
S12) by the quantity of state serialization on discrete time point: in discrete sampling time point t=t0, t1, t2..., tnOn
Sensor temperature values x (t) by the method for B-spline (B-spline curves fitting), obtain the x (t) on continuous time, t ∈
[t0, tn]。
In above-mentioned technical proposal, the step S2) specifically includes the following steps:
S21, can be micro- on t) since x (t) is polynomial function, it is calculatedIt willLine is done to x (t), u (t)
Property return, t=t0, t1, t2..., tn, obtain linear system equation:
Wherein:
A and B is permanent matrix, A ∈ Rn×n, B ∈ Rn×m；
S22 Optimal Control Model) is constructed, optimization aim includes two parts:
Minimize the relevant cost function for representing refrigeration system power consumption of water valve aperture and sensor temperature and target temperature
The tracking error of degree, i.e.,
It enables
Wherein,The cost function in [0, T] period power consumption is represented,
The tracking effect in [0, T] period temperature and setting value is represented,
Represent the tracking effect of temperature and setting value in final state.
In above-mentioned technical proposal, which meets following constraint:
Original state amount: x (0)=x0；
Function of state:
Control variable threshold: g (u, t)=[u (t)-umax, umin-u(t)]T≤ t ∈ [0, T]；
Final state quantity of state constraint: a (X (T), T)=[xmin- x (T), x (T)-xmax]T≤0。
In above-mentioned technical proposal, the step S3) specifically includes the following steps:
S31 optimum control u) is found out*(t) necessary condition:
Hamilton equation is denoted as:
Lagrange's equation and work:
L [x, u, λ, μ, t]=H (x, u, λ, t)+γ g (u, t),
According to minimum theorem, the u of optimum control is realized*(t) and x*(t) it must satisfy:
λ (T)=Sx(x*(T), T)+α ax(x*(T), T), α >=0, α ax(x*(T), T)=0,
γ >=0, γ g (u*, t)=0,
a(x*(T), T)≤0；
S32) necessary condition for needing to meet according to step S31) optimum control, the method by discrete emulation are available
Optimum control path u*(t)；
According to optimal path, implement the optimum control amount u at current time*(0)；
Next sampling instant solves optimum control path at that time according to quantity of state at that time again, then implements to work as
When optimum control amount, and so on, acquire the optimum control path of t ∈ [0, T] period, but only implement apart from current time
Nearest optimum control, each sampling instant iteration optimization are avoided due to prediction and the error accumulation of phantom error bring.
Technical solution of the present invention has the advantages that
1, the invention proposes a kind of approximating methods of data-driven, to multiple air conditioner water valve openings (control amount) and sensing
Coupled relation between device temperature (quantity of state) is modeled, avoid physical modeling there are the drawbacks of, be found through experiments that, control
It is in a linear relationship between amount processed and the variable quantity of quantity of state and quantity of state, so being modeled using linear system.
2, by the method for B-spline fitting of a polynomial, by the sensor temperature timed sample sequence of discrete-time sample
It carries out serialization simulation and solves sampling interval mistake to obtain the sensor air-conditioner temperature pace of change on continuous time point
It is long, the infull problem of temperature information.
3, the threshold value constraint of air conditioner water valve opening (control amount) is considered in optimum control, i.e. optimum control amount only limits
In feasible zone, the infeasible situation of Theory Solution is avoided.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention without any creative labor, may be used also for those of ordinary skill in the art
To obtain other drawings based on these drawings.
Fig. 1 is the overall step stage schematic diagram of technical solution of the present invention；
Fig. 2 is technical solution of the present invention data modeling and solution optimal path step schematic diagram.
Specific embodiment
As depicted in figs. 1 and 2, the air conditioner water valve regulation method based on the theory of optimal control that the invention proposes a kind of, packet
Include following steps:
S1 data) are acquired and are carried out curve fitting, the data of acquisition include:
The state variable of system, i.e. sensor temperature x (t), x (t) ∈ Rn, n is the number of sensor；
The control variable of system, i.e. air conditioner water valve opening u (t), u (t) ∈ Rm, m is the number of air-conditioning；
System reaches control x (t) in range of set value by setting u (t)；
S2) Optimized model is established according to the data that step S1) is acquired；
S3 optimum control path) is solved.
