CN109189049B - Active time delay compensation method suitable for networked control system - Google Patents

Active time delay compensation method suitable for networked control system Download PDF

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CN109189049B
CN109189049B CN201811196434.8A CN201811196434A CN109189049B CN 109189049 B CN109189049 B CN 109189049B CN 201811196434 A CN201811196434 A CN 201811196434A CN 109189049 B CN109189049 B CN 109189049B
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赵云波
韩康
黄涛
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Zhejiang University of Technology ZJUT
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    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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Abstract

An active time delay compensation method for a multi-sensor networked control system comprises the following steps: 1) data of a plurality of sensors and control signals actually used by the actuators are sent to the controller through a single communication channel; 2) the controller receives and judges whether the acquired controller signals are missing, if the controller signals are not missing, the controller signals are directly calculated according to a control algorithm, and if signals of partial sensors are missing, system reconstruction is firstly carried out; 3) the controller makes different control sequences according to the completeness of the signal, and then packs the control signal into a data packet to be sent out; 4) the control signal selector selects an optimal control signal among the control sequences to be applied to the actuator, and transmits the selected control signal to the controller through the communication channel.

Description

Active time delay compensation method suitable for networked control system
Technical Field
The invention relates to an active time delay compensation method for a multi-sensor networked control system.
Background
With the development of computer technology, logistics network technology and network communication technology, the combination of the traditional control system and communication technology is more and more compact, and a networked control system is formed. Since the networked control system can reduce the wiring cost of the traditional control system and has the advantages of scalability, easy control and monitoring, etc., the networked control system has become a new development direction in the field of industrial automatic control.
The networked control systems differ from the traditional control systems in some particularities of the structure of the networked control systems themselves. The communication channel from the sensor to the controller or from the controller to the actuator is a communication network, which includes not only the common CAN and DN but also a communication network in a broad sense, such as the internet. The introduction of the communication network enables remote data among sensors and controllers and between the controllers and actuators in the control system to be possible, which brings the advantages of easy maintenance, convenient expansion and the like to the control system. The communication network, however, introduces other problems as well as the aforementioned convenience for the control system. In the networked control system, real-time data exchange between a sensor and a controller and between the controller and an actuator is carried out through a network, the data are transmitted in the form of data packets in the network, and random time delay and loss phenomena of the data packets can be caused when congestion, faults and routing errors occur in a data packet 'transfer station' -network node. The time lag and loss of the data packet can affect the rapidity and accuracy of the control system and finally affect the stability of the system.
Some methods and techniques applied in the field of networked control systems have solved to some extent the impact of delay and packet loss on the control system. However, with the development of the internet of everything trend, in some practical application scenarios, data of a plurality of different sensors may need to be read at the same time, and when the data of the sensors are distributed in different geographic locations, the conventional single sensor has completely failed to meet the requirement of such a networked control system. For such systems, it is necessary to use multiple sensor nodes to transmit sensor data to the controller node using different communication channels, respectively. For the problems in such new scenarios, a new method is needed to solve.
In a networked control system with a plurality of sensors, the time required for each sensor node to send sensor data to a controller node is not necessarily equal, so that the time delay from each sensor node to the controller is independent, and how to reduce the influence of the time delay on the control system has no solution so far.
Disclosure of Invention
The present invention overcomes the above-mentioned shortcomings of the prior art, and provides an active compensation method suitable for a multi-sensor networked control system.
An active time delay compensation method for a multi-sensor networked control system comprises the following steps:
step 1) data of a plurality of sensors and control signals actually used by an actuator are sent to a controller through a single communication channel;
