CN115128961B - Self-triggering model prediction method and system based on LoRaWAN (Long Range network) - Google Patents

Self-triggering model prediction method and system based on LoRaWAN (Long Range network) Download PDF

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CN115128961B
CN115128961B CN202211050547.3A CN202211050547A CN115128961B CN 115128961 B CN115128961 B CN 115128961B CN 202211050547 A CN202211050547 A CN 202211050547A CN 115128961 B CN115128961 B CN 115128961B
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吕亮
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Ningbo Liangkong Information Technology Co ltd
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Abstract

The invention relates to a self-triggering model prediction method and a self-triggering model prediction system based on a LoRaWAN (Long area network), which solve the problems of large calculation burden of a prediction control algorithm of a wireless network control system and high communication energy consumption of a wireless communication network, construct an extension triggering time based on a relaxation dynamic programming condition to the maximum extent based on a finite time domain value function by solving an optimal control input sequence obtained by solving a constrained finite time domain optimal control problem, and reduce the updating times of model prediction control as much as possible; meanwhile, the trigger interval is further adjusted within the self-triggering model predictive control convergence range by combining the communication protocol characteristics of the LoRaWAN network, so that the trigger interval is synchronous with the information receiving window of the controlled object, on one hand, the communication energy consumption is reduced, on the other hand, the effectiveness of the trigger interval of the self-triggering model predictive control is ensured, and thus, the calculated amount and the communication burden are reduced while the model predictive control accuracy is ensured.

Description

Self-triggering model prediction method and system based on LoRaWAN (Long Range network)
Technical Field
The invention relates to the technical field of model prediction control, in particular to a self-triggering model prediction method and system based on a LoRaWAN (LoRaWAN) network.
Background
Model Predictive Control (MPC) is a powerful constrained system optimal control technique, and the basic principle thereof is to solve an open-loop optimal control problem on line at each sampling time to obtain an optimal control sequence, and then apply a first control input element in the optimal control sequence to a controlled system.
LoRaWAN is a set of communication protocol and system architecture based on LoRa long-distance communication technology supporting design. The existing MPC does not consider the limited distribution quantity of communication, when the controlled object is not in the information receiving window, the MPC information communication fails, and the communication failure causes the control input of the controlled object to exceed the control boundary of the MPC, so that the MPC process cannot be converged, and the accuracy of model prediction control is reduced.
Disclosure of Invention
The invention solves the problem of how to reduce the calculation amount and the communication burden of model predictive control on the basis of ensuring the accuracy of the model predictive control.
In order to solve the above problems, the present invention provides a self-triggering model prediction method based on a LoRaWAN network, including:
step 1, according to the time of the controlled object
Figure 327060DEST_PATH_IMAGE002
The state input, the control input and the output of the control system construct a linear model of the control system;
step 2, constructing a constrained finite time domain optimal control problem based on a linear model of the control system, and solving the finite time domain optimal control problem to obtain the slave time
Figure 911625DEST_PATH_IMAGE002
Time of arrival
Figure 548142DEST_PATH_IMAGE004
Optimal control input sequence of
Figure 559961DEST_PATH_IMAGE006
，
Figure 383560DEST_PATH_IMAGE008
Representing time
Figure 506237DEST_PATH_IMAGE002
Is input to the state of the mobile terminal,
Figure 997261DEST_PATH_IMAGE010
representing a prediction step size;
step 3, constructing a finite time domain cost function for solving the finite time domain optimal control problem, and calculating a trigger interval based on a relaxation dynamic programming condition according to the finite time domain cost function
Figure 179981DEST_PATH_IMAGE012
，
Figure 287614DEST_PATH_IMAGE014
In order to trigger the moment of time,
Figure 213982DEST_PATH_IMAGE016
for the state at the moment of triggering, to obtain an optimal control input sequence
Figure 559513DEST_PATH_IMAGE006
The number of control input elements meeting the linear model constraint of the control system;
step 4, calculating the next trigger time based on the calculated trigger interval
Figure 647554DEST_PATH_IMAGE018
；
Step 5, judging the next trigger time according to the LoRaWAN protocol
Figure 445746DEST_PATH_IMAGE018
Whether the information is in the information receiving window of the controlled object or not, if so, entering a step 6; if not, entering step 7;
step 6, inputting the optimal control into a sequence
Figure 181664DEST_PATH_IMAGE006
Time of flight
Figure 381701DEST_PATH_IMAGE002
To the next trigger moment
Figure 906223DEST_PATH_IMAGE018
The control input element in between is sent to the controlled object and is triggered at the next moment
Figure 457290DEST_PATH_IMAGE018
Requesting the controlled object to collect and report new state input,
Figure 991040DEST_PATH_IMAGE020
，
Figure 45584DEST_PATH_IMAGE022
and returning to the step 2;
step 7, receiving window modification correction according to the information of the controlled object to obtain a new next trigger moment
Figure 741007DEST_PATH_IMAGE024
And inputting the optimal control into the sequence
Figure 717053DEST_PATH_IMAGE006
Time of flight
Figure 788915DEST_PATH_IMAGE026
To the new next trigger moment
Figure 697965DEST_PATH_IMAGE028
The control input element between is sent to the controlled object and at the new next trigger moment
Figure 95448DEST_PATH_IMAGE028
Requesting the controlled object to collect and report new state input,
Figure 89949DEST_PATH_IMAGE030
，
Figure 965501DEST_PATH_IMAGE022
and returning to the step 2;
wherein:
the linear model expression of the control system constructed in the step 1 is as follows:
Figure DEST_PATH_IMAGE032AA
Figure DEST_PATH_IMAGE034AA
in the formula (I), the compound is shown in the specification,
Figure 322533DEST_PATH_IMAGE008
representing time
Figure 625338DEST_PATH_IMAGE002
The status of (2) is input;
Figure 107135DEST_PATH_IMAGE036
representing time
Figure 724061DEST_PATH_IMAGE002
A control input of (2);
Figure 607704DEST_PATH_IMAGE038
representing time
Figure 81411DEST_PATH_IMAGE002
An output of (d);
Figure 316083DEST_PATH_IMAGE040
a parameter matrix representing a linear model of the control system;
the mathematical expression for constructing the constrained finite time domain optimal control problem in the step 2 is as follows:
Figure DEST_PATH_IMAGE042AA
s.t.
