CN111025913A - Networked predictive control method and system for engineering control - Google Patents

Networked predictive control method and system for engineering control Download PDF

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CN111025913A
CN111025913A CN201911360356.5A CN201911360356A CN111025913A CN 111025913 A CN111025913 A CN 111025913A CN 201911360356 A CN201911360356 A CN 201911360356A CN 111025913 A CN111025913 A CN 111025913A
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CN111025913B (en
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庞中华
刘国平
董哲
史运涛
孙德辉
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North China University of Technology
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    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
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Abstract

The embodiment of the invention provides a networked predictive control method and a networked predictive control system for engineering control. The method comprises the following steps: caching the output data in the sensor and packaging the output data with the timestamp to obtain feedback data of the sensor and sending the feedback data to the controller; and calculating a control quantity predicted value based on an input and output model of a controlled object to obtain a control quantity predicted value sequence, packaging the control quantity predicted value sequence and the time stamp and sending the control quantity predicted value sequence to an actuator, and selecting a preset control signal by the actuator according to the time stamp to actively compensate the system feedback channel random network delay and the system forward channel random network delay. The embodiment of the invention has the advantages that the stability and the tracking performance of the closed-loop networked control system are unrelated to the communication constraints of random network induced time delay, data packet disorder, loss and the like in the feedback channel and the forward channel by setting, so that the design and the implementation of the control system are facilitated, the good tracking performance can be ensured, the control structure is simple, the implementation is easy, and the popularization is facilitated.

Description

Networked predictive control method and system for engineering control
Technical Field
The invention relates to the technical field of engineering control, in particular to a networked predictive control method and a networked predictive control system for engineering control.
Background
With the rapid development of industrialization and informatization, a communication network is gradually developed to be fully and deeply integrated with various industrial control systems by the aid of automation-oriented auxiliary action, so that a plurality of networked control systems are generated. Compared with the traditional point-to-point control system, the networked control system has many advantages, such as: the system design and installation are simplified, the system cost and energy consumption are reduced, resource sharing and remote control are facilitated, and the flexibility, reliability, mobility and the like of the system are enhanced. Therefore, the networked control system has been widely applied to various fields of national economy and national defense construction, such as: process control, aerospace, traffic management, power production, device manufacturing, robotic control, telemedicine, unmanned aerial vehicles, automotive electronics, smart home, and the like. With the advent of the internet + intelligence era, it is expected that networked control systems will appear in more and wider fields.
However, due to the limitation of network bandwidth and other conditions, the network itself may bring many disadvantages to the control system, such as: network induced delays, packet misordering and loss, etc., which can lead to system performance degradation and even instability. Therefore, extensive and intensive research is conducted on the networked system with the communication constraint, and a great deal of research results are obtained, while relatively few research is conducted on the output tracking control problem of the networked system, and the conventional method has the following defects: 1) some methods treat the random network induced delay and the continuous data packet loss number as the fixed network induced delay and the continuous data packet loss number according to the maximum upper bound, and have strong conservatism; 2) some methods are designed for communication constraints such as random network induced delay, data packet disorder and loss, but the control performance of some methods also fluctuates randomly. Due to the limitation, the application and popularization of the conventional networked output tracking control method in actual engineering are greatly limited.
Disclosure of Invention
The embodiment of the invention provides a networked predictive control method and a networked predictive control system for engineering control, which are used for solving the defects of larger limitation, unstable control performance and the like when the problems of network induced delay, data packet disorder, loss and the like are solved in a networked control system in the prior art.
In a first aspect, an embodiment of the present invention provides a networked predictive control method for engineering control, including:
caching output data of a controlled object in a sensor, packaging the output data and a timestamp to obtain feedback data of the sensor, and sending the feedback data to a controller;
calculating a control quantity predicted value based on the input and output model of the controlled object, the feedback data, the system reference input data and the historical control quantity according to the system feedback channel random network delay and the system forward channel random network delay to obtain a control quantity predicted value sequence, and packaging the control quantity predicted value sequence and the timestamp to send to an actuator;
and selecting a preset control signal by the actuator according to the timestamp, and actively compensating the system feedback channel random network delay and the system forward channel random network delay by the preset control signal.
