CN115167132A - Self-adaptive networked prediction control method and system - Google Patents
Self-adaptive networked prediction control method and system Download PDFInfo
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Abstract
The invention provides a self-adaptive networked predictive control method, a self-adaptive networked predictive control system, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring measurement output data and control input data of a controlled object, and performing data encapsulation on the measurement output data, the control input data and corresponding timestamps to obtain an initial data sequence; obtaining a model time-varying parameter estimation value of a preset time point by an identifier based on the initial data sequence and the model time-varying parameter estimation value of the previous time point; obtaining a control increment prediction sequence by a predictor based on a model time-varying parameter estimation value of a preset time point, a preset system output reference value and an initial data sequence, and sending the control increment prediction sequence and a corresponding time stamp to a compensator; and determining a target control signal by the compensator based on the control increment prediction sequence, the time stamp and the receiving time point, and performing networked prediction control on the controlled object by using the target control signal. The method can improve the control effect.
Description
Technical Field
The invention relates to the technical field of engineering control, in particular to a self-adaptive networked predictive control method and a self-adaptive networked predictive control system.
Background
With the development of the technical field of engineering control, the application of the networked predictive control method is more and more extensive. The networked control system is a networked nonlinear system with random network time delay, packet loss and disorder existing in a sensor-controller channel and a controller-actuator channel, and an unknown mathematical model, and the networked prediction control method is a method operated in the system. Due to high flexibility, strong mobility and convenience for remote monitoring and control, the networked control system is more and more widely applied to multiple fields such as process control, intelligent manufacturing, traffic systems, energy systems and the like. When the measurement signal and the control signal are transmitted in the networked control loop, communication constraint problems such as random network delay, packet loss, disorder and the like inevitably exist, and the factors can reduce the control effect of the system.
In the prior art, when a system model is unknown or inaccurate, a networked predictive control method often has the defect of poor control effect.
Disclosure of Invention
The invention provides a self-adaptive networked predictive control method and a self-adaptive networked predictive control system, which are used for solving the problem that the networked predictive control effect is poor in the prior art and achieving the purpose of improving the networked predictive control effect.
The invention provides a self-adaptive networked prediction control method, which comprises the following steps: acquiring measurement output data and control input data of a controlled object, performing data encapsulation on the measurement output data, the control input data and corresponding timestamps to obtain an initial data sequence, and sending the initial data sequence to an identifier; obtaining, by the identifier, a model time-varying parameter estimation value of a preset time point based on the initial data sequence and a model time-varying parameter estimation value of a previous time point, and sending the model time-varying parameter estimation value of the preset time point to a predictor, the previous time point being a time point previous to the preset time point; a predictor obtains a control increment prediction sequence based on the model time-varying parameter estimation value of the preset time point, a preset system output reference value and the initial data sequence, and sends the control increment prediction sequence and the corresponding time stamp to a compensator; determining, by the compensator, a target control signal based on the control delta prediction sequence and the timestamp, and a reception time point, the reception time point being a time point at which the compensator receives the control delta prediction sequence and the timestamp; and performing networked predictive control on the controlled object by using the target control signal.
According to the adaptive networked predictive control method provided by the invention, the obtaining of the model time-varying parameter estimation value of the preset time point based on the model time-varying parameter estimation value of the previous time point comprises the following steps: acquiring a first rule function; and obtaining a model time-varying parameter estimation value of a preset time point under the condition that the first criterion function takes the minimum value.
According to the adaptive networked predictive control method provided by the invention, the method further comprises the following steps: and when the model time-varying parameter estimation value at the preset time point meets a parameter limiting condition, determining the initial parameter of the model time-varying parameter estimation value as the model time-varying parameter estimation value at the preset time point, wherein the parameter limiting condition comprises at least one of that the model time-varying parameter estimation value at the preset time point is less than or equal to a parameter threshold value or that a sign function of the model time-varying parameter estimation value at the preset time point is not equal to a sign function of the initial parameter of the model time-varying parameter estimation value.
According to the adaptive networked predictive control method provided by the invention, the step of obtaining a control increment prediction sequence based on the estimated value of the model time-varying parameter at the preset time point, the preset system output reference value and the initial data sequence comprises the following steps: acquiring a second criterion function corresponding to the control increment prediction sequence based on the estimated value of the model time-varying parameter at the preset time point, a preset system output reference value and the initial data sequence; under the condition that the second criterion function is the minimum value, obtaining a self-adaptive control law expression; iterating the self-adaptive control law expression to obtain a prediction control increment expression; and obtaining the predictive control increment sequence based on the predictive control increment expression.
