CN107561937B - Event-driven-based lamp networking control method - Google Patents

Event-driven-based lamp networking control method Download PDF

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CN107561937B
CN107561937B CN201710765685.2A CN201710765685A CN107561937B CN 107561937 B CN107561937 B CN 107561937B CN 201710765685 A CN201710765685 A CN 201710765685A CN 107561937 B CN107561937 B CN 107561937B
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state
estimator
measurement
event
time
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CN107561937A (en
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唐文明
彭力
唐贤
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MINSTAR OPTOELECTRONICS TECHNOLOGY (KUNSHAN) CO LTD
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MINSTAR OPTOELECTRONICS TECHNOLOGY (KUNSHAN) CO LTD
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Abstract

The invention relates to a lamp networking control method based on event driving, which uses simple parameterization of a quadratic approximation function of a relevant Markov decision process to obtain a sampling and estimation strategy based on events, thereby minimizing the upper limit of the performance of a class of systems and being effectively applied to the calculation of the strategy of a system with a high-dimensional state space.

Description

Event-driven-based lamp networking control method
Technical Field
The invention relates to a lamp lighting technology, in particular to an area lamp lighting technology, and particularly shows a lamp networking control method based on event driving.
Background
In public places, such as offices, classrooms, meeting rooms, parks, underground garages and the like, when light needs exist, regional control is often needed, and the light is turned on only in places where people or vehicles appear, so that the purposes of energy conservation and high-efficiency utilization are achieved. These actions with occasional temporary occurrences we refer to as an event.
In event-based control, the system is activated or the control signal is changed only when certain events occur. For example, the control signal may be applied only if some of the measurements deviate beyond a state of equilibrium of the system. Thus, control actions are applied only when needed, while reducing the speed at which the system must be detected and started, thereby maintaining good control performance.
In principle, the question of determining how best to arrange the sensing or activation of the system can be taken as a decision process by markov. However, these markov decision process optima functions do not generally have a simple structure. Determining expressions or simple parameters of the optimal function is generally not possible, and therefore a numerical approach that takes into account the discretization of the state space of the physical system model is desirable.
Therefore, it is necessary to provide a networked lamp control method based on event driving.
Disclosure of Invention
The invention aims to provide a lamp networking control method based on event driving, which uses simple parameterization of a quadratic approximation function of an associated Markov decision process to obtain a sampling and estimation strategy based on events, thereby minimizing the upper limit of the performance of a class of systems and effectively applying to the calculation of the strategy of a system with a high-dimensional state space.
The invention realizes the purpose through the following technical scheme:
a lamp networking control method based on event driving is characterized in that after state measurement is carried out on equipment, measurement quantity is applied to the next state measurement through technology and control signals;
signal
Figure GDA0002760245090000011
Measured for the most recent state, and applying a constant control signal
Figure GDA0002760245090000012
Until a new measurement value is received, the measurement value is,
the state of the device is recurred as:
Figure GDA0002760245090000013
control variable atRepresents the time of state sampling, then
Figure GDA0002760245090000014
The recursion of (1) is:
Figure GDA0002760245090000015
the error is defined as:
Figure GDA0002760245090000016
the error recursion is:
et+1＝(1-at)((A+BK-I)xt+(I-BK)et)+ωt (4)
setting:
Figure GDA0002760245090000021
the recursion of the states and errors is:
zt+1＝((1-at)A1+atA2)zt+vt (6)
wherein
Figure GDA0002760245090000022
And is
Figure GDA0002760245090000023
The event-based sampling scheme is to send the entire system state to the estimator at the sample time, simplifying the analysis because the estimation error is reset to zero at each sample time, but the analysis will become complex when only the output measurements are sent during each time period;
the state estimate must be updated in real time as new measurements are received, which can be extended by the control method discussed above to the estimation of the output measurements,
in estimating problems, a system of dynamics is considered
xt+1＝Axt+wt，yt＝Cxt+vt (9)
When all output measurements are available, the steady state Kalman filter is based on recursion
Figure GDA0002760245090000024
Generating an optimal state estimate
Figure GDA0002760245090000025
Where L is the steady state Kalman filter observer gain, the estimator
Figure GDA0002760245090000026
Minimization;
intermittently transmitting a measured value of an output of a device to an estimator in a system composed of the device and the estimator, operatingThe following were used: if it is not
Figure GDA0002760245090000027
Is the current state estimate and no measurement is available at time t, then the state
Figure GDA0002760245090000028
Is estimated to be
Figure GDA0002760245090000029
If there is a measurement available at time t, state
Figure GDA00027602450900000210
Is estimated to be
Figure GDA00027602450900000211
Using variable atE {0,1} to indicate that a measurement has been made, the state estimate is based on
Figure GDA0002760245090000031
(ii) a change;