Preferably, the input-output model includes:
A(z-1)y(k)＝B(z-1)u(k-1)
wherein k represents the current time, y (k) epsilon R represents the output data, and u (k) epsilon R represents the system control input data; a (z)-1) And B (z)-1) Respectively, the following polynomials:
Figure BDA0002337017470000021
wherein n isaAnd nbRespectively represent a polynomial A (z)-1) And B (z)-1) The order of (a).
Preferably, the system feedback channel has a random network delay of
Figure BDA0002337017470000022
The system forward channel random network time delay is
Figure BDA0002337017470000023
Wherein
Figure BDA0002337017470000024
And is
Figure BDA0002337017470000025
And
Figure BDA0002337017470000026
are all integers;
correspondingly, if
Figure BDA0002337017470000027
Then the system control input data is represented as:
u(k)＝F(z-1)u(k-1)+G(z-1)e(k)
where e (k) is the output tracking error, defined as follows:
e(k)＝r(k)-y(k)
F(z-1) And G (z)-1) Respectively, the following polynomials:
Figure BDA0002337017470000031
wherein n isfAnd ngAre respectively a polynomial F (z)-1) And G (z)-1) The order of (a).
Preferably, the sequence corresponding to the output data includes:
Y(k)＝[y(k)y(k-1)…y(k-n)]
where n is max { n ═ na-1,ng}。
Preferably, the calculating a control quantity predicted value based on the input/output model, the feedback data, the system reference input data and the historical control quantity according to the system feedback channel random network delay and the system forward channel random network delay to obtain a control quantity predicted value sequence, and packing and sending the control quantity predicted value sequence and the timestamp to the actuator specifically includes:
based on the latest feedback data from the sensors
Figure BDA0002337017470000032
Calculating a control quantity predicted value
Figure BDA0002337017470000033
And corresponding control quantity predicted value sequence
Figure BDA0002337017470000034
Packed with the timestamp k and sent to the actuator through a system forward channel, wherein
Figure BDA0002337017470000035
The latest timestamp from the sensor is subtracted from the current time of the controller.
Preferably, the calculating a control quantity predicted value based on the input/output model, the feedback data, the system reference input data and the historical control quantity according to the system feedback channel random network delay and the system forward channel random network delay to obtain a control quantity predicted value sequence, and packing and sending the control quantity predicted value sequence and the timestamp to the actuator further includes:
at the current time k, the system outputs a predicted value of:
Figure BDA0002337017470000036
in the formula (I), the compound is shown in the specification,
Figure BDA0002337017470000037
A1(z-1)＝1-A(z-1) And when i-j is less than or equal to 0:
Figure BDA0002337017470000038
wherein j is 1,2, …, na；
The preset control signal is
Figure BDA0002337017470000039
The values at time are:
Figure BDA00023370174700000310
in the formula (I), the compound is shown in the specification,
Figure BDA00023370174700000311
wherein j is 0,1, …, ngThe control quantity predicted value sequence corresponding to the preset control signal at the moment k is as follows:
Figure BDA0002337017470000041
preferably, the selecting, by the actuator, a preset control signal according to the timestamp, and actively compensating, by the preset control signal, the system feedback channel random network delay and the system forward channel random network delay, specifically include:
based on the latest control quantity predicted value sequence from the controller
Figure BDA0002337017470000042
From which to select
Figure BDA0002337017470000043
A control quantity, i.e. u (k), and applying u (k) to the controlled object to actively compensate the random network delay of the forward channel of the system, wherein
Figure BDA0002337017470000044
The latest timestamp from the sensor is subtracted from the current time of the actuator.