Figure 595754DEST_PATH_IMAGE044
Figure 333903DEST_PATH_IMAGE046
Figure DEST_PATH_IMAGE048AA
Figure 40828DEST_PATH_IMAGE050
Figure 700480DEST_PATH_IMAGE052
；
in the formula (I), the compound is shown in the specification,
Figure 455946DEST_PATH_IMAGE010
representing a prediction step size;
Figure 314181DEST_PATH_IMAGE054
，
Figure 129690DEST_PATH_IMAGE056
，
Figure 73375DEST_PATH_IMAGE058
a set of constraints representing the state input,
Figure 626673DEST_PATH_IMAGE060
a set of constraints representing control inputs;
Figure 339414DEST_PATH_IMAGE062
is based on time
Figure 529087DEST_PATH_IMAGE002
State input of
Figure 694489DEST_PATH_IMAGE008
And control input set
Figure 791758DEST_PATH_IMAGE064
The constructed prediction step length is a value function of N steps;
thereby obtaining the time from
Figure 890164DEST_PATH_IMAGE002
Time of arrival
Figure 47476DEST_PATH_IMAGE004
Optimal control input sequence of
Figure 700174DEST_PATH_IMAGE006
The expression is as follows:
Figure 601134DEST_PATH_IMAGE066
；
the finite time domain cost function expression constructed in the step 3 is as follows:
Figure 491730DEST_PATH_IMAGE068
；
the finite time domain cost function is used for constructing a mathematical expression based on a relaxation dynamic programming condition, wherein the mathematical expression is as follows:
Figure 819943DEST_PATH_IMAGE070
；
in the formula (I), the compound is shown in the specification,
Figure 959937DEST_PATH_IMAGE072
to trigger the moment, satisfy
Figure 664588DEST_PATH_IMAGE074
；
Figure 206428DEST_PATH_IMAGE076
(ii) a In the formula (I), the compound is shown in the specification,
Figure 705542DEST_PATH_IMAGE078
for a given scalar:
Figure 536095DEST_PATH_IMAGE080
，
Figure 778857DEST_PATH_IMAGE082
sequence of
Figure 175204DEST_PATH_IMAGE084
For the error term:
Figure 110799DEST_PATH_IMAGE086
；
due to the next moment of triggering
Figure 225385DEST_PATH_IMAGE088
Has a value of
Figure 271839DEST_PATH_IMAGE014
Is unknown, for which reason it is based on an optimal control input sequence
Figure 725954DEST_PATH_IMAGE006
Construction of Shift control sequences
Figure 566871DEST_PATH_IMAGE090
：
Figure 168753DEST_PATH_IMAGE092
；
Thereby obtaining
Figure 550056DEST_PATH_IMAGE094
Is estimated by
Figure 920995DEST_PATH_IMAGE096
：
Figure 932813DEST_PATH_IMAGE098
；
Figure 21992DEST_PATH_IMAGE100
For shifting control sequences
Figure 144669DEST_PATH_IMAGE102
Closed loop output after being applied to a controlled object;
therefore, the trigger interval is calculated based on the relaxed dynamic programming condition
Figure 370114DEST_PATH_IMAGE012
The method specifically comprises the following steps:
Figure 818412DEST_PATH_IMAGE104
；
satisfies the following conditions:
Figure DEST_PATH_IMAGE106A
for the
Figure 926046DEST_PATH_IMAGE108
Is provided with
Figure 852413DEST_PATH_IMAGE110
；
The next trigger time in step 4
Figure 203803DEST_PATH_IMAGE112
The calculation formula of (2) is as follows:
Figure 557424DEST_PATH_IMAGE114
。
the self-triggering model prediction method based on the LoRaWAN network has the beneficial effects that: an optimal control input sequence is obtained by solving a constrained finite time domain optimal control problem, and a relaxation-based dynamic programming condition is constructed based on a finite time domain cost function to use the optimal control input sequence as much as possible
Figure 355616DEST_PATH_IMAGE006
The control input element in (1) so as to prolong the triggering time to the maximum extent and reduce the updating times of model predictive control as much as possible; meanwhile, the trigger interval is further adjusted within the self-triggering model predictive control convergence range by combining the protocol characteristics of the LoRaWAN network, so that the next trigger time of the self-triggering model predictive control is matched and synchronized with the information receiving window of the controlled object, on one hand, the communication energy consumption is reduced, on the other hand, the effectiveness of the trigger interval of the self-triggering model predictive control is ensured, and therefore, the calculated amount and the communication burden are reduced while the model predictive control accuracy is ensured.
Preferably, in step 7, the new next trigger time is obtained according to the information of the controlled object and the window modification and correction
Figure 85675DEST_PATH_IMAGE028
Comprises the following steps:
Figure 285712DEST_PATH_IMAGE116
in the formula (I), wherein,
Figure 75813DEST_PATH_IMAGE118
the function is a remainder function and is a function,
Figure 361301DEST_PATH_IMAGE120
the window period duration is the message acceptance window.