According to the adaptive networked predictive control method provided by the invention, the step of determining the target control signal based on the control increment prediction sequence, the time stamp and the receiving time point comprises the following steps: calculating the difference between the receiving time point and the timestamp to obtain the total time delay of the feedback channel and the forward channel corresponding to the receiving time point; and carrying out time compensation on the total time delay by using each control prediction increment in the control increment prediction sequence to obtain the target control signal.
The invention also provides a self-adaptive networked predictive control system, which comprises: the buffer is used for acquiring measurement output data and control input data of a controlled object, performing data encapsulation on the measurement output data, the control input data and corresponding timestamps to obtain an initial data sequence, and sending the initial data sequence to the identifier; the identifier is connected with the buffer and used for acquiring a model time-varying parameter estimation value of a preset time point based on the initial data sequence and a model time-varying parameter estimation value of a previous time point, and sending the model time-varying parameter estimation value of the preset time point to the predictor, wherein the previous time point is the time point which is the last time point of the preset time point; the predictor is connected with the identifier and used for obtaining a control increment prediction sequence based on the model time-varying parameter estimation value of the preset time point, a preset system output reference value and the initial data sequence, and sending the control increment prediction sequence and the corresponding timestamp to the compensator; the compensator is connected with the predictor and the controlled object and used for determining a target control signal based on the control increment prediction sequence and the time stamp and a receiving time point, wherein the receiving time point is the time point when the compensator receives the control increment prediction sequence and the time stamp; and performing networked predictive control on the controlled object by using the target control signal.
The present invention also provides an electronic device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the adaptive networked predictive control method as described in any of the above when executing the program.
The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the adaptive networked predictive control method according to any one of the preceding claims.
The present invention also provides a computer program product comprising a computer program which, when executed by a processor, performs the steps of the adaptive networked predictive control method according to any of the above-mentioned methods.
The invention provides a self-adaptive networked prediction control method, a system, electronic equipment and a storage medium, wherein measurement output data and control input data of a controlled object are obtained, the measurement output data, the control input data and corresponding time stamps are subjected to data encapsulation to obtain an initial data sequence, and the initial data sequence is sent to an identifier; the identifier obtains a model time-varying parameter estimation value of a preset time point based on the initial data sequence and the model time-varying parameter estimation value of the previous time point, and sends the model time-varying parameter estimation value of the preset time point to the predictor; the previous time point is the previous time point of the preset time point; obtaining a control increment prediction sequence by a predictor based on a model time-varying parameter estimation value of a preset time point, a preset system output reference value and the initial data sequence, and sending the control increment prediction sequence and the corresponding timestamp to a compensator; determining, by the compensator, a target control signal based on the control increment prediction sequence and the timestamp, and a reception time point, the reception time point being a time point at which the compensator receives the control increment prediction sequence and the timestamp; and performing networked predictive control on the controlled object by using the target control signal. The networked predictive control of the controlled object is realized through a series of processing of the measurement output data and the control input data of the controlled object, and the effect of the networked predictive control is improved.
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In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of an adaptive networked predictive control method according to the present invention;
FIG. 2 is a second schematic flow chart of the adaptive networked predictive control method according to the present invention;
FIG. 3 is a third schematic flow chart of the adaptive networked predictive control method according to the present invention;
FIG. 4 is a fourth flowchart illustrating an adaptive networked predictive control method according to the present invention;
FIG. 5 is a schematic diagram of an adaptive networked predictive control system provided by the present invention;
FIG. 6 is a schematic diagram illustrating feedback channel delay in the adaptive networked predictive control system according to the present invention;
fig. 7 is a schematic diagram of forward channel delay in the adaptive networked predictive control system according to the present invention;
FIG. 8 is a second schematic diagram illustrating the feedback channel delay in the adaptive networked predictive control system according to the present invention;
fig. 9 is a second schematic diagram of the forward channel delay in the adaptive networked predictive control system according to the present invention;
FIG. 10 is a schematic diagram illustrating the control effect of the adaptive networked predictive control method according to the present invention;
FIG. 11 is a schematic diagram illustrating a second control effect of the adaptive networked predictive control method according to the present invention;
fig. 12 is a schematic structural diagram of an electronic device provided by the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, 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.
The adaptive networked predictive control method of the present invention is described below in conjunction with fig. 1-4.