In a second aspect, an embodiment of the present invention provides a networked predictive control system for engineering control, including:
the cache module is used for caching output data of a controlled object in a sensor, packaging the output data and a timestamp to obtain feedback data of the sensor, and sending the feedback data to the controller;
the control module is used for calculating a control quantity predicted value by the controller by adopting a predictive control algorithm according to the system feedback channel random network delay and the system forward channel random network delay based on the input and output model of the controlled object, the feedback data, the system reference input data and the historical control quantity to obtain a control quantity predicted value sequence, and packaging and sending the control quantity predicted value sequence and the timestamp to the actuator;
and the compensation module is used for selecting a preset control signal by the actuator according to the timestamp, and actively compensating the system feedback channel random network delay and the system forward channel random network delay by the preset control signal.
In a third aspect, an embodiment of the present invention provides an electronic device, including:
the computer program comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the steps of any one networked predictive control method for engineering control when executing the program.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer readable storage medium, on which a computer program is stored, the computer program, when being executed by a processor, implementing any one of the steps of the networked predictive control method for engineering control.
According to the networked predictive control method and system for engineering control, provided by the embodiment of the invention, the stability and tracking performance of the closed-loop networked control system are set to be irrelevant to the communication constraints such as random network induced time delay, data packet disorder and loss in the feedback channel and the forward channel, so that the design and implementation of the control system are facilitated, good tracking performance can be ensured, and the control structure is simple, easy to implement and convenient to popularize.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a block diagram of a control scheme provided by an embodiment of the present invention;
fig. 2 is a flowchart of a networked predictive control method for engineering control according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a random network delay in a system feedback channel according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a random network delay in a forward channel of a system according to an embodiment of the present invention;
fig. 5 is a diagram illustrating a local control effect of the system without random network delay according to an embodiment of the present invention;
FIG. 6 is a diagram illustrating the effect of the system with random network delay without compensation according to an embodiment of the present invention;
FIG. 7 is a diagram illustrating the effect of the system with random network delay and compensation according to an embodiment of the present invention;
fig. 8 is a structural diagram of a networked predictive control system for engineering control according to an embodiment of the present invention;
fig. 9 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to overcome the defects in the prior art, the embodiments of the present invention fully utilize the packet transmission characteristic of the communication network, and provide a networked predictive control method to actively compensate for random network-induced delay, data packet disorder and loss existing in the feedback channel and the forward channel of the networked system. The random network induced time delay, the data packet disorder and the loss in the feedback channel and the forward channel are respectively and uniformly processed into the random network time delay of the respective channels. In order to compensate the random network delay in the feedback channel and the forward channel, the control scheme of the invention mainly comprises three modules: the device comprises a data buffer module, a prediction controller module and a time delay compensator module. The overall scheme block diagram of the system is shown in figure 1.
Fig. 2 is a flowchart of a networked predictive control method for engineering control according to an embodiment of the present invention, as shown in fig. 2, including:
s1, caching output data of a controlled object in a sensor, packaging the output data and a timestamp to obtain feedback data of the sensor, and sending the feedback data to a controller;
s2, calculating a control quantity predicted value based on the input/output model of the controlled object, the feedback data, the system reference input data and the historical control quantity according to the system feedback channel random network delay and the system forward channel random network delay to obtain a control quantity predicted value sequence, and packaging the control quantity predicted value sequence and the time stamp to send to an actuator;
and S3, selecting a preset control signal by the actuator according to the timestamp, and actively compensating the system feedback channel random network delay and the system forward channel random network delay by the preset control signal.