due to geographical isolation, the sensors are distributed in different places, and data sent from the sensors to the controller cannot be packaged into one data packet, so that the data packets have to be sent to the controller through separate communication channels, more than one communication channel exists between the sensors and the controller, and each separate channel can ensure normal sending and receiving of the data packet;
step 2) the state reconstruction process of the system, specifically, the ① controller receives and judges whether the acquired controller signal is missing, if ② is not missing, the step 3) is directly carried out to calculate the control quantity, if the signal of a part of sensors is missing, the system reconstruction is carried out first, and then the control quantity is calculated;
the specific system reconstruction method is described in conjunction with the following linear system
x(k+1)＝Ax(k)+Bu(k) (2-1)
Wherein x ∈ Rn,u∈Rn,A∈Rn×n,B∈Rn×m；
Introducing a system matrix A and a system matrix B into (2-1), splitting A and B into block matrixes of r × r and r × 1, wherein r represents the number of sensors, and the specific process is as follows:
Figure GDA0002386389080000021
where A isij1,2, r is ni×njDimension of (A), BiIs niDimension x m; the matrix is partitioned and a mathematical description of each sensor data update is written:
Figure GDA0002386389080000022
due to the k-tau of the system at time ksc,kAll system state information at the time is available, so a mathematical description of the sensor data prediction step can be written in the form:
Figure GDA0002386389080000031
where k ≧ k0，k0，yjIs the state information that was saved by the system at the last time,
Figure GDA0002386389080000032
the controller can obtain the information of each sensor by predicting the lost sensor data; thereby obtaining the complete state quantity of the system
Figure GDA0002386389080000033
Including measured and estimated values;
as can be seen from (2-3) and (2-4),
Figure GDA0002386389080000034
comprises two parts, when the measured value of the system sent by the sensor is available, the actual measured state of the system is used for defining yi(k-τsc,k) If the actual measured value of the system is not complete, the measured value plus the predicted value is used as the system state of the controller for the next calculation, and the following formula is used to represent the system state without ambiguity
Figure GDA0002386389080000035
Figure GDA0002386389080000036
Further, a state matrix of the system can be obtained
Figure GDA0002386389080000037
Step 3) the controller according to the system state
Figure GDA0002386389080000038
Calculating the control signal, packaging the control signal into a data packet and sending out the data packet;
a controller according to
Figure GDA0002386389080000039
Predicting a control quantity of the system, wherein
Figure GDA00023863890800000310
Indicating that the system calculated at time k
Figure GDA00023863890800000311
The control sequence of the time of day is,
Figure GDA00023863890800000312
representing the feedback gain obtained by the system at time k,
Figure GDA00023863890800000313
is the state matrix of the system.
The specific calculation process is as follows:
using a model-based predictive control Method (MPC) to calculate the controllable signal, firstly constructing an objective function before calculation, and further calculating the controller according to the objective function; the objective function is expressed as follows (3-1):
Figure GDA00023863890800000314
here, the
Figure GDA00023863890800000315
Is the objective function of the system at time k,
Figure GDA00023863890800000316
is systematicThe control signals are represented as follows:
Figure GDA0002386389080000041
for pre-measurement of system state trajectory
Figure GDA0002386389080000042
To represent
Figure GDA0002386389080000043
Q, R in the formula (3-1) is the weighting matrix of the system, NpIs a prediction time domain, NuIs the prediction time domain.
Step 4) the control signal selector selects an optimal control signal from the control sequences to act on the actuator, and the selected control signal is sent to the controller through a communication channel;
when the actuator receives
Figure GDA0002386389080000044
Then selecting a control quantity from the control sequence to act on the actuator according to the time delay from the current controller to the actuator; the selection process of the actuator selection control sequence is represented as follows:
Figure GDA0002386389080000045
Figure GDA0002386389080000046
representing the sum of the time delay from the sensor to the controller and the time delay from the controller to the actuator.
The main advantages of the invention are as follows: the data of each sensor can be obtained by a model prediction method, and considering that communication constraint can affect the system, the data packet-based method is used for active compensation, so that the convergence speed of the system is improved, and the system has smaller stability margin.
Drawings
FIG. 1 is a representation of a networked control system with multiple sensors communicating data packets implementing the method of the present invention.
FIG. 2 is a flow chart of the present invention.
Fig. 3 is a representation of the time delay from the sensor to the controller.
Fig. 4 is a representation of the time delay from the controller to the actuator.
Fig. 5 is a representation of a control signal selector selecting a control signal.
FIG. 6 is a state simulation diagram of a system using the method.
Detailed Description