In one embodiment, as shown in fig. 1, an adaptive networked predictive control method is provided, which is described by taking an example of the method applied to an adaptive networked predictive control system, and includes the following steps:
The controlled object is an object to be controlled in the networked predictive control. The measurement output data refers to measurement data output by the controlled object at a certain point in time. The control input data is data that is input to a controlled object at a certain point in time and controls the controlled object.
Specifically, the method comprises the steps of utilizing a buffer in a self-adaptive networked predictive control system to achieve collection of measurement output data and control input data of a controlled object, packaging the measurement output data and the control input data according to a collected time point, namely a first time stamp to obtain an initial data sequence, and sending the initial data sequence to an identifier.
In one embodiment, assuming that the measurement output data at time k is represented as y (k) and the control input data is represented as u (k-1), the measurement output data, the control input data and the corresponding first time stamp k are data-packed to obtain an initial data sequence D sent to the identifier k Then the initial data sequence D k Can be expressed as the formula:
wherein, the first and the second end of the pipe are connected with each other,
and the maximum value of the time delay in the feedback channel is shown, and N is the length of the incremental triangular model.
In one embodiment, the obtaining of the incremental triangular model includes obtaining a single-input single-output discrete-time nonlinear system, which is expressed by the following formula:
y(k+1)＝f(y(k),…,y(k-n y ),u(k),…,u(k-n u )) (2)
wherein y (k) and u (k) are respectively the measurement output and control input of the controlled object in the system at time k, f (-) is an unknown nonlinear function, n y And n u Respectively unknown system output order and input order.
If there is a continuous partial derivative of f (·) with respect to u (k) and it is satisfied that for any time k and | Δ u (k) | ≠ 0, there is | Δ y (k + 1) | ≦ b | Δ u (k) |, where b > 0 is constant, there is a time-varying bounded parameter h (k) such that the above equation (2) can be equivalently transformed into the following incremental triangular data model:
Δy(k+1)＝h(k)Δω(k) (3)
h (k) is a model time-varying parameter of the incremental triangular model, and can be estimated online in real time for a given nonlinear system.
Where Δ ω (k) is the system generalized input increment, and can be expressed as the formula:
the positive integers m and N satisfy 0 < m < N, m is the corresponding sampling time, N is the length of the incremental triangular model, and m and N can be correspondingly determined according to the system pulse.
Wherein, Δ y (k + 1) represents the output increment of the controlled object in the system, and is expressed as a formula:
Δy(k+1)＝y(k+1)-y(k) (5)
wherein, Δ u (k) represents the input increment of the controlled object in the system, and is expressed as the formula:
Δu(k)＝u(k)-u(k-1) (6)
and 104, acquiring a model time-varying parameter estimation value of a preset time point by the identifier based on the initial data sequence and the model time-varying parameter estimation value of the previous time point, and sending the model time-varying parameter estimation value of the preset time point to the predictor, wherein the previous time point is the previous time point of the preset time point.
Specifically, after receiving the initial data sequence, an identifier in the adaptive networked predictive control system obtains a model time-varying parameter estimation value of a previous time point of a forward time point in consideration of random time delay, packet loss, disorder and the like existing in a feedback channel, obtains a model time-varying parameter estimation value of a preset time point based on the model time-varying parameter estimation value of the previous time point, and sends the model time-varying parameter estimation value of the preset time point to a predictor.
In one embodiment, the predetermined time point is denoted as k, and the initial data sequence D k Time-varying parameter estimation of a model at a previous time point
Obtaining a model time-varying parameter estimation value of a preset time point of the preset time point by using the following formula
The total delay of the feedback path is expressed as
A predetermined time point k, wherein
The estimated value of the model time-varying parameter at the previous time point is expressed as
The initial data sequence is denoted as D above k And obtaining the estimated value of the model time-varying parameter of the preset time point by combining the related formula in the step 102 and the formula (7)
And sending the model time-varying parameter estimation value of the preset time point to the predictor.
And 106, obtaining a control increment prediction sequence by the predictor based on the model time-varying parameter estimation value of the preset time point, the output reference value of the preset system and the initial data sequence, and sending the control increment prediction sequence and the corresponding time stamp to the compensator.
Specifically, the predictor in the adaptive networked predictive control system, after receiving the model time-varying parameter, may use the above equation (3) to obtain the predicted measured output data, which is denoted as y (t) k +1|t k ) Predicting the measurement output data y (t) k +1|t k ) Expressed as the formula:
wherein
Is t k And (3) estimating the time-varying parameter of the model at a preset time point.