In above-mentioned technical proposal, the step S1) specifically includes the following steps:
S11) pass through experiment acquisition discrete time point data: in t=t0, t1, t2..., tnMoment, acquisition control amount u (t)
With quantity of state x (t), constantly adjustment control amount, the x (t) under different u (t) is obtained, respectively obtains u (t) and x (t) in time series
Under sample point；
S12) by the quantity of state serialization on discrete time point: in discrete sampling time point t=t0, t1, t2..., tnOn
Sensor temperature values x (t) by the method for B-spline (B-spline curves fitting), obtain the x (t) on continuous time, t ∈
[t0, tn]。
In above-mentioned technical proposal, the step S2) specifically includes the following steps:
S21, can be micro- on t) since x (t) is polynomial function, it is calculatedIt willLine is done to x (t), u (t)
Property return, t=t0, t1, t2..., tn, obtain linear system equation:
Wherein:
A and B is permanent matrix, A ∈ Rn×n, B ∈ Rn×m；
S22 Optimal Control Model) is constructed, optimization aim includes two parts:
Minimize the relevant cost function for representing refrigeration system power consumption of water valve aperture and sensor temperature and target temperature
The tracking error of degree, i.e.,
It enables
Wherein,The cost function in [0, T] period power consumption is represented,
The tracking effect in [0, T] period temperature and setting value is represented,
Represent the tracking effect of temperature and setting value in final state.
In above-mentioned technical proposal, which meets following constraint:
Original state amount: x (0)=x0；
Function of state:
Control variable threshold: g (u, t)=[u (t)-umax, umin-u(t)]T≤ t ∈ [0, T]；
Final state quantity of state constraint: a (X (T), T)=[xmin- x (T), x (T)-xmax]T≤0。
In above-mentioned technical proposal, the step S3) specifically includes the following steps:
S31 optimum control u) is found out*(t) necessary condition:
Hamilton equation is denoted as:
Lagrange's equation and work:
L [x, u, λ, μ, t]=H (x, u, λ, t)+γ g (u, t),
According to minimum theorem, the u of optimum control is realized*(t) and x*(t) it must satisfy:
λ (T)=Sx(x*(T), T)+α ax(x*(T), T), α >=0, α ax(x*(T), T)=0,
γ >=0, γ g (u*, t)=0,
a(x*(T), T)≤0；
S32) necessary condition for needing to meet according to step S31) optimum control, the method by discrete emulation are available
Optimum control path u*(t)；
According to optimal path, implement the optimum control amount u at current time*(0)；
Next sampling instant solves optimum control path at that time according to quantity of state at that time again, then implements to work as
When optimum control amount, and so on, acquire the optimum control path of t ∈ [0, T] period, but only implement apart from current time
Nearest optimum control, each sampling instant iteration optimization are avoided due to prediction and the error accumulation of phantom error bring.
1, technical solution of the present invention is between multiple air conditioner water valve openings (control amount) and sensor temperature (quantity of state)
Coupled relation modeled, the temperature and temperature of computer room water valve aperture and sensor sensing are fitted using the mode of data-driven
Coupled relation between degree variation has quantified multiple water valves and has combined the influence generated to multiple sensor temperatures.
2, optimal model is constructed, to minimize power consumption and sensor by the relevant refrigeration system of water valve aperture
Temperature tracking error is target, using minimum theorem, finds out the necessary condition that optimum control path should meet.
3, by the available optimal path for meeting necessary condition of emulation mode, implement nearest apart from current point in time
Optimum control amount u*(0).Each sampled point avoids error accumulation according to current quantity of state iteration optimization later.