The invention will be further described with reference to the accompanying drawings, which are intended to illustrate how the invention may be applied in practical applications and to solve problems in practice by means of the invention, said method comprising the steps of:
step 1) data of a plurality of sensors and control signals actually used by an actuator are sent to a controller through a single communication channel;
due to geographical isolation, the sensors are distributed in different places, and data sent from the sensors to the controller cannot be packaged into one data packet, so that the data packets have to be sent to the controller through separate communication channels, more than one communication channel exists between the sensors and the controller, and each separate channel can ensure normal sending and receiving of the data packet;
step 2) the state reconstruction process of the system, specifically, the ① controller receives and judges whether the acquired controller signal is missing, if ② is not missing, the step 3) is directly carried out to calculate the control quantity, if the signal of a part of sensors is missing, the system reconstruction is carried out first, and then the control quantity is calculated;
the specific system reconstruction method is described in conjunction with the following linear system
x(k+1)＝Ax(k)+Bu(k) (2-1)
Wherein x ∈ Rn,u∈Rn,A∈Rn×n,B∈Rn×m；
A system matrix A and B are introduced into the formula (2-1), the A and B are split into block matrixes of r multiplied by r and r multiplied by 1, r represents the number of sensors, and the specific process is as follows:
Figure GDA0002386389080000051
where A isij1,2, r is ni×njDimension of (A), BiIs niDimension x m; the matrix is partitioned and a mathematical description of each sensor data update is written:
Figure GDA0002386389080000052
due to the k-tau of the system at time ksc,kAll system state information at the time is available, so a mathematical description of the sensor data prediction step can be written in the form:
Figure GDA0002386389080000053
where k ≧ k0，k0，yjIs the state information that was saved by the system at the last time,
Figure GDA0002386389080000061
the controller can obtain the information of each sensor by predicting the lost sensor data; thereby obtaining the complete state quantity of the system
Figure GDA0002386389080000062
Including measured and estimated values;
as is apparent from the formulae (2-3) and (2-4),
Figure GDA0002386389080000063
comprises two parts, when the measured value of the system sent by the sensor is available, the system is usedActual measurement state definition of the System yi(k-τsc,k) If the actual measured value of the system is not complete, the measured value plus the predicted value is used as the system state of the controller for the next calculation, and the following formula is used to represent the system state without ambiguity
Figure GDA0002386389080000064
Figure GDA0002386389080000065
Further, a state matrix of the system can be obtained
Figure GDA0002386389080000066
Step 3) the controller according to the system state
Figure GDA0002386389080000067
Calculating the control signal, packaging the control signal into a data packet and sending out the data packet;
a controller according to
Figure GDA0002386389080000068
Predicting a control quantity of the system, wherein
Figure GDA0002386389080000069
Indicating that the system calculated at time k
Figure GDA00023863890800000610
The control sequence of the time of day is,
Figure GDA00023863890800000611
representing the feedback gain obtained by the system at time k,
Figure GDA00023863890800000612
is the state matrix of the system.
The specific calculation process is as follows:
using a model-based predictive control Method (MPC) to calculate the controllable signal, firstly constructing an objective function before calculation, and further calculating the controller according to the objective function; the objective function is expressed as shown in the following formula (3-1):
Figure GDA00023863890800000613
here, the
Figure GDA00023863890800000614
Is the objective function of the system at time k, where Q, R is the weighting matrix of the system,
Figure GDA00023863890800000615
it is the control signals of the system that are expressed as follows:
Figure GDA0002386389080000071
for pre-measurement of system state trajectory
Figure GDA0002386389080000072
To represent
Figure GDA0002386389080000073
NpIs a prediction time domain, NuIs the control time domain.
Step 4) the control signal selector selects an optimal control signal from the control sequences to act on the actuator, and the selected control signal is sent to the controller through a communication channel; the selection process is described as follows:
when the actuator receives
Figure GDA0002386389080000074
Then selecting a control quantity from the control sequence to act on the actuator according to the time delay from the current controller to the actuator; selection procedure representation of actuator selection control sequenceThe following were used:
Figure GDA0002386389080000075
Figure GDA0002386389080000076
representing the sum of the time delay from the sensor to the controller and the time delay from the controller to the actuator.
The embodiments described in this specification are merely illustrative of implementations of the inventive concept and the scope of the present invention should not be considered limited to the specific forms set forth in the embodiments but rather by the equivalents thereof as may occur to those skilled in the art upon consideration of the present inventive concept.