Preferably, in step 7, the new next trigger time is obtained according to the information of the controlled object and the window modification and correction
Figure 363892DEST_PATH_IMAGE028
The method specifically comprises the following steps: judgment of
Figure 418436DEST_PATH_IMAGE122
Whether or not it is greater than or equal to
Figure 379439DEST_PATH_IMAGE124
If so, then
Figure 152223DEST_PATH_IMAGE126
And if not, the step (B),
Figure 958505DEST_PATH_IMAGE128
(ii) a In the formula (I), the compound is shown in the specification,
Figure 133134DEST_PATH_IMAGE130
2 is the communication time of the information acceptance window 2 of the Class B type terminal,
Figure 999459DEST_PATH_IMAGE130
1 is the communication time of the information acceptance window 1 of the Class B type terminal.
Self-triggering model prediction system based on LoRaWAN network includes:
the remote control terminal is used for executing the self-triggering model prediction method based on the LoRaWAN network, and comprises a control system module used for constructing a linear model of the control system, a computing unit used for executing the self-triggering model prediction control based on the linear model of the controlled system and solving to obtain a triggering interval, the next triggering moment and a control input sequence used for ensuring the convergence of the model prediction control, an edge control unit used for computing the control input sequence, and a LoRaWAN network communication unit used for communication, wherein the edge control unit adjusts the control input sequence based on an information receiving window and the new next triggering moment of the controlled object;
the wireless network communication module is used for storing a LoRaWAN protocol;
the system comprises at least 1 controlled object, wherein each controlled object is communicated with a remote control terminal through a wireless network communication module respectively, each controlled object comprises a LoRaWAN network/bus for establishing communication, a data processing unit, an actuator, controlled equipment and a sensor unit, the data processing unit receives a control input sequence from the remote control terminal through the LoRaWAN network/bus and sequentially sends control input elements in the control input sequence to the actuator, the actuator controls the controlled equipment to work, the sensor unit reports acquired data parameters to the remote control terminal through the wireless network communication module after the data parameters are processed by the data processing unit and serves as new state input, and the remote control terminal executes the self-triggering model prediction method based on the LoRaWAN network again based on the received state input.
The self-triggering model prediction system based on the LoRaWAN network has the advantages that: the method comprises the steps of solving a finite time domain optimal control problem through a remote control terminal to obtain an optimal control input sequence, calculating a control input sequence capable of applying a controlled object based on a relaxation dynamic programming condition through a finite time domain cost function, ensuring model prediction control to be in an effective control range, simultaneously using control input elements in the optimal control input sequence as much as possible, and reducing the calculation burden of the remote control terminal.
Drawings
FIG. 1 is a diagram illustrating that a Class A type terminal obtains a new next trigger time according to a modification in an embodiment of the present invention
Figure 259539DEST_PATH_IMAGE132
A schematic diagram;
FIG. 2 is a diagram illustrating that a Class B type terminal obtains a new next trigger time after modification according to another embodiment of the present invention
Figure 869512DEST_PATH_IMAGE132
A schematic diagram;
FIG. 3 is a diagram illustrating that a new next trigger time is obtained by modifying the preference of the Class B type terminal according to another embodiment of the present invention
Figure 898648DEST_PATH_IMAGE132
A schematic view;
fig. 4 is a system framework diagram of embodiment 2 of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
According to another embodiment provided by the present invention, the present embodiment provides a self-triggering model prediction method based on a LoRaWAN network, which is applied to a large-scale data center room cooling scenario, where the scenario includes a data center control end and multiple large data center rooms, each large data center room includes at least 600 server cabinets and a cooling system composed of at least 10 precision air conditioners, the data center control end communicates with the cooling system through the LoRaWAN network, the data center control end performs self-triggering model prediction by acquiring a state of the cooling system, and then transmits an output of the self-triggering model prediction to the cooling system through the LoRaWAN network for control; meanwhile, loRaWAN is a remote wide area network protocol and is used for communicating a plurality of controlled objects with a data center control end far away from the controlled objects at a low speed, and the self-triggering model prediction method specifically comprises the following steps:
step 1, according to the time of the controlled object
Figure 201453DEST_PATH_IMAGE002
The state input, the control input and the output of the control system construct a linear model of the control system; in this embodiment, the data center control end determines the time of the controlled object
Figure 683250DEST_PATH_IMAGE002
State input of
Figure 96914DEST_PATH_IMAGE008
Control input
Figure 980556DEST_PATH_IMAGE036
And output
Figure 454263DEST_PATH_IMAGE038
Constructing a linear model of a controlled system, wherein a controlled object is a cooling system, and the established linear model expression of the control system is as follows:
Figure DEST_PATH_IMAGE032AAA
Figure DEST_PATH_IMAGE034AAA
in the formula (I), the compound is shown in the specification,
Figure 547990DEST_PATH_IMAGE008
representing time
Figure 499765DEST_PATH_IMAGE002