And constructing a second criterion function based on the predicted measurement output data, the estimated value of the model time-varying parameter at the preset time point, the preset system output reference value and the initial data sequence, obtaining a control increment prediction sequence based on the second criterion function, and sending the control increment prediction sequence and a corresponding second time stamp to the compensator.
And step 108, determining the target control signal by the compensator based on the control increment prediction sequence and the time stamp and the receiving time point, wherein the receiving time point is the time point when the compensator receives the control increment prediction sequence and the time stamp.
Specifically, after receiving the control increment prediction sequence, a compensator in the adaptive networked prediction control system first obtains the total time delay of the feedback channel and the forward channel according to the difference between the receiving time point and a second time stamp corresponding to the control increment prediction sequence, and compensates the control signal corresponding to the second time stamp by using the total time delay to obtain the target control signal.
And 110, performing networked predictive control on the controlled object by using the target control signal.
Specifically, after the compensator in the adaptive networked predictive control system determines the target control signal, the adaptive networked predictive control system can perform networked predictive control on the controlled object by using the target control signal.
In the adaptive networked predictive control method, measurement output data and control input data of a controlled object are obtained, the measurement output data and the control input data and corresponding time stamps are subjected to data encapsulation to obtain an initial data sequence, and the initial data sequence is sent to an identifier; obtaining a model time-varying parameter estimation value of a preset time point by an identifier based on an initial data sequence and a model time-varying parameter estimation value of a previous time point, and sending the model time-varying parameter estimation value of the preset time point to a predictor, wherein the previous time point is the previous time point with the preset time point; obtaining a control increment prediction sequence by a predictor based on a model time-varying parameter estimation value of a preset time point, a preset system output reference value and an initial data sequence, and sending the control increment prediction sequence and a corresponding timestamp to a compensator; determining, by the compensator, a target control signal based on the control increment prediction sequence and the timestamp, and a reception time point, the reception time point being a time point at which the compensator receives the control increment prediction sequence and the timestamp; and performing networked predictive control on the controlled object by using the target control signal. The networked predictive control of the controlled object can be realized through a series of processing of the measurement output data and the control input data of the controlled object, and the control effect is improved.
In one embodiment, as shown in fig. 2, obtaining the estimated value of the model time-varying parameter at the preset time point based on the estimated value of the model time-varying parameter at the previous time point includes:
in step 202, a first criterion function is obtained.
Specifically, after the identifier obtains the target data sequence, the model time-varying parameter h (t) may be modeled by constructing a criterion function k ) Estimating the model time-varying parameter h (t) k ) The first criterion function for the estimation can be expressed as the formula:
wherein
Is an estimate of h (k), and μ > 0.
And 204, under the condition that the first rule function takes the minimum value, obtaining a model time-varying parameter estimation value of a preset time point, and determining the model time-varying parameter estimation value of the preset time point as a model time-varying parameter.
Specifically, after the identifier obtains the first criterion function, the minimum value of the first criterion function is determined, and when the formula (9) is the minimum value, the estimated value of the model time-varying parameter at the preset time point is obtained
The estimated value of the model time-varying parameter at the preset time point
Can be expressed as the formula:
estimating the model to time-varying parameters
Determined as a model time-varying parameter h (t) k )。
In this embodiment, by obtaining the first criterion function, the estimated value of the model time-varying parameter at the preset time point is obtained under the condition that the first criterion function takes the minimum value, and the estimated value of the model time-varying parameter at the preset time point is determined as the model time-varying parameter, so that the purpose of accurately determining the model time-varying parameter can be achieved.
In one embodiment, the method further comprises: when the model time-varying parameter estimation value at the preset time point meets a parameter limiting condition, determining the initial parameter of the model time-varying parameter estimation value as the model time-varying parameter estimation value at the preset time point, wherein the parameter limiting condition comprises that the model time-varying parameter estimation value at the preset time point is less than or equal to a parameter threshold value or a symbolic function of the model time-varying parameter estimation value at the preset time point is unequal to that of the initial parameter of the model time-varying parameter estimation value.
The parameter limitation condition is a condition that the model estimates the time-varying parameter to satisfy. The parameter threshold is a critical value of the parameter, and is less than or equal to the critical value, the model estimation time-varying parameter is considered to satisfy the parameter limitation condition, and is greater than the critical value, the model estimation time-varying parameter is considered to not satisfy the parameter limitation condition. The model time-varying initial parameter estimation value refers to a model time-varying parameter estimation value when k takes a value of 0.