The above shows and describes the basic principle, main features and advantages of the invention.The technology of the industry
Personnel are it should be appreciated that the present invention is not limited to the above embodiments, and the above embodiments and description only describe this
The principle of invention, without departing from the spirit and scope of the present invention, various changes and improvements may be made to the invention, these changes
Change and improvement all fall within the protetion scope of the claimed invention.The claimed scope of the invention by appended claims and its
Equivalent thereof.
Claims (4)
1. a kind of air conditioner water valve regulation method based on the theory of optimal control, which comprises the following steps:
S1 data) are acquired and are carried out curve fitting, the data of acquisition include:
The state variable of system, i.e. sensor temperature x (t), x (t) ∈ Rn, n is the number of sensor；
The control variable of system, i.e. air conditioner water valve opening u (t), u (t) ∈ Rm, m is the number of air-conditioning；
System reaches control x (t) in range of set value by setting u (t)；
S2) Optimized model is established according to the data that step S1) is acquired；
S3 optimum control path) is solved.
2. a kind of air conditioner water valve regulation method based on the theory of optimal control according to claim 1, which is characterized in that step
Rapid S1) specifically includes the following steps:
S11) pass through experiment acquisition discrete time point data: in t=t0,t1,t2,…,tnMoment, acquisition control amount u (t) and state
It measures x (t), constantly adjustment control amount, obtains the x (t) under different u (t), respectively obtain u (t) and sample of the x (t) under time series
This point；
S12) by the quantity of state serialization on discrete time point: in discrete sampling time point t=t0,t1,t2,…,tnOn sensing
Device temperature value x (t) obtains the x (t) on continuous time, t ∈ [t by the method for B-spline0,tn]。
3. a kind of air conditioner water valve regulation method based on the theory of optimal control according to claim 1, which is characterized in that institute
State step S2) specifically includes the following steps:
S21, can be micro- on t) since x (t) is polynomial function, it is calculatedIt willLinear return is done to x (t), u (t)
Return, t=t0,t1,t2,…,tn, obtain linear system equation:
Wherein:
A and B is permanent matrix, A ∈ Rn×n, B ∈ Rn×m；
S22 Optimal Control Model) is constructed, optimization aim includes two parts:
Minimize the relevant cost function for representing refrigeration system power consumption of water valve aperture and sensor temperature and target temperature
Tracking error, i.e.,
It enables
Wherein,The cost function in [0, T] period power consumption is represented,
The tracking effect in [0, T] period temperature and setting value is represented,
Represent the tracking effect of temperature and setting value in final state.
4. a kind of air conditioner water valve regulation method based on the theory of optimal control according to claim 1, which is characterized in that institute
State step S3) specifically includes the following steps:
S31 optimum control u) is found out*(t) necessary condition:
Hamilton equation is denoted as:
Lagrange's equation and work:
L [x, u, λ, μ, t]=H (x, u, λ, t)+γ g (u, t),
According to minimum theorem, the u of optimum control is realized*(t) and x*(t) it must satisfy:
λ (T)=Sx(x*(T),T)+αax(x*(T),T),α≥0,αax(x*(T), T)=0,
γ≥0,γg(u*, t)=0,
a(x*(T),T)≤0；
S32) the necessary condition for needing to meet according to step S31) optimum control, it is available optimal by the method for discrete emulation
Control path u*(t)；
According to optimal path, implement the optimum control amount u at current time*(0)；
Next sampling instant solves optimum control path at that time again, then implements at that time according to quantity of state at that time
Optimum control amount, and so on, the optimum control path of t ∈ [0, T] period is acquired, but only implement nearest apart from current time