further, the state estimation error
Figure GDA0002760245090000032
The dynamic equation of (a) is:
Figure GDA0002760245090000033
with A1Denotes the open-loop estimator dynamics, a2A + LC represents the closed loop estimator dynamics to simplify the notation;
arranging the measurements to minimize transmission rate and estimation error, i.e. determining a strategy, selecting atMake it
Figure GDA0002760245090000034
Minimum; the event detector may observe the current state of the device and the current state estimate used by the estimator:
when an event depending on the estimation error occurs, the current output measurement value y is settSent to the estimator, which then updates its state estimate accordingly;
proposing event-based selection of atThe transmission strategy of (1): order to
Figure GDA0002760245090000035
Let ρ and Y be the solutions of the optimization problem; then there is
Figure GDA0002760245090000036
By setting up
Figure GDA0002760245090000037
Sending the measured values to an estimator;
thereby further comprising
Figure GDA0002760245090000038
An upper limit on the cost incurred by the strategy is derived.
The invention uses simple parameterization of quadratic approximation function of associated Markov decision process to obtain sampling and estimation strategy based on event, thereby minimizing the performance upper limit of one class of system and effectively applying to calculating the strategy of system with high-dimensional state space.
Drawings
Fig. 1 is a schematic structural view of the present invention.
Detailed Description
Example (b):
the embodiment shows a lamp networking control method based on event driving:
referring to fig. 1, which shows the basic architecture of the present embodiment, each time a state is sampled, a control signal is calculated and applied to the next state measurement; signal
Figure GDA0002760245090000041
Measured for the most recent state, and applying a constant control signal
Figure GDA0002760245090000042
Until a new measurement is received;
the state of the device is recurred as:
Figure GDA0002760245090000043
control variable atRepresents the time of state sampling, then
Figure GDA0002760245090000044
The recursion of (1) is:
Figure GDA0002760245090000045
the error is defined as:
Figure GDA0002760245090000046
the error recursion is:
et+1＝(1-at)((A+BK-I)xt+(I-BK)et)+ωt (4)
setting:
Figure GDA0002760245090000047
the recursion of the states and errors is:
zt+1＝((1-at)A1+atA2)zt+vt (6)
wherein
Figure GDA0002760245090000048
And is
Figure GDA0002760245090000049
The event-based sampling scheme is to send the entire system state to the estimator at the sample time, simplifying the analysis because the estimation error is reset to zero at each sample time, but the analysis becomes complex when only the output measurements are sent during each time period.
The state estimate must be updated in real time as new measurements are received, which can be extended by the control method discussed above to the estimation of the output measurements,
in estimating problems, a system of dynamics is considered
xt+1＝Axt+wt，yt＝Cxt+vt (9)
When all output measurements are available, the steady state Kalman filter is based on recursion
Figure GDA0002760245090000051
Generating an optimal state estimate
Figure GDA0002760245090000052
Where L is the steady state Kalman filter observer gain, the estimator
Figure GDA0002760245090000053
And (4) minimizing.
Simultaneously intermittently transmitting a measured value of an output of the device to the estimator in a system comprised of the device and the estimator, the operations are as follows: if it is not
Figure GDA0002760245090000054
Is the current state estimate and no measurement is available at time t, then the state
Figure GDA0002760245090000055
Is estimated to be
Figure GDA0002760245090000056
If there is a measurement available at time t, state
Figure GDA0002760245090000057
Is estimated to be
Figure GDA0002760245090000058
Using variable atE {0,1} to indicate that a measurement has been made, the state estimate is based on
Figure GDA0002760245090000059
(ii) a change;
further, the state estimation error
Figure GDA00027602450900000510
The dynamic equation of (a) is:
Figure GDA00027602450900000511
with A1Denotes the open-loop estimator dynamics, a2A + LC represents the closed loop estimator dynamics to simplify the notation;
arranging the measurements to minimize transmission rate and estimation error, i.e. determining a strategy, selecting atMake it
Figure GDA00027602450900000512
Minimum; the event detector may observe the current state of the device and the current state estimate used by the estimator:
when an event depending on the estimation error occurs, the current output measurement value y is settSent to the estimator, which then updates its state estimate accordingly;
the estimation model can be converted into the model of the above control in the same manner, and like the control problem, a selected based on the event is proposedtThe transmission strategy of (1): order to
Figure GDA0002760245090000061
Let ρ and Y be the solutions of the optimization problem; then there is
Figure GDA0002760245090000062
By setting up
Figure GDA0002760245090000063
The measured values are sent to an estimator.
Thereby further comprising
Figure GDA0002760245090000064
An upper limit on the cost incurred by the strategy is derived.
The invention uses simple parameterization of quadratic approximation function of associated Markov decision process to obtain sampling and estimation strategy based on event, thereby minimizing the performance upper limit of one class of system and effectively applying to calculating the strategy of system with high-dimensional state space.
What has been described above are merely some embodiments of the present invention. It will be apparent to those skilled in the art that various changes and modifications can be made without departing from the inventive concept thereof, and these changes and modifications can be made without departing from the spirit and scope of the invention.