Specifically, after the identifier obtains the estimated value of the model time-varying parameter at the preset time point, under certain parameter limiting conditions, the estimated value of the model time-varying initial parameter is determined as the estimated value of the model time-varying parameter at the preset time point.
In one embodiment, the estimated value of the model time-varying parameter at a predetermined time point is expressed as
The initial parameters of the model time-varying estimated value are expressed as
The parameter threshold is expressed as ε, which may be a sufficiently small value, and the sign function of the model time-varying parameter estimate is expressed as
The symbolic function of the initial parameter of the model time-varying estimation value is expressed as
Then is being satisfied
Or
Determining the estimated value of the model time-varying parameter at the preset time point is expressed as the formula:
in this embodiment, when the estimated value of the model time-varying parameter at the preset time point satisfies the parameter limiting condition, the initial parameter of the estimated value of the model time-varying parameter is determined as the estimated time-varying parameter at the preset time point, so that the estimated value of the model time-varying parameter at the preset time point can be accurately and comprehensively determined.
In one embodiment, as shown in fig. 3, obtaining the control increment prediction sequence based on the estimated value of the model time-varying parameter at the preset time point, the preset system output reference value and the initial data sequence includes:
Specifically, after receiving the estimated value of the model time-varying parameter at the preset time point, the predictor obtains a related second criterion function based on the above formula (8), and may obtain a corresponding control increment prediction sequence by processing the second criterion function.
In one embodiment, the second criterion function may be expressed as the formula:
J(Δu(t k |t k ))＝(y r (t k +1)-y(t k +1|t k )) 2 +λΔu(t k |t k ) 2 (12)
wherein, y r (t k + 1) is t k And λ > 0 is a weighting factor for the reference signal at time + 1.
And 304, obtaining an adaptive control law expression under the condition that the second criterion function takes the minimum value.
Specifically, after the predictor obtains the second criterion function, the function is minimized to obtain an adaptive control law expression, where the adaptive control law expression is expressed as:
wherein, e (t) k ) Expressed as the formula:
e(t k )＝y r (t k +1)-y(t k ) (14)
and step 306, iterating the adaptive control law expression to obtain a prediction control increment expression.
Specifically, after the predictor obtains the adaptive control law expression, iteration is performed on the adaptive control law expression, and the predictive control incremental expression can be expressed as a formula:
wherein
Upper bound of total delay for the reverse path and the forward path, e (t) k +j|t k ) Expressed as the formula:
e(t k +j|t k )＝y r (t k +j+1)-y(t k +j|t k ) (16)
wherein y (t) k +j|t k ) Expressed as the formula:
and 308, obtaining a predictive control increment sequence based on the predictive control increment expression.
Specifically, after the predictor obtains the predicted control increment expression, a predicted control increment sequence can be obtained, which represents:
in the embodiment, a second criterion function corresponding to the control increment prediction sequence is obtained by a model time-varying parameter estimation value based on a preset time point, a preset system output reference value and an initial data sequence, an adaptive control law expression is obtained under the condition that the second criterion function is the minimum value, the adaptive control law expression is iterated to obtain a predictive control increment expression, the predictive control increment sequence is obtained based on the predictive control increment expression, and the purpose of accurately determining the predictive control increment sequence can be achieved.
In one embodiment, deriving the sequence of predicted control increments based on the predicted control increment expression comprises: under the condition that the first total time delay of the feedback channel and the forward channel is less than the sampling time point, the predicted control increment is the same as the actual control increment; and under the condition that the predicted control increment is the same as the actual control increment, obtaining a predicted control increment sequence based on the predicted control increment expression.
Specifically, after the predictor obtains the prediction control incremental expression, the prediction control incremental expression obtained by the predictor can be limited by using a delay condition, so as to obtain a corresponding prediction control incremental sequence.
In one embodiment, the first total delay of the feedback path and the forward path is denoted as j, the sampling time point is denoted as i, and the prediction control increment is denoted as Δ u (t) k +j-i|t k ) The actual control increment is expressed as Δ u (t) k + j-i), the formula is satisfied when the first total delay j is less than the sampling time point i:
Δu(t k +j-i|t k )＝Δu(t k +j-i) (19)
the above equation (14) can be defined by using the conditions of equation (18) to obtain the corresponding predicted control increment sequence.