Is input into the state of (a) or (b),
Figure 503493DEST_PATH_IMAGE036
representing time
Figure 148101DEST_PATH_IMAGE002
The control input of (a) is performed,
Figure 870070DEST_PATH_IMAGE038
representing time
Figure 625536DEST_PATH_IMAGE002
An output of (d);
Figure 970190DEST_PATH_IMAGE040
a parameter matrix representing a linear model of the control system, in this particular embodiment,
Figure 785699DEST_PATH_IMAGE134
the specific matrix parameters of the parameter matrix are obtained by calculating a heat exchange calculation model in the prior art; the state input comprises the temperature of each server cabinet and the energy consumption of each cabinet; the control input comprises a temperature set value and/or a set air supply quantity of the cooling system; the output includes the cooling system time
Figure 729385DEST_PATH_IMAGE002
A cost function of (a);
step 2, the data center control end constructs a constrained finite time domain optimal control problem based on a control system linear model, in the specific embodiment, sampling time is adopted
Figure 288542DEST_PATH_IMAGE002
Solving the optimal control problem of finite time domain to obtain the input sequence of the cooling system
Figure 1283DEST_PATH_IMAGE006
，
Figure 518852DEST_PATH_IMAGE008
Representing time
Figure 153096DEST_PATH_IMAGE002
Is input to the state of the mobile terminal,
Figure 578261DEST_PATH_IMAGE010
representing a prediction step size; optimal control input sequence
Figure 411088DEST_PATH_IMAGE006
The interval duration between each control input element is 30min, specifically:
the mathematical expression for constructing the constrained finite time domain optimal control problem is as follows:
Figure DEST_PATH_IMAGE042AAA
Figure DEST_PATH_IMAGE136A
Figure 161875DEST_PATH_IMAGE046
Figure DEST_PATH_IMAGE048AAA
Figure 673628DEST_PATH_IMAGE050
Figure 840167DEST_PATH_IMAGE052
；
in the formula (I), the compound is shown in the specification,
Figure 996342DEST_PATH_IMAGE010
representing a prediction step size;
Figure 324555DEST_PATH_IMAGE138
，
Figure 464549DEST_PATH_IMAGE058
a set of constraints representing the state input,
Figure 169200DEST_PATH_IMAGE060
a set of constraints representing control inputs;
Figure 711040DEST_PATH_IMAGE062
is based on time
Figure 210154DEST_PATH_IMAGE002
State input of (2)
Figure 837445DEST_PATH_IMAGE008
And control input set
Figure 80207DEST_PATH_IMAGE064
The constructed prediction step length is a value function of N steps;
obtaining the cooling system slave time based on the above solving finite time domain optimal control problem
Figure 476553DEST_PATH_IMAGE002
Time of arrival
Figure 146569DEST_PATH_IMAGE004
Optimal control input sequence of
Figure 261156DEST_PATH_IMAGE006
The expression is as follows:
Figure 47889DEST_PATH_IMAGE066
；
step 3, the data center control end constructs finite time domain price for solving finite time domain optimal control problemValue function, and calculating trigger interval based on relaxation dynamic programming condition according to finite time domain cost function
Figure 564321DEST_PATH_IMAGE012
，
Figure 405238DEST_PATH_IMAGE014
In order to trigger the moment of time,
Figure 7121DEST_PATH_IMAGE016
for the state at the moment of triggering, to obtain an optimal control input sequence
Figure 591686DEST_PATH_IMAGE006
The number of control input elements that satisfy the linear model constraint of the control system, thereby using the optimal control input sequence as much as possible
Figure 759362DEST_PATH_IMAGE006
A control input element of (1); specifically, the method comprises the following steps:
the constructed finite time domain cost function expression is as follows:
Figure 974443DEST_PATH_IMAGE068
；
the finite time domain cost function is used for constructing a mathematical expression based on a relaxation dynamic programming condition, wherein the mathematical expression is as follows:
Figure 798042DEST_PATH_IMAGE070
；
in the formula (I), the compound is shown in the specification,
Figure 186298DEST_PATH_IMAGE014
the time for solving the finite time domain optimal control problem for the triggering moment, namely the time of the data center control end meets
Figure 411743DEST_PATH_IMAGE140
；
Figure 594463DEST_PATH_IMAGE076
(ii) a In the formula (I), the compound is shown in the specification,
Figure 170938DEST_PATH_IMAGE078
for a given scalar:
Figure 97306DEST_PATH_IMAGE080
，
Figure 177257DEST_PATH_IMAGE082
sequence of
Figure 796457DEST_PATH_IMAGE142
For the error term:
Figure 594649DEST_PATH_IMAGE086
；
due to the next moment of triggering
Figure 324708DEST_PATH_IMAGE088
Has a value of
Figure 259165DEST_PATH_IMAGE014
Is unknown, for which reason it is based on an optimal control input sequence
Figure 49267DEST_PATH_IMAGE006
Construction of Shift control sequences
Figure 334755DEST_PATH_IMAGE090
：
Figure 399663DEST_PATH_IMAGE092
；
Thereby obtaining
Figure 454206DEST_PATH_IMAGE088
Is estimated by
Figure 415209DEST_PATH_IMAGE144
：
Figure 922414DEST_PATH_IMAGE146
；
Figure 994275DEST_PATH_IMAGE148
For shifting control sequences
Figure 168904DEST_PATH_IMAGE090
Closed loop output after application to a cooling system;
therefore, the trigger interval is calculated based on the relaxed dynamic programming condition
Figure 35229DEST_PATH_IMAGE012
The method specifically comprises the following steps:
Figure 295309DEST_PATH_IMAGE150
；
satisfies the following conditions:
Figure 905282DEST_PATH_IMAGE152
；
for the
Figure 934418DEST_PATH_IMAGE108
Is provided with
Figure 971644DEST_PATH_IMAGE110
；
In this embodiment, the trigger interval is calculated to be 7 units based on the dynamic relaxation planning condition, and the current trigger time is defined
Figure 713161DEST_PATH_IMAGE154
That is, the control input sequence obtained by solving the finite time domain optimal control problem at the control end of the current data center and directly applied to the cooling system is as follows:
Figure DEST_PATH_IMAGE156A
this embodiment is intended to enable the optimal control input sequence to be applied as much as possible
Figure 189142DEST_PATH_IMAGE006
The triggering interval is calculated by constructing a relaxed dynamic programming condition through a finite time domain cost function