Claims (1)

1. A lamp networking control method based on event driving is characterized in that: after the state measurement is carried out on the equipment, the measurement quantity is applied to the next state measurement through the technology and the control signal; signal
Figure FDA0002760245080000011
Measured for the most recent state, and applying a constant control signal
Figure FDA0002760245080000012
Until a new measurement is received, the state of the device is recurred as:
Figure FDA0002760245080000013
control variable atRepresents the time of state sampling, then
Figure FDA0002760245080000014
The recursion of (1) is:
Figure FDA0002760245080000015
the error is defined as:
Figure FDA0002760245080000016
the error recursion is:
et+1＝(1-at)((A+BK-I)xt+(I-BK)et)+ωt (4)
setting:
Figure FDA0002760245080000017
the recursion of the states and errors is:
zt+1＝((1-at)A1+atA2)zt+vt (6)
wherein
Figure FDA0002760245080000018
And is
Figure FDA0002760245080000019
The event-based sampling scheme is to send the entire system state to the estimator at the sample time, simplifying the analysis because the estimation error is reset to zero at each sample time, but the analysis will become complex when only the output measurements are sent during each time period;
the state estimate must be updated in real time as new measurements are received, which can be extended by the control method discussed above to the estimation of the output measurements,
in estimating problems, a system of dynamics is considered
xt+1＝Axt+wt，yt＝Cxt+vt (9)
When all output measurements are available, the steady state Kalman filter is based on recursion
Figure FDA00027602450800000110
Generating an optimal state estimate
Figure FDA0002760245080000021
Where L is the steady state Kalman filter observer gain, the estimator
Figure FDA0002760245080000022
Minimization;
intermittently transmitting a measurement of an output of a device to an estimator in a system comprised of the device and the estimator, the operations being as follows: if it is not
Figure FDA0002760245080000023
Is the current state estimate and no measurement is available at time t, then the state
Figure FDA0002760245080000024
Is estimated to be
Figure FDA0002760245080000025
If there is a measurement available at time t, state
Figure FDA0002760245080000026
Is estimated to be
Figure FDA0002760245080000027
Using variable atE 0,1 to indicate that a measurement has been made,state estimation with
Figure FDA0002760245080000028
(ii) a change;
further, the state estimation error
Figure FDA0002760245080000029
The dynamic equation of (a) is:
Figure FDA00027602450800000210
with A1Denotes the open-loop estimator dynamics, a2A + LC represents the closed loop estimator dynamics to simplify the notation;
arranging the measurements to minimize transmission rate and estimation error, i.e. determining a strategy, selecting atMake it
Figure FDA00027602450800000211
Minimum; the event detector may observe the current state of the device and the current state estimate used by the estimator:
when an event depending on the estimation error occurs, the current output measurement value y is settSent to the estimator, which then updates its state estimate accordingly;
proposing event-based selection of atThe transmission strategy of (1): order to
Figure FDA00027602450800000212
Let ρ and Y be the solutions of the optimization problem; then there is
Figure FDA0002760245080000031
By setting up
Figure FDA0002760245080000032
Sending the measured values to an estimator;
thereby further comprising
Figure FDA0002760245080000033
An upper limit on the cost incurred by the strategy is derived.
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6801810B1 (en) * 1999-05-14 2004-10-05 Abb Research Ltd. Method and device for state estimation
CN105391299A (en) * 2015-12-24 2016-03-09 西安理工大学 Single strategy model prediction control method of Buck converter
CN105425582A (en) * 2015-11-04 2016-03-23 北京航空航天大学 Kalman filtering based online calibrating method of Stewart mechanism
CN107065545A (en) * 2017-04-01 2017-08-18 同济大学 Distributed event triggering filtering system and design method based on Markov saltus step
CN107065551A (en) * 2017-04-24 2017-08-18 哈尔滨工大航博科技有限公司 A kind of artificial rotary table automatic correction controling method accurately recognized based on model parameter

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6801810B1 (en) * 1999-05-14 2004-10-05 Abb Research Ltd. Method and device for state estimation
CN105425582A (en) * 2015-11-04 2016-03-23 北京航空航天大学 Kalman filtering based online calibrating method of Stewart mechanism
CN105391299A (en) * 2015-12-24 2016-03-09 西安理工大学 Single strategy model prediction control method of Buck converter
CN107065545A (en) * 2017-04-01 2017-08-18 同济大学 Distributed event triggering filtering system and design method based on Markov saltus step
CN107065551A (en) * 2017-04-24 2017-08-18 哈尔滨工大航博科技有限公司 A kind of artificial rotary table automatic correction controling method accurately recognized based on model parameter

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