Claims (1)

1. An active time delay compensation method for a multi-sensor networked control system comprises the following steps:
step 1) data of a plurality of sensors and control signals actually used by an actuator are sent to a controller through a single communication channel;
due to geographical isolation, the sensors are distributed in different places, and data sent from the sensors to the controller cannot be packaged into one data packet, so that the data packets have to be sent to the controller through separate communication channels, more than one communication channel exists between the sensors and the controller, and each separate channel can ensure normal sending and receiving of the data packet;
step 2) the state reconstruction process of the system, specifically, the ① controller receives and judges whether the acquired controller signal is missing, if ② is not missing, the step 3) is directly carried out to calculate the control quantity, if the signal of a part of sensors is missing, the system reconstruction is carried out first, and then the control quantity is calculated;
the specific system reconstruction method is described in conjunction with the following linear system
x(k+1)＝Ax(k)+Bu(k) (2-1)
Wherein x ∈ Rn,u∈Rn,A∈Rn×n,B∈Rn×m；
A system matrix A and B are introduced into the formula (2-1), the A and B are split into block matrixes of r multiplied by r and r multiplied by 1, r represents the number of sensors, and the specific process is as follows:
Figure FDA0002386389070000011
where A isij1,2, r is ni×njDimension of (A), BiIs niDimension x m; the matrix is partitioned and a mathematical description of each sensor data update is written:
Figure FDA0002386389070000012
due to the k-tau of the system at time ksc,kAll system state information at the time is available, so a mathematical description of the sensor data prediction step can be written in the form:
Figure FDA0002386389070000013
where k ≧ k0，k0，yjIs the state information that was saved by the system at the last time,
Figure FDA0002386389070000021
the controller can obtain the information of each sensor by predicting the lost sensor data; thereby obtaining the complete state quantity of the system
Figure FDA0002386389070000022
Including measured and estimated values;
as is apparent from the formulae (2-3) and (2-4),
Figure FDA0002386389070000023
comprises two parts, when the measured value of the system sent by the sensor is available, the actual measured state of the system is used for defining yi(k-τsc,k) If the actual measured value of the system is not complete, the measured value plus the predicted value is used as the system state of the controller for the next calculation, and the following formula is used to represent the system state without ambiguity
Figure FDA0002386389070000024
Figure FDA0002386389070000025
Further, a state matrix of the system can be obtained
Figure FDA0002386389070000026
Step 3) the controller according to the system state
Figure FDA0002386389070000027
Calculating the control signal, packaging the control signal into a data packet and sending out the data packet;
a controller according to
Figure FDA0002386389070000028
Predicting a control quantity of the system, wherein
Figure FDA0002386389070000029
Indicating that the system calculated at time k
Figure FDA00023863890700000210
The control sequence of the time of day is,
Figure FDA00023863890700000211
representing the feedback gain obtained by the system at time k,
Figure FDA00023863890700000212
is the state matrix of the system.
The specific calculation process is as follows:
using a model-based predictive control Method (MPC) to calculate the controllable signal, firstly constructing an objective function before calculation, and further calculating the controller according to the objective function; the objective function is expressed as shown in the following formula (3-1):
Figure FDA00023863890700000213
here, the
Figure FDA00023863890700000214
Is the objective function of the system at time k, Q, R in equation (3-1) is the weighting matrix of the system,
Figure FDA00023863890700000215
it is the control signals of the system that are expressed as follows:
Figure FDA00023863890700000216
for pre-measurement of system state trajectory
Figure FDA0002386389070000031
To represent
Figure FDA0002386389070000032
NpIs a prediction time domain, NuIs the control time domain.
Step 4) the control signal selector selects an optimal control signal from the control sequences to act on the actuator, and the selected control signal is sent to the controller through a communication channel;
when the actuator receives
Figure FDA0002386389070000033
Then selecting a control quantity from the control sequence to act on the actuator according to the time delay from the current controller to the actuator; the selection process of the actuator selection control sequence is represented as follows:
Figure FDA0002386389070000034
Figure FDA0002386389070000035
representing the sum of the time delay from the sensor to the controller and the time delay from the controller to the actuator.
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