In this embodiment, the purpose of accurately obtaining the sequence of the prediction control increment can be achieved by determining the relationship between the prediction control increment and the actual control increment when the first total delay of the feedback channel and the forward channel is smaller than the sampling time point.
In one embodiment, as shown in fig. 4, determining the target control signal based on the control delta prediction sequence and the time stamp, and the reception time point includes:
and 402, calculating a difference value between the receiving time point and the timestamp to obtain the total time delay of the feedback channel and the forward channel corresponding to the receiving time point, wherein the total time delay is the total time delay of the feedback channel and the forward channel corresponding to the receiving time point.
Specifically, in order to be able to determine an accurate target control signal, the total time delay of the feedback path and the forward path needs to be determined first.
In one embodiment, the point in time of receipt is denoted as k and the timestamp is denoted as
The total delay of the feedback path and the forward path is denoted as tau k Due to the random network delay, packet loss and the existence of random network delay in the feedback channel and the forward channelOut of order, the compensator cannot guarantee that a new control increment prediction sequence is received every execution cycle. Assume that at the current time k, the received control increment prediction sequence is represented as:
total time delay tau of feedback path and forward path k Expressed as the formula:
and step 404, performing time compensation on the total time delay by using each predicted control increment in the control increment prediction sequence to obtain a target control signal.
Specifically, the compensator obtains the total time delay of the feedback channel and the forward channel, and performs time compensation on the total time delay by using each prediction control increment in the control increment prediction sequence to obtain a target control signal. Assuming that the target control signal is represented as u (k), the target control signal u (k) is represented as the formula:
in this embodiment, the total delay of the feedback channel and the forward channel is obtained by performing difference calculation on the receiving time point and the timestamp, and time compensation is performed on the total delay by using each prediction control increment in the control increment prediction sequence to obtain a target control signal, so that the purpose of accurately obtaining the target control signal can be achieved.
In one embodiment, as shown in fig. 5, there is provided an adaptive networked predictive control system, including: the buffer is used for acquiring the measurement output data and the control input data of the controlled object, performing data encapsulation on the measurement output data, the control input data and the corresponding time stamp to obtain an initial data sequence, and sending the initial data sequence to the identifier; the identifier is connected with the buffer and used for acquiring a model time-varying parameter estimation value of a preset time point by the identifier based on the initial data sequence and the model time-varying parameter estimation value of the previous time point, and sending the model time-varying parameter estimation value of the preset time point to the predictor, wherein the previous time point is the previous time point of the preset time point; the predictor is connected with the identifier and used for obtaining a control increment prediction sequence based on the model time-varying parameter estimation value of the preset time point, the preset system output reference value and the initial data sequence and sending the control increment prediction sequence and the corresponding timestamp to the compensator; the compensator is connected with the predictor and the controlled object and is used for determining a target control signal based on the control increment prediction sequence and the time stamp as well as a receiving time point, and the receiving time point is the time point when the compensator receives the control increment prediction sequence and the time stamp; and performing networked predictive control on the controlled object by using the target control signal.
In one embodiment, the adaptive networked predictive control method and the partial-format adaptive networked predictive control method are used for simulation verification, MATLAB software is used for numerical simulation verification of the nonlinear system, and the measured output data is expressed as:
the reference signal output by the system is expressed as:
y r (k+1)＝sign(sin(kπ/50)) (24)
the model time-varying estimation parameters in the partial-format networked predictive control method are expressed as follows:
The prediction control increment in the partial-format networked prediction control method is expressed as follows:
equation (26) is satisfied when j is smaller than i:
Δu(t k +j-i|t k )＝Δu(t k +j-i) (28)
wherein
Representing the total delay of the reverse path and the forward path, L representing the dynamic linearization length,
expressed as:
the prediction control increment in the partial-format networked prediction control method is expressed as follows:
ΔU(t k -1)＝[Δu(t k -1),Δu(t k -2),…,Δu(t k -L)] T (30)
in the foregoing adaptive networked predictive control method, the parameter settings are μ =0.5 and ∈ =10, respectively -5 And m =2, and N =4, and λ =3.4, and
parameters L =3, and λ =7.6, and
as shown in fig. 6-9, the random delays of the forward channel and the feedback channel are both selected to be delays of 1 to 4 steps, where the ordinate represents the step size of the random delay and the abscissa represents the sampling time. The simulation results for the two control schemes at different sampling instants are shown in fig. 10 and 11. As shown in fig. 10, which is a simulation result of the present invention and the partial-format networked predictive control method during the period from time 400 to 700, it can be seen that, in the case where the overshoot amounts are almost equal, the method has a faster system output response than the partial-format networked predictive control method, and can track the reference signal closer and faster. Specifically, as shown in fig. 11, it is a simulation result of the method (curve corresponding to the triangular model in the figure) and the partial-format networked prediction control method (curve corresponding to the partial-format model) during the period from time 9400 to time 9700, and it can be seen that after the method is operated for a long time, the tracking effect is still almost the same as the initial tracking effect, and still a good output response is maintained, while after the partial-format networked prediction control method is operated for a long time, the tracking effect is significantly deteriorated. Therefore, the method is more suitable for application and popularization in practical engineering.