In particular, the amount of the solvent to be used,
in step S1, the data buffer module in fig. 1 is disposed in the sensor, and buffers the output data of the controlled object, and packages the output data with the timestamp to obtain the feedback data of the sensor, and sends the feedback data to the controller;
in step S2, the predictive controller module in fig. 1 is configured in the controller, and calculates a control quantity predicted value based on the input/output model of the controlled object and the prediction control method by using the feedback data from the sensor (i.e., the output data of the controlled object and the timestamp thereof), the system reference input signal, and the historical control quantity stored in the predictive controller, and according to the upper bound of the real-time network delay of the feedback channel and the random network delay of the forward channel, and packing a group of control quantity predicted value sequences and the timestamp thereof to send to the actuator;
in step S3, the delay compensator module in fig. 1 is disposed in the actuator, and buffers the latest predicted control sequence sent from the controller, and selects a control signal applied to the controlled object according to the timestamp, so as to actively compensate the random network delay in the feedback channel and the forward channel.
The embodiment of the invention has the advantages that the stability and the tracking performance of the closed-loop networked control system are unrelated to the communication constraints of random network induced time delay, data packet disorder, loss and the like in the feedback channel and the forward channel by setting, so that the design and the implementation of the control system are facilitated, the good tracking performance can be ensured, the control structure is simple, the implementation is easy, and the popularization is facilitated.
Based on the above embodiment, the input-output model includes:
A(z-1)y(k)＝B(z-1)u(k-1)
wherein k represents the current time, y (k) epsilon R represents the output data, and u (k) epsilon R represents the system control input data; a (z)-1) And B (z)-1) Respectively, the following polynomials:
Figure BDA0002337017470000071
wherein n isaAnd nbRespectively represent a polynomial A (z)-1) And B (z)-1) The order of (a).
Specifically, embodiments of the present invention consider a controlled object described by a linear system as follows:
A(z-1)y(k)＝B(z-1)u(k-1) (1)
where k represents the current time, and y (k) e R and u (k) e R are the measurement outputs of the system, respectivelyOut and control inputs, A (z)-1) And B (z)-1) Respectively, the following polynomials:
Figure BDA0002337017470000072
wherein n isaAnd nbAre respectively polynomial A (z)-1) And B (z)-1) The order of (a).
Based on any of the above embodiments, the system feedback channel has a random network delay of
Figure BDA0002337017470000073
The system forward channel random network time delay is
Figure BDA0002337017470000074
Wherein
Figure BDA0002337017470000075
And is
Figure BDA0002337017470000076
And
Figure BDA0002337017470000077
are all integers;
correspondingly, if
Figure BDA0002337017470000078
Then the system control input data is represented as:
u(k)＝F(z-1)u(k-1)+G(z-1)e(k)
where e (k) is the output tracking error, defined as follows:
e(k)＝r(k)-y(k)
F(z-1) And G (z)-1) Respectively, the following polynomials:
Figure BDA0002337017470000081
wherein n isfAnd ngAre respectively polynomialF(z-1) And G (z)-1) The order of (a).
Specifically, assume that the random network delays in the system feedback path and the forward path are respectively
Figure BDA0002337017470000082
And
Figure BDA0002337017470000083
wherein
Figure BDA0002337017470000084
And
Figure BDA0002337017470000085
are integers. If it is
Figure BDA0002337017470000086
And
Figure BDA0002337017470000087
then, it means that there is no network delay in the control system, i.e. local control, and at this time, the following feedback control law may be designed for the system (1):
u(k)＝F(z-1)u(k-1)+G(z-1)e(k) (2)
where e (k) is the output tracking error, defined as follows:
e(k)＝r(k)-y(k) (3)
F(z-1) And G (z)-1) Respectively, the following polynomials:
Figure BDA0002337017470000088
wherein n isfAnd ngAre respectively a polynomial F (z)-1) And G (z)-1) The order of (2), the parameters in the formula (2), can be designed by using the traditional control theory, such as a PID control algorithm, according to the specific controlled object and the control performance requirement.