Figure 72784DEST_PATH_IMAGE012
Thereby using as many optimal control input sequences as possible
Figure 546491DEST_PATH_IMAGE006
The control input element in (1) so as to reduce the triggering times of model predictive control of the control end of the data center and reduce the calculation burden of the control end of the data center;
step 4, calculating the time for solving the finite time domain optimal control problem at the data center control end next time based on the calculated trigger interval, namely the next trigger moment
Figure 515584DEST_PATH_IMAGE018
：
Figure 201780DEST_PATH_IMAGE114
；
This embodiment is described in detail
Figure 471087DEST_PATH_IMAGE158
；
Step 5, in order to ensure the communication reliability, the data center control end judges the next trigger moment of the communication between the cooling system and the data center control end according to the LoRaWAN protocol
Figure 850116DEST_PATH_IMAGE160
Whether the information is in the information receiving window of the controlled object or not, if so, entering a step 6; if not, entering step 7;
step 6,Data center control end inputs optimal control sequence
Figure 306505DEST_PATH_IMAGE006
Time of flight
Figure 61972DEST_PATH_IMAGE002
To the next trigger moment
Figure 920206DEST_PATH_IMAGE160
The control input element in between is sent to the cooling system, which is dependent on time
Figure 735716DEST_PATH_IMAGE002
To the next trigger moment
Figure 679401DEST_PATH_IMAGE160
The control input elements between the control input elements sequentially control the precise air conditioner to supply cold to the data center machine room according to the temperature set value and/or the air supply quantity, and the precise air conditioner supplies cold to the data center machine room at the next trigger moment
Figure 972979DEST_PATH_IMAGE160
Requesting the controlled object to collect and report new state input parameters,
Figure 685720DEST_PATH_IMAGE162
，
Figure 672130DEST_PATH_IMAGE164
and returning to the step 2;
step 7, receiving window modification correction according to the information of the controlled object to obtain a new next trigger moment
Figure 103112DEST_PATH_IMAGE028
In this embodiment, the controlled object communicates with the data center control end by using the LoRaWAN protocol, and the controlled object is used as the LoRaWAN terminal device, and may be a Class a terminal or a Class B terminal, where:
the cooling system in the context of an embodiment, communicates remotely with the data center control end via LoRaWAN protocolThe cooling system in this embodiment is a Class A type terminal, shown in FIG. 1, spaced after the transmission window
Figure 403643DEST_PATH_IMAGE130
Figure 403643DEST_PATH_IMAGE130
1, opening an information receiving window at the time, and allowing a data center control end to receive data information;
Figure DEST_PATH_IMAGE166A
in the formula (I), the compound is shown in the specification,
Figure 298787DEST_PATH_IMAGE118
the function is a remainder function and is a function,
Figure 456099DEST_PATH_IMAGE120
is the communication period of a Class A type terminal;
the Class a type terminal is adopted to reduce service energy consumption, and the Class a type terminal is adopted in the embodiment to make the calculation delay of the new next trigger time smaller in the embodiment;
the cooling system in the context of another embodiment is a Class B type terminal, as shown in FIG. 2, spaced after the transmission window
Figure 374376DEST_PATH_IMAGE130
Figure 374376DEST_PATH_IMAGE130
1 time-open message acceptance window 1, interval after the transmission window
Figure 213019DEST_PATH_IMAGE130
2, opening an information receiving window 2 at the time, and allowing data information to be received from the data center control end at the two information receiving windows;
modifying and correcting to obtain new next trigger time
Figure 900352DEST_PATH_IMAGE028
As shown in fig. 2:
Figure 494145DEST_PATH_IMAGE116
；
in the formula (I), the compound is shown in the specification,
Figure 634139DEST_PATH_IMAGE120
receiving a window period duration for the information;
more preferably, when
Figure 338790DEST_PATH_IMAGE122
Is greater than or equal to
Figure 83892DEST_PATH_IMAGE124
Then, the next trigger time is preferably corrected to the start time of the message acceptance window 2, as shown in fig. 3:
Figure 583007DEST_PATH_IMAGE126
；
in the formula (I), the compound is shown in the specification,
Figure 210297DEST_PATH_IMAGE130
2 is the communication time of the information acceptance window 2,
Figure 534618DEST_PATH_IMAGE130
1 is the communication time of the information receiving window 1;
in the embodiment, a Class B type terminal is adopted, the number of information receiving windows is increased, a control input sequence can be applied to a controlled object to the maximum extent on the premise of self-triggering model prediction control convergence, and the number of times of self-touch model prediction is reduced;
cooling system based on slave time
Figure 196543DEST_PATH_IMAGE026
To the new next trigger moment
Figure 132138DEST_PATH_IMAGE168
The control input elements between the control input elements sequentially control the precision air conditioner to supply cold to the data center machine room according to the temperature set value and/or the air supply quantity, and the control input elements control the precision air conditioner to supply cold to the data center machine room at the next new trigger moment
Figure 449987DEST_PATH_IMAGE168
Requesting the controlled object to collect and report new state input,
Figure 230861DEST_PATH_IMAGE170
，
Figure 12872DEST_PATH_IMAGE164
and returns to step 2.
Meanwhile, in this embodiment, the data center control end of the specific packet data of the sending window in fig. 1 and fig. 2 sends a request communication to the cooling system, and the cooling system sends the acquired state input to the data center control end for calculation; the information acceptance window in fig. 1 and 2 is used for the data center control to transmit the calculated control input elements to the cooling system.