Fig. 12 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 12: a processor (processor) 1210, a communication Interface (Communications Interface) 1220, a memory (memory) 1230, and a communication bus 1240, wherein the processor 1210, the communication Interface 1220, and the memory 1230 communicate with each other via the communication bus 1240. Processor 1210 may invoke logic instructions in memory 1230 to perform an adaptive networked predictive control method comprising: acquiring measurement output data and control input data of a controlled object, performing data encapsulation on the measurement output data, the control input data and corresponding timestamps to obtain an initial data sequence, and sending the initial data sequence to an identifier; the identifier acquires a model time-varying parameter estimation value of a preset time point based on the initial data sequence and the model time-varying parameter estimation value of a previous time point, and sends the model time-varying parameter estimation value of the preset time point to the predictor, wherein the previous time point is the previous time point of the preset time point; obtaining a control increment prediction sequence by a predictor based on a model time-varying parameter estimation value of a preset time point, a preset system output reference value and an initial data sequence, and sending the control increment prediction sequence and a corresponding timestamp to a compensator; determining a target control signal by the compensator based on the control increment prediction sequence and the time stamp, and a receiving time point, wherein the receiving time point is a time point when the compensator receives the control increment prediction sequence and the time stamp; and performing networked predictive control on the controlled object by using the target control signal.
In addition, the logic instructions in the memory 1230 may be implemented in software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as a stand-alone product. 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 various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product, the computer program product including a computer program, the computer program being storable on a non-transitory computer-readable storage medium, wherein when the computer program is executed by a processor, a computer is capable of executing the adaptive networked predictive control method provided by the above methods, the method including: acquiring measurement output data and control input data of a controlled object, performing data encapsulation on the measurement output data, the control input data and corresponding timestamps to obtain an initial data sequence, and sending the initial data sequence to an identifier; obtaining a model time-varying parameter estimation value of a preset time point by an identifier based on an initial data sequence and a model time-varying parameter estimation value of a previous time point, and sending the model time-varying parameter estimation value of the preset time point to a predictor, wherein the previous time point is the previous time point of the preset time point; obtaining a control increment prediction sequence by a predictor based on a model time-varying parameter estimation value of a preset time point, a preset system output reference value and an initial data sequence, and sending the control increment prediction sequence and a corresponding timestamp to a compensator; determining a target control signal by the compensator based on the control increment prediction sequence and the time stamp, and a receiving time point, wherein the receiving time point is a time point when the compensator receives the control increment prediction sequence and the time stamp; and performing networked predictive control on the controlled object by using the target control signal.
In yet another aspect, the present invention also provides a non-transitory computer readable storage medium, on which a computer program is stored, the computer program being implemented by a processor to perform the adaptive networked predictive control method provided by the above methods, the method including: acquiring measurement output data and control input data of a controlled object, performing data encapsulation on the measurement output data, the control input data and corresponding timestamps to obtain an initial data sequence, and sending the initial data sequence to an identifier; obtaining a model time-varying parameter estimation value of a preset time point by an identifier based on an initial data sequence and a model time-varying parameter estimation value of a previous time point, and sending the model time-varying parameter estimation value of the preset time point to a predictor, wherein the previous time point is the previous time point of the preset time point; obtaining a control increment prediction sequence by a predictor based on a model time-varying parameter estimation value of a preset time point, a preset system output reference value and an initial data sequence, and sending the control increment prediction sequence and a corresponding time stamp to a compensator; determining a target control signal by the compensator based on the control increment prediction sequence and the time stamp, and a receiving time point, wherein the receiving time point is a time point when the compensator receives the control increment prediction sequence and the time stamp; and performing networked predictive control on the controlled object by using the target 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 (9)
1. An adaptive networked predictive control method, comprising:
acquiring measurement output data and control input data of a controlled object, performing data encapsulation on the measurement output data, the control input data and corresponding timestamps to obtain an initial data sequence, and sending the initial data sequence to an identifier;
obtaining, by the identifier, a model time-varying parameter estimation value of a preset time point based on the initial data sequence and a model time-varying parameter estimation value of a previous time point, and sending the model time-varying parameter estimation value of the preset time point to a predictor, the previous time point being a previous time point of the preset time point;
obtaining a control increment prediction sequence by a predictor based on the model time-varying parameter estimation value of the preset time point, a preset system output reference value and the initial data sequence, and sending the control increment prediction sequence and the corresponding timestamp to a compensator;
determining, by the compensator, a target control signal based on the control delta prediction sequence and the timestamp, and a reception time point, the reception time point being a time point at which the compensator receives the control delta prediction sequence and the timestamp;
and performing networked predictive control on the controlled object by using the target control signal.