Further, consider random network latency
Figure BDA0002337017470000089
And
Figure BDA00023370174700000810
based on the control law (2), a networked prediction control scheme is designed, so that the system outputs y (k) tracking reference input signals R (k) epsilon R, namely networked output tracking control. To achieve the above object, the embodiments of the present invention make the following assumptions:
assume that 1: the system sensor, the controller and the actuator are all time-driven, and clocks are synchronous;
assume 2: the random network delay is bounded, i.e. satisfied
Figure BDA00023370174700000811
And
Figure BDA00023370174700000812
wherein
Figure BDA00023370174700000813
And
Figure BDA00023370174700000814
is an integer;
assume that 3: data packets transmitted in the network are accompanied by time stamps.
Based on any of the above embodiments, the sequence corresponding to the output data includes:
Y(k)＝[y(k)y(k-1)…y(k-n)]
where n is max { n ═ na-1,ng}。
Specifically, the design of the data buffer is specifically as follows:
the data buffer is arranged in the sensor, and in order to actively compensate the disorder and loss of data packets in the feedback channel of the system, the following output data sequences of the controlled object are buffered:
Y(k)＝[y(k)y(k-1)…y(k-n)](4)
where n is max { n ═ na-1,ng}. At each sampling instant, the sensor packages the data sequence y (k) and its time stamp k to the controller.
Based on any of the above embodiments, the calculating a control quantity predicted value based on the input/output model, the feedback data, the system reference input data, and the historical control quantity according to the system feedback channel random network delay and the system forward channel random network delay to obtain a control quantity predicted value sequence, and packing and sending the control quantity predicted value sequence and the timestamp to the actuator specifically includes:
based on the latest feedback data from the sensors
Figure BDA0002337017470000091
Calculating a control quantity predicted value
Figure BDA0002337017470000092
And corresponding control quantity predicted value sequence
Figure BDA0002337017470000093
Packed with the timestamp k and sent to the actuator through a system forward channel, wherein
Figure BDA0002337017470000094
The latest timestamp from the sensor is subtracted from the current time of the controller.
The method includes the steps of calculating a control quantity predicted value based on the input/output model, the feedback data, system reference input data and historical control quantity according to system feedback channel random network delay and system forward channel random network delay to obtain a control quantity predicted value sequence, packaging the control quantity predicted value sequence and the timestamp, and sending the control quantity predicted value sequence and the timestamp to an actuator, and further includes the steps of:
at the current time k, the system outputs a predicted value of:
Figure BDA0002337017470000095
in the formula (I), the compound is shown in the specification,
Figure BDA0002337017470000096
A1(z-1)＝1-A(z-1) And when i-j is less than or equal to 0:
Figure BDA0002337017470000097
wherein j is 1,2, …, na；
The preset control signal is
Figure BDA0002337017470000098
The values at time are:
Figure BDA0002337017470000099
in the formula (I), the compound is shown in the specification,
Figure BDA00023370174700000910
wherein j is 0,1, …, ngThe control quantity predicted value sequence corresponding to the preset control signal at the moment k is as follows:
Figure BDA00023370174700000911
in particular, the predictive controller is arranged in the controller, the main task of which is based on the latest feedback data received from the sensors
Figure BDA00023370174700000912
Calculating a control quantity predicted value
Figure BDA00023370174700000913
And predicting the sequence of the control quantity
Figure BDA0002337017470000101
And its corresponding timestamp k, are packed and sent to the actuators via the forward path. In a predictive controller, feedback path random network delay
Figure BDA0002337017470000102
Can be reduced by the controller at the current momentTo the latest timestamp received from the sensor.
At the current time k, using equation (1), the following system output prediction value can be obtained:
Figure BDA0002337017470000103
in the formula (I), the compound is shown in the specification,
Figure BDA0002337017470000104
A1(z-1)＝1-A(z-1) And when i-j is less than or equal to 0,
Figure BDA0002337017470000105
wherein j is 1,2, …, na；
Using equation (2), the system is obtained
Figure BDA0002337017470000106
The control signals at the moment are:
Figure BDA0002337017470000107
in the formula (I), the compound is shown in the specification,
Figure BDA0002337017470000108
wherein j is 0,1, …, ng；
At each sampling instant, the predictive controller will predict a sequence of control quantities as follows:
Figure BDA0002337017470000109
and its timestamp k is packed to the executor.