In addition, the next triggering time for the cooling system to communicate with the data center control end in step 5 of the present embodiment is determined
Figure 853789DEST_PATH_IMAGE160
The method for judging whether the object is in the information receiving window of the controlled object is
Figure 455672DEST_PATH_IMAGE122
If equal to 0, if yes, the next trigger moment
Figure 57815DEST_PATH_IMAGE160
Is in the information receiving window, otherwise, the next trigger moment
Figure 428754DEST_PATH_IMAGE160
Not within the information acceptance window;
in addition, in order to ensure communication and facilitate calculation, the correction trigger time in this embodiment is the start time of the calculation information receiving window.
In the embodiment, the communication between the data center control end and the cooling system is further adjusted and controlled by combining the LoRaWAN protocol, so that the data center control end executes the self-triggering model predictive control and always converges, and meanwhile, the new next triggering time is obtained by correcting the self-triggering model predictive control convergence range according to a Class A type terminal or a Class B type terminal by combining the protocol characteristics of the LoRaWAN network, so that the new next triggering time of the self-triggering model predictive control is matched and synchronized with the information receiving window of the controlled object, and the communication energy consumption is greatly reduced.
According to another embodiment of the present invention, as shown in fig. 4, in a specific embodiment 2, a self-triggered model prediction system based on a LoRaWAN network is provided, and in this specific embodiment, the self-triggered model prediction system based on a LoRaWAN network is applied to a large-scale data center room cooling scenario, where the large-scale data center room cooling scenario includes a data center control end and multiple large data center rooms, each large data center room includes at least 600 server cabinets and a cooling system composed of at least 10 precision air conditioners; wherein:
the remote control terminal, that is, the data center control terminal in this embodiment, is configured to execute a self-triggering model prediction method based on a LoRaWAN network, where the remote control terminal includes a control system module, a computing unit, an edge control unit, and a LoRaWAN network communication unit;
the control system module is used for constructing a control system module of a linear model of the control system; in the embodiment, the control system module constructs a linear system model according to a cooling system in a data center machine room;
the computing unit executes self-triggering model prediction control based on a controlled system linear model and solves to obtain a triggering interval and the next triggering moment; in this embodiment, the computing unit first solves the optimal control problem of the finite time domain to obtain the optimal control input sequence
Figure 643834DEST_PATH_IMAGE006
(ii) a Next, a finite time domain cost function is constructed
Figure 467434DEST_PATH_IMAGE172
And based on finite time-domain cost functions
Figure 855690DEST_PATH_IMAGE172
Constructing a dynamic planning condition based on relaxation:
Figure 346714DEST_PATH_IMAGE174
(ii) a The trigger interval is then calculated based on the relaxed dynamic programming conditions:
Figure 529434DEST_PATH_IMAGE176
(ii) a And solving through the trigger interval to obtain the time for the data center control end to execute the model predictive control next time, namely the next trigger moment:
Figure 105909DEST_PATH_IMAGE114
(ii) a Further obtaining a control input sequence which can make the model prediction control converge;
the edge control unit is used for further adjusting a control input sequence capable of enabling model predictive control to be converged according to an information receiving window by using a LoRaWAN protocol so as to enable the control input sequence to be synchronous with the information receiving window of the controlled object; on one hand, communication energy consumption is reduced, and on the other hand, the effectiveness of the trigger interval of the self-triggering model predictive control is ensured, so that the accuracy of the model predictive control is ensured, and meanwhile, the calculated amount and the communication burden are reduced;
the data center control end sends the adjusted control input sequence to the cooling system through the LoRaWAN network communication unit through the wireless network communication module;
the wireless network communication module is used for storing a LoRaWAN protocol;
at least 1 controlled object, namely, a cooling system in a data center machine room in this embodiment, each cooling system communicates with a data center control end through a wireless network communication module, and the cooling system includes a LoRaWAN network/bus for establishing communication, a data processing unit, an actuator, a controlled device, and a sensor unit; wherein:
the data processing unit receives a control input sequence from a remote control terminal through a LoRaWAN network/bus and sequentially sends control input elements in the control input sequence to an actuator; the control input elements of the embodiment comprise a temperature set value and/or a set air supply quantity of the precise air conditioner, and the actuator controls the precise air conditioner to work and supply cold according to the control input elements until the last control input element is sent;
the actuator controls the controlled equipment to work, and the controlled equipment in the specific embodiment is a precise air conditioner forming a cooling system;
the sensor unit comprises a temperature sensor for sensing the temperature of the server cabinet and a current transformer for calculating the energy consumption of the server cabinet, the sensor unit takes the collected temperature data and the collected current energy consumption as state input, the state input is processed by the data processing unit and reported to a control system module of the remote control terminal as new state input through the wireless network communication module, and the remote control terminal executes the self-triggering model prediction method based on the LoRaWAN network again based on the received new state input.
At each triggering moment, the sensor unit collects new state input and reports the new state input to the remote control terminal through the LoRaWAN network communication unit, the remote control terminal solves the finite time domain optimal control problem to obtain an optimal control input sequence, the control input sequence which can be used for a controlled object is calculated based on a relaxation dynamic programming condition through a finite time domain cost function, control input elements in the optimal control input sequence are used as much as possible while model prediction control is guaranteed to be in an effective control range, the calculation burden of the remote control terminal is reduced, meanwhile, the remote control terminal adjusts the control input sequence through a type terminal of the controlled object and sends the adjusted control input sequence to the controlled object, the next triggering moment of the remote control terminal is matched and synchronized with an information receiving window of the controlled object, on one hand, the communication energy consumption is reduced, on the other hand, the effectiveness of a triggering interval of self-triggering model prediction control is guaranteed, and on the other hand, the calculation amount and the communication burden are reduced while the accuracy of model prediction control is guaranteed.
Although the present disclosure has been described above, the scope of the present disclosure is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present disclosure, and such changes and modifications will fall within the scope of the present invention.