2. The adaptive networked predictive control method of claim 1, wherein the obtaining the estimated value of the model time-varying parameter at the preset time point based on the estimated value of the model time-varying parameter at the previous time point comprises:
acquiring a first rule function;
and obtaining a model estimation time-varying parameter under the condition that the first criterion function takes the minimum value, and determining the model estimation time-varying parameter as the model time-varying parameter estimation value of the preset time point.
3. The adaptive networked predictive control method of claim 2, further comprising:
and when the model time-varying parameter estimation value at the preset time point meets a parameter limiting condition, determining the initial parameter of the model time-varying parameter estimation value as the model time-varying parameter estimation value at the preset time point, wherein the parameter limiting condition comprises at least one of that the model time-varying parameter estimation value at the preset time point is less than or equal to a parameter threshold value or that a sign function of the model time-varying parameter estimation value at the preset time point is unequal to a sign function of the initial parameter of the model time-varying parameter estimation value.
4. The adaptive networked predictive control method of claim 1, wherein the obtaining of the control increment prediction sequence based on the model time-varying parameter estimation value at the preset time point, a preset system output reference value and the initial data sequence comprises:
acquiring a second criterion function corresponding to the control increment prediction sequence based on the model time-varying parameter estimation value of the preset time point, a preset system output reference value and the initial data sequence;
under the condition that the second criterion function is the minimum value, obtaining a self-adaptive control law expression;
iterating the self-adaptive control law expression to obtain a prediction control increment expression;
and obtaining the predictive control increment sequence based on the predictive control increment expression.
5. The adaptive networked predictive control method of any of claims 1-4, wherein determining a target control signal based on the control delta prediction sequence and the time stamp, and a point in time of receipt comprises:
calculating a difference value between the receiving time point and the timestamp to obtain the total time delay of the feedback channel and the forward channel corresponding to the receiving time point, wherein the total time delay is the total time delay of the feedback channel and the forward channel corresponding to the receiving time point;
and carrying out time compensation on the total time delay by using each control prediction increment in the control increment prediction sequence to obtain the target control signal.
6. An adaptive networked predictive control system, comprising:
the buffer is used for acquiring measurement output data and control input data of a controlled object, performing data encapsulation on the measurement output data, the control input data and corresponding timestamps to obtain an initial data sequence, and sending the initial data sequence to the identifier;
the identifier is connected with the buffer and used for acquiring a model time-varying parameter estimation value of a preset time point based on the initial data sequence and a model time-varying parameter estimation value of a previous time point, and sending the model time-varying parameter estimation value of the preset time point to the predictor, wherein the previous time point is the time point which is the last time point of the preset time point;
the predictor is connected with the identifier and used for obtaining a control increment prediction sequence based on the model time-varying parameter estimation value of the preset time point, a preset system output reference value and the initial data sequence and sending the control increment prediction sequence and the corresponding timestamp to the compensator;
the compensator is connected with the predictor and the controlled object and used for determining a target control signal based on the control increment prediction sequence and the time stamp and a receiving time point, wherein the receiving time point is the time point when the compensator receives the control increment prediction sequence and the time stamp; and performing networked predictive control on the controlled object by using the target control signal.
7. 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 adaptive networked predictive control method according to any of claims 1 to 5.
8. A non-transitory computer readable storage medium, having stored thereon a computer program, wherein the computer program, when being executed by a processor, is adapted to carry out the steps of the adaptive networked predictive control method according to any one of claims 1 to 5.
9. A computer program product comprising a computer program, wherein the computer program when executed by a processor implements the steps of the adaptive networked predictive control method according to any one of claims 1 to 5.
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