Based on any of the embodiments, the selecting, by the actuator, a preset control signal according to the timestamp, and actively compensating, by the preset control signal, the system feedback channel random network delay and the system forward channel random network delay specifically include:
based on the latest control quantity predicted value sequence from the controller
Figure BDA00023370174700001010
From which to select
Figure BDA00023370174700001011
A control quantity, i.e. u (k), and applying u (k) to the controlled object to actively compensate the random network delay of the forward channel of the system, wherein
Figure BDA00023370174700001012
The latest timestamp from the sensor is subtracted from the current time of the actuator.
Specifically, a network delay compensator is arranged in the actuator and used for receiving the latest control quantity predicted value sequence from the controller at each sampling moment
Figure BDA00023370174700001013
From which to select
Figure BDA00023370174700001014
A control quantity, i.e. u (k), applied to the controlled object to actively compensate the random network delay in the forward channel of the system, wherein
Figure BDA00023370174700001015
The latest timestamp received from the controller may be subtracted from the current time of the actuator.
Based on any one of the above embodiments, the embodiment of the present invention performs numerical simulation verification on the control scheme of the present invention by using MATLAB software, and considers that, in a certain brushless dc motor system, the output is the motor speed, the input is the driving voltage, and when the sampling time is 0.05s, the transfer function model is:
Figure BDA0002337017470000111
designing the following control law for the transfer function model:
u(k)＝u(k-1)+0.005e(k)-0.0042e(k-1)
in simulation, the initial input and output of the motor are all 0, the reference input is a square wave signal, the random network time delay in a system feedback channel is 2-5 steps, and the random network time delay in a forward channel is 1-4 steps, namely
Figure BDA0002337017470000112
As shown in fig. 3 and 4, respectively.
The simulation is divided into the following 3 cases: 1) no network delay; 2) network delay but no compensation; 3) there is network delay and compensation.
Fig. 5 shows the local control result of the motor system without random network delay, and it can be seen from fig. 5 that the speed output of the motor can well track the time-varying reference input.
Fig. 6 shows the control result of the motor system with random network delay but without compensation, and the random network delays in the feedback channel and the forward channel are respectively shown in fig. 3 and fig. 4, and it can be seen from fig. 6 that the random network delay in the networked system finally causes divergence of the system compared with the local control result (fig. 5).
Fig. 7 shows that, compared with the control result of the uncompensated networked system (fig. 6), the output tracking performance of the compensated networked system is greatly improved under the same influence of the random network delay, even approaching to the local control effect without the random network delay (fig. 5), and if the initial state of the motor system is a steady state, the control scheme of the embodiment of the invention can achieve the control effect completely same as the local control. It can be seen that the control scheme of the embodiments of the present invention is effective.
Fig. 8 is a structural diagram of a networked predictive control system for engineering control according to an embodiment of the present invention, as shown in fig. 8, including: a buffer module 81, a control module 82 and a compensation module 83; wherein:
the cache module 81 is configured to cache output data of a controlled object in a sensor, package the output data and a timestamp to obtain feedback data of the sensor, and send the feedback data to a controller; the control module 82 is configured to calculate a control quantity predicted value by the controller according to a system feedback channel random network delay and a system forward channel random network delay based on the input/output model of the controlled object, the feedback data, the system reference input data, and the historical control quantity, and by using a predictive control algorithm, obtain a control quantity predicted value sequence, and package the control quantity predicted value sequence and the timestamp to send to the actuator; the compensation module 83 is configured to select a preset control signal by the actuator according to the timestamp, and actively compensate the system feedback channel random network delay and the system forward channel random network delay by the preset control signal.