Claims (4)

1. A self-triggering model prediction method based on a LoRaWAN (Long area network) is characterized by comprising the following steps:
step 1, according to the time of the controlled object
Figure DEST_PATH_IMAGE002
The state input, the control input and the output of the control system construct a linear model of the control system;
step 2, constructing a constrained finite time domain optimal control problem based on a linear model of the control system, and solving the finite time domain optimal control problem to obtain the slave time
Figure 719476DEST_PATH_IMAGE002
Arrival time
Figure DEST_PATH_IMAGE004
Optimal control input sequence of
Figure DEST_PATH_IMAGE006
，
Figure DEST_PATH_IMAGE008
Representing time
Figure 210632DEST_PATH_IMAGE002
Is input to the state of the mobile terminal,
Figure DEST_PATH_IMAGE010
representing a prediction step size;
step 3, constructing a finite time domain cost function for solving the finite time domain optimal control problem, and calculating a trigger interval based on a relaxation dynamic programming condition according to the finite time domain cost function
Figure DEST_PATH_IMAGE012
，
Figure DEST_PATH_IMAGE014
In order to trigger the moment of time,
Figure DEST_PATH_IMAGE016
for the state at the moment of triggering, to obtain an optimum control input sequence
Figure 192625DEST_PATH_IMAGE006
The number of control input elements meeting the linear model constraint of the control system;
step 4, triggering interval based on calculation
Figure 311891DEST_PATH_IMAGE012
Calculating to obtain the next trigger time
Figure DEST_PATH_IMAGE018
；
Step 5, judging the next trigger moment according to the LoRaWAN protocol
Figure 314613DEST_PATH_IMAGE018
Whether the information is in the information receiving window of the controlled object or not, if so, entering a step 6; if not, entering step 7;
step 6, inputting the optimal control into a sequence
Figure 417698DEST_PATH_IMAGE006
Time of flight
Figure 452651DEST_PATH_IMAGE002
To the next trigger moment
Figure 692002DEST_PATH_IMAGE018
The control input element in between is sent to the controlled object and is triggered at the next moment
Figure 255839DEST_PATH_IMAGE018
Requesting the controlled object to collect and report new state input,
Figure DEST_PATH_IMAGE020
，
Figure DEST_PATH_IMAGE022
and returning to the step 2;
step 7, receiving window modification correction according to the information of the controlled object to obtain a new next trigger moment
Figure DEST_PATH_IMAGE024
And inputting the optimal control into the sequence
Figure 450147DEST_PATH_IMAGE006
Time of flight
Figure DEST_PATH_IMAGE026
To the new next trigger moment
Figure 960894DEST_PATH_IMAGE024
The control input element in between is sent to the controlled object and at the new next trigger moment
Figure 789173DEST_PATH_IMAGE024
Requesting the controlled object to collect and report new state input,
Figure DEST_PATH_IMAGE028
，
Figure 461594DEST_PATH_IMAGE022
and returning to the step 2;
wherein:
the linear model expression of the control system constructed in the step 1 is as follows:
Figure DEST_PATH_IMAGE030
Figure DEST_PATH_IMAGE032
in the formula (I), the compound is shown in the specification,
Figure 290004DEST_PATH_IMAGE008
representing time
Figure 401179DEST_PATH_IMAGE002
The status of (2) is input;
Figure DEST_PATH_IMAGE034
representing time
Figure 287227DEST_PATH_IMAGE002
A control input of (a);
Figure DEST_PATH_IMAGE036
representing time
Figure 661708DEST_PATH_IMAGE002
An output of (d);
Figure DEST_PATH_IMAGE038
a parameter matrix representing a linear model of the control system;
the mathematical expression for constructing the constrained finite time domain optimal control problem in the step 2 is as follows:
Figure DEST_PATH_IMAGE040
s.t.
Figure DEST_PATH_IMAGE042
Figure DEST_PATH_IMAGE044
Figure DEST_PATH_IMAGE046
Figure DEST_PATH_IMAGE048
Figure DEST_PATH_IMAGE050
；
in the formula (I), the compound is shown in the specification,
Figure 50183DEST_PATH_IMAGE010
representing a prediction step size;
Figure DEST_PATH_IMAGE052
，
Figure DEST_PATH_IMAGE054
a set of constraints representing the state input,
Figure DEST_PATH_IMAGE056
a set of constraints representing control inputs;
Figure DEST_PATH_IMAGE058
is based on time
Figure 778098DEST_PATH_IMAGE002
State input of
Figure 846548DEST_PATH_IMAGE008
And control input set
Figure DEST_PATH_IMAGE060
Value function with N steps as prediction step lengthCounting;
thereby obtaining the time from
Figure 391930DEST_PATH_IMAGE002
Arrival time
Figure 178621DEST_PATH_IMAGE004
Optimal control input sequence of
Figure 631599DEST_PATH_IMAGE006
The expression is as follows:
Figure DEST_PATH_IMAGE062
；
the finite time domain cost function expression constructed in the step 3 is as follows:
Figure DEST_PATH_IMAGE064
；
the finite time domain cost function is used for constructing a mathematical expression based on a relaxation dynamic programming condition, wherein the mathematical expression is as follows:
Figure DEST_PATH_IMAGE066
；
in the formula (I), the compound is shown in the specification,
Figure 424062DEST_PATH_IMAGE014
to trigger the moment, satisfy
Figure DEST_PATH_IMAGE068
；
Figure DEST_PATH_IMAGE070
(ii) a In the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE072
for a given scalar:
Figure DEST_PATH_IMAGE074
，
Figure DEST_PATH_IMAGE076
sequence of
Figure DEST_PATH_IMAGE078
To the error term:
Figure DEST_PATH_IMAGE080
；
due to the next moment of triggering
Figure DEST_PATH_IMAGE082
Has a value of
Figure 563182DEST_PATH_IMAGE014
Is unknown, for which reason it is based on an optimal control input sequence
Figure 837169DEST_PATH_IMAGE006
Construction of Shift control sequences
Figure DEST_PATH_IMAGE084
：
Figure DEST_PATH_IMAGE086
；