Optimum control, each sampling instant iteration optimization, avoid due to prediction and the error accumulation of phantom error bring.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111219848A (en) * | 2019-12-06 | 2020-06-02 | 创新奇智(青岛)科技有限公司 | Data center machine room precise air conditioner control method based on channel sensor feedback |
| CN113944993A (en) * | 2020-07-15 | 2022-01-18 | 西门子瑞士有限公司 | Maximum flow setting |
| CN114578875A (en) * | 2022-03-07 | 2022-06-03 | 杭州电子科技大学 | Device and method for realizing low-temperature work of pneumatic valve positioner |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101782261A (en) * | 2010-04-23 | 2010-07-21 | 吕红丽 | Nonlinear self-adapting energy-saving control method for heating ventilation air-conditioning system |
| CN101846355A (en) * | 2010-02-26 | 2010-09-29 | 新疆孚德节能工程有限公司 | Arrangement method based on dynamic balance unit technology in hot water heating system |
| CN102135311A (en) * | 2011-04-06 | 2011-07-27 | 华南理工大学 | Air conditioning system integral optimized control device |
| CN203824002U (en) * | 2014-04-11 | 2014-09-10 | 杭州哲达科技股份有限公司 | Optimal control system for comprehensive electricity unit consumption of refrigeration station for central air conditioner |
| CN104566868A (en) * | 2015-01-27 | 2015-04-29 | 徐建成 | Central air-conditioning control system and control method thereof |
| CN104965409A (en) * | 2015-06-19 | 2015-10-07 | 北京甘为科技发展有限公司 | Industrial circulating water system energy consumption self-learning optimization control method |
| CN106647268A (en) * | 2016-12-21 | 2017-05-10 | 东南大学 | MGT-CCHP layered optimal control system based on model prediction control |
| CN107918277A (en) * | 2017-11-13 | 2018-04-17 | 浙江大学 | A kind of plug flow tubular reactor optimal control system based on self adaptive control grid |
| US20190179269A1 (en) * | 2017-12-12 | 2019-06-13 | Distech Controls Inc. | Environment controller and method for inferring via a neural network one or more commands for controlling an appliance |
-
2019
- 2019-09-06 CN CN201910843130.4A patent/CN110470039B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101846355A (en) * | 2010-02-26 | 2010-09-29 | 新疆孚德节能工程有限公司 | Arrangement method based on dynamic balance unit technology in hot water heating system |
| CN101782261A (en) * | 2010-04-23 | 2010-07-21 | 吕红丽 | Nonlinear self-adapting energy-saving control method for heating ventilation air-conditioning system |
| CN102135311A (en) * | 2011-04-06 | 2011-07-27 | 华南理工大学 | Air conditioning system integral optimized control device |
| CN203824002U (en) * | 2014-04-11 | 2014-09-10 | 杭州哲达科技股份有限公司 | Optimal control system for comprehensive electricity unit consumption of refrigeration station for central air conditioner |
| CN104566868A (en) * | 2015-01-27 | 2015-04-29 | 徐建成 | Central air-conditioning control system and control method thereof |
| CN104965409A (en) * | 2015-06-19 | 2015-10-07 | 北京甘为科技发展有限公司 | Industrial circulating water system energy consumption self-learning optimization control method |
| CN106647268A (en) * | 2016-12-21 | 2017-05-10 | 东南大学 | MGT-CCHP layered optimal control system based on model prediction control |
| CN107918277A (en) * | 2017-11-13 | 2018-04-17 | 浙江大学 | A kind of plug flow tubular reactor optimal control system based on self adaptive control grid |
| US20190179269A1 (en) * | 2017-12-12 | 2019-06-13 | Distech Controls Inc. | Environment controller and method for inferring via a neural network one or more commands for controlling an appliance |
Non-Patent Citations (3)
| Title |
|---|
| [意]莱昂纳多.马兹尼著: "《挠性航天器动力学、控制与制导》", 30 March 2019, 国防工业出版社 * |
| 左军毅等: "《自动控制原理》", 31 March 2018, 航空工业出版社 * |
| 董超俊等: "HVAC***最优控制模型的开发及应用", 《工业仪表与自动化装置》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111219848A (en) * | 2019-12-06 | 2020-06-02 | 创新奇智(青岛)科技有限公司 | Data center machine room precise air conditioner control method based on channel sensor feedback |
| CN113944993A (en) * | 2020-07-15 | 2022-01-18 | 西门子瑞士有限公司 | Maximum flow setting |
| CN114578875A (en) * | 2022-03-07 | 2022-06-03 | 杭州电子科技大学 | Device and method for realizing low-temperature work of pneumatic valve positioner |
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