The system provided by the embodiment of the present invention is used for executing the corresponding method, the specific implementation manner of the system is consistent with the implementation manner of the method, and the related algorithm flow is the same as the algorithm flow of the corresponding method, which is not described herein again.
The embodiment of the invention has the advantages that the stability and the tracking performance of the closed-loop networked control system are unrelated to the communication constraints of random network induced time delay, data packet disorder, loss and the like in the feedback channel and the forward channel by setting, so that the design and the implementation of the control system are facilitated, the good tracking performance can be ensured, the control structure is simple, the implementation is easy, and the popularization is facilitated.
Fig. 9 illustrates a physical structure diagram of an electronic device, and as shown in fig. 9, the electronic device may include: a processor (processor)910, a communication Interface (Communications Interface)920, a memory (memory)930, and a communication bus 940, wherein the processor 910, the communication Interface 920, and the memory 930 communicate with each other via the communication bus 940. Processor 910 may invoke logic instructions in memory 930 to perform the following method: caching output data of a controlled object in a sensor, packaging the output data and a timestamp to obtain feedback data of the sensor, and sending the feedback data to a controller; calculating a control quantity predicted value based on the input and output model of the controlled object, the feedback data, the system reference input data and the historical control quantity according to the system feedback channel random network delay and the system forward channel random network delay to obtain a control quantity predicted value sequence, and packaging the control quantity predicted value sequence and the timestamp to send to an actuator; and selecting a preset control signal by the actuator according to the timestamp, and actively compensating the system feedback channel random network delay and the system forward channel random network delay by the preset control signal.
Furthermore, the logic instructions in the memory 930 may be implemented in software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented to perform the transmission method provided in the foregoing embodiments when executed by a processor, and for example, the method includes: caching output data of a controlled object in a sensor, packaging the output data and a timestamp to obtain feedback data of the sensor, and sending the feedback data to a controller; calculating a control quantity predicted value based on the input and output model of the controlled object, the feedback data, the system reference input data and the historical control quantity according to the system feedback channel random network delay and the system forward channel random network delay to obtain a control quantity predicted value sequence, and packaging the control quantity predicted value sequence and the timestamp to send to an actuator; and selecting a preset control signal by the actuator according to the timestamp, and actively compensating the system feedback channel random network delay and the system forward channel random network delay by the preset control signal.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. A networked predictive control method for engineering control, comprising:
caching output data of a controlled object in a sensor, packaging the output data and a timestamp to obtain feedback data of the sensor, and sending the feedback data to a controller;
calculating a control quantity predicted value based on the input and output model of the controlled object, the feedback data, the system reference input data and the historical control quantity according to the system feedback channel random network delay and the system forward channel random network delay to obtain a control quantity predicted value sequence, and packaging the control quantity predicted value sequence and the timestamp to send to an actuator;
and selecting a preset control signal by the actuator according to the timestamp, and actively compensating the system feedback channel random network delay and the system forward channel random network delay by the preset control signal.
2. The networked predictive control method for engineering control of claim 1, wherein the input-output model comprises:
A(z-1)y(k)＝B(z-1)u(k-1)
wherein k represents the current time, y (k) epsilon R represents the output data, and u (k) epsilon R represents the system control input data; a (z)-1) And B (z)-1) Respectively, the following polynomials:
Figure FDA0002337017460000011
wherein n isaAnd nbRespectively represent a polynomial A (z)-1) And B (z)-1) The order of (a).
3. The networked predictive control method for engineering control of claim 2, wherein the system feedbackChannel random network delay of
Figure FDA0002337017460000012
The system forward channel random network time delay is
Figure FDA0002337017460000013
Wherein
Figure FDA0002337017460000014
And is
Figure FDA0002337017460000015
And
Figure FDA0002337017460000016
are all integers;
correspondingly, if
Figure FDA0002337017460000017
Then the system control input data is represented as:
u(k)＝F(z-1)u(k-1)+G(z-1)e(k)
where e (k) is the output tracking error, defined as follows:
e(k)＝r(k)-y(k)
F(z-1) And G (z)-1) Respectively, the following polynomials:
Figure FDA0002337017460000021
wherein n isfAnd ngAre respectively a polynomial F (z)-1) And G (z)-1) The order of (a).