Thereby obtaining
Figure 578991DEST_PATH_IMAGE082
Is estimated value of
Figure DEST_PATH_IMAGE088
：
Figure DEST_PATH_IMAGE090
；
Figure DEST_PATH_IMAGE092
For shifting control sequences
Figure 718240DEST_PATH_IMAGE084
Closed loop output after being applied to a controlled object;
therefore, the trigger interval is calculated based on the relaxed dynamic programming condition
Figure 402162DEST_PATH_IMAGE012
The method specifically comprises the following steps:
Figure DEST_PATH_IMAGE094
；
satisfies the following conditions:
Figure DEST_PATH_IMAGE096
for
Figure DEST_PATH_IMAGE098
Is provided with
Figure DEST_PATH_IMAGE100
；
The next trigger time in the step 4
Figure 648598DEST_PATH_IMAGE018
The calculation formula of (2) is as follows:
Figure DEST_PATH_IMAGE102
。
2. the LoRaWAN network-based self-triggering model prediction method of claim 1, wherein the step 7 is performed according to a controlled basisThe modification and correction of the information receiving window of the object to obtain the new next trigger time
Figure 912220DEST_PATH_IMAGE024
Comprises the following steps:
Figure DEST_PATH_IMAGE104
in the formula (I), the compound is shown in the specification,
Figure DEST_PATH_IMAGE106
the function is a remainder function and is a function,
Figure DEST_PATH_IMAGE108
the window period duration is the message acceptance window.
3. The self-triggering model prediction method based on LoRaWAN network according to claim 2, characterized in that in step 7, the window is modified and corrected according to the information of the controlled object to obtain a new next triggering time
Figure 419556DEST_PATH_IMAGE024
The method specifically comprises the following steps:
judgment of
Figure DEST_PATH_IMAGE110
Whether or not it is greater than or equal to
Figure DEST_PATH_IMAGE112
If so, then
Figure DEST_PATH_IMAGE114
And if not, the step (B),
Figure DEST_PATH_IMAGE116
(ii) a Wherein 2 is the communication time of the information acceptance window 2 of the Class B type terminal,
Figure DEST_PATH_IMAGE118
1 is the communication time of the information acceptance window 1 of the Class B type terminal.
4. A self-triggering model prediction system based on a LoRaWAN (Long Range network) is characterized by comprising:
a remote control terminal for executing the self-triggering model prediction method based on the LoRaWAN network according to any one of claims 1-3, wherein the remote control terminal includes a control system module for constructing a linear model of the control system, a computing unit for performing the self-triggering model prediction control based on the linear model of the controlled system and solving to obtain a triggering interval, a next triggering time and a control input sequence for ensuring convergence of the model prediction control, an edge control unit for computing the control input sequence, the edge control unit adjusting the control input sequence based on an information acceptance window and a new next triggering time of the controlled object, and a LoRaWAN network communication unit for communication;
the wireless network communication module is used for storing a LoRaWAN protocol;
the system comprises at least 1 controlled object, wherein each controlled object is communicated with a remote control terminal through a wireless network communication module respectively, each controlled object comprises a LoRaWAN network/bus for establishing communication, a data processing unit, an actuator, controlled equipment and a sensor unit, the data processing unit receives a control input sequence from the remote control terminal through the LoRaWAN network/bus and sequentially sends control input elements in the control input sequence to the actuator, the actuator controls the controlled equipment to work, the sensor unit reports acquired data parameters to the remote control terminal through the wireless network communication module after the data parameters are processed by the data processing unit and serves as new state input, and the remote control terminal executes the self-triggering model prediction method based on the LoRaWAN network again based on the received state input.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114423020A (en) * 2022-01-21 2022-04-29 温州大学乐清工业研究院 LoRaWAN network downlink route control method and system
CN114600127A (en) * 2019-09-10 2022-06-07 辉达公司 Architecture searching method based on machine learning for neural network
CN114662850A (en) * 2022-02-22 2022-06-24 大连海事大学 Electric energy prediction distribution system based on LoRaWAN network cloud monitoring
CN114746872A (en) * 2020-04-28 2022-07-12 辉达公司 Model predictive control techniques for autonomous systems

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160170429A1 (en) * 2014-12-15 2016-06-16 King Fahd University Of Petroleum And Minerals System and method for non-linear model predictive control of multi-machine power systems
CN111708277B (en) * 2020-05-29 2021-10-01 中国科学技术大学 Adaptive time domain event trigger model prediction control method

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114600127A (en) * 2019-09-10 2022-06-07 辉达公司 Architecture searching method based on machine learning for neural network
CN114746872A (en) * 2020-04-28 2022-07-12 辉达公司 Model predictive control techniques for autonomous systems
CN114423020A (en) * 2022-01-21 2022-04-29 温州大学乐清工业研究院 LoRaWAN network downlink route control method and system
CN114662850A (en) * 2022-02-22 2022-06-24 大连海事大学 Electric energy prediction distribution system based on LoRaWAN network cloud monitoring

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
基于LoRa无线通信的变电站火灾报警系统设计;赵志浩等;《电子与封装》;20220731;第22卷(第7期);第070501-1-070501-4页 *

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