4. The networked predictive control method for engineering control of claim 3, wherein the corresponding sequence of output data includes:
Y(k)＝[y(k) y(k-1)…y(k-n)]
where n is max { n ═ na-1,ng}。
5. The networked predictive control method for engineering control according to claim 4, wherein the step of calculating a control quantity predicted value based on the input/output model, the feedback data, the system reference input data, and the historical control quantity according to a system feedback channel random network delay and a system forward channel random network delay to obtain a control quantity predicted value sequence, and packing the control quantity predicted value sequence and the timestamp to send the control quantity predicted value sequence to an actuator specifically includes:
based on the latest feedback data from the sensors
Figure FDA0002337017460000022
Calculating a control quantity predicted value
Figure FDA0002337017460000023
And corresponding control quantity predicted value sequence
Figure FDA0002337017460000024
Packed with the timestamp k and sent to the actuator through a system forward channel, wherein
Figure FDA0002337017460000025
The latest timestamp from the sensor is subtracted from the current time of the controller.
6. The networked predictive control method for engineering control according to claim 5, wherein the step of calculating a control quantity predicted value based on the input/output model, the feedback data, system reference input data and a historical control quantity according to a system feedback channel random network delay and a system forward channel random network delay to obtain a control quantity predicted value sequence, and packing the control quantity predicted value sequence and the timestamp to send the control quantity predicted value sequence to an actuator further comprises the steps of:
at the current time k, the system outputs a predicted value of:
Figure FDA0002337017460000026
wherein, i is 1,2, …,
Figure FDA0002337017460000027
A1(z-1)＝1-A(z-1) And when i-j is less than or equal to 0:
Figure FDA0002337017460000028
wherein j is 1,2, …, na；
The preset control signal is
Figure FDA0002337017460000029
The values at time are:
Figure FDA0002337017460000031
in the formula (I), the compound is shown in the specification,
Figure FDA0002337017460000032
wherein j is 0,1, …, ngThe control quantity predicted value sequence corresponding to the preset control signal at the moment k is as follows:
Figure FDA0002337017460000033
7. the networked predictive control method for engineering control of claim 6, wherein the selecting, by the actuator, a preset control signal according to the timestamp, and the actively compensating, by the preset control signal, the system feedback channel random network delay and the system forward channel random network delay comprise:
based on information from the controlLatest control quantity predicted value sequence of device
Figure FDA0002337017460000034
From which to select
Figure FDA0002337017460000035
A control quantity, i.e. u (k), and applying u (k) to the controlled object to actively compensate the random network delay of the forward channel of the system, wherein
Figure FDA0002337017460000036
The latest timestamp from the sensor is subtracted from the current time of the actuator.
8. A networked predictive control system for engineering control, comprising:
the cache module is used for caching output data of a controlled object in a sensor, packaging the output data and a timestamp to obtain feedback data of the sensor, and sending the feedback data to the controller;
the control module is used for calculating a control quantity predicted value by the controller by adopting a predictive control algorithm according to the system feedback channel random network delay and the system forward channel random network delay based on the input and output model of the controlled object, the feedback data, the system reference input data and the historical control quantity to obtain a control quantity predicted value sequence, and packaging and sending the control quantity predicted value sequence and the timestamp to the actuator;
and the compensation module is used for selecting a preset control signal by the actuator according to the timestamp, and actively compensating the system feedback channel random network delay and the system forward channel random network delay by the preset control signal.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the networked predictive control method for engineering control according to any one of claims 1 to 7.
10. A non-transitory computer readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the networked predictive control method for engineering control according to any one of claims 1 to 7.
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