CN106209474B - A kind of network control system tracking and controlling method based on predictive compensation - Google Patents
A kind of network control system tracking and controlling method based on predictive compensation Download PDFInfo
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- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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Abstract
The invention discloses a kind of network control system tracking and controlling method based on predictive compensation, neural network forecast controller are made of prediction generator and time delay compensator two parts, the controlling increment sequence of k moment neural network forecast generator output are as follows:The controlling increment of time delay compensator t moment are as follows:The present invention shortens prediction step as cost using the prediction error for introducing very little, improves predictive controller to the compensation effect of network delay, is easier to realize compared to traditional neural network forecast control.
Description
Technical field
The present invention relates to network control systems, track control more particularly to the network control system based on predictive compensation
Method processed.
Background technique
It is known as network control system (networked control by the closed-loop control system that communication network is formed
Systems is abbreviated NCSs), NCSs has many advantages, such as that convenient for installation and maintenance, flexibility is high and is easy to reconstruct.Control based on network system
The components such as controlled device, sensor, controller and actuator connect and compose closed-loop system by real-time network in system.Network draws
Enter, on the one hand overcome the remote distributed in space, to realize that remotely monitor and control provides basis.On the other hand, network topology
Inevitable time delay and packet loss during structure, Internet resources are limited, packet transmission communicates all choose traditional control method proposition
War.Currently, Control Methods of Networked Control Systems is broadly divided into two classes: a kind of method is by network directly with the parameter confirmed in advance
Description introduces system model, is analyzed with the control theory for having time delay in traditional theory, this kind of theory has time-delay system reason
By, stochastic control theory, Optimal Control Theory, switching system theory etc..Such methods simplify the shadow of network in the controls
It rings, existing theoretical result, but non-Active Compensation time delay can be used for reference, system performance cannot be substantially improved.It is another kind of by network and
System performance of the system Independent modeling collaborative design to meet the requirements is the hot spot studied at present.
Neural network forecast control is a kind of collaborative design method, the compensation network time delay by way of prediction.For it is discrete when
Between linear time invariant system state can not survey situation, existing method designs controller end observer, is provided according to feedback channel
Delay information estimate the State Viewpoint measured value before several steps, and by several steps of system state space model recursion current and future
Status predication value is fed back by fixed gain state and realizes closed-loop control, can be with lifting system performance, but needs designated lane, difficult real
It is existing, at high cost；Another kind improves neural network forecast control method, realizes prediction closed-loop control system, nothing in conjunction with optimal control thought
Designated lane is needed, but underuses controller end Given information, increases prediction step.
Summary of the invention
For above-mentioned problems of the prior art, the present invention provides a kind of control based on network based on predictive compensation
System tracking and controlling method.It is equal in forward path and feedback channel for the tracking control problem that engineering is widely present in practice
Closed loop controller is designed in the case where there are random delay, sufficiently uses Given information effective compensation time delay.
The technical scheme adopted by the invention is that: a kind of network control system tracing control side based on predictive compensation
Method, neural network forecast controller are made of prediction generator and time delay compensator two parts, comprising the following steps:
1) network control system model is established
Constant multiple-input and multiple-output controlled system when a kind of discrete-time linear, augmented system state space description is such as
Under:
Wherein,Δ u (t)=u (t)-u (t-1)；x(t)∈Rn、u(t)∈RmWith y (t) ∈ RlRespectively
For the state vector of controlled device, control input and control output, w (t) ∈ RqFor deterministic perturbation, A ∈ Rn×n,B∈Rn×m,C
∈Rl×nFor coefficient matrix, system satisfaction (A, B) is fully controllable, and (A, C) is completely considerable.System exports y (t) and tracks external reference
Input y0(t) realize that asymptotic tracking, i.e. tracking error e (t) meet:
The coefficient matrix of augmented system is respectively as follows:
System mode is not directly surveyed, and state observer is established:
Wherein, x (t+1 | t) is State Viewpoint measured value of the current time to subsequent time, x (t | t-1) and y (t | t-1) distinguish
It is previous moment to the State Viewpoint measured value and output observation at current time, L ∈ Rn*lFor observer gain.Augmentation observer increases
Benefit
2) generator is predicted
The controlling increment sequence of k moment neural network forecast generator output are as follows:
Wherein, For 1 × (Nu+τsc,k- 1) block matrix, from τsc,k+1
BOB(beginning of block) is unit matrix I ∈ Rm×m；Real-time time delay of the k time data packet in feedback channel is τsc,k, Nu- 1 is optimization section
Length；For Np× 1 block matrix,For lower triangular matrix, meet j-i≤τsc,k-1
AndY0(k) target value sequence: Y is followed for system0(k)=[y0(k+1)…y0(k+Np)]T；R, Q is power
Weight coefficient matrix；System output and control input are respectively y (k- τsc,k) and u (k- τsc,k),For the k moment
The last updated State Viewpoint measured value of state observer:
3) time delay compensator
The controlling increment of t moment are as follows:
Wherein,For 1 × (Nu- 1) block matrix, τ* ca,tBlock is unit matrix I ∈ Rm×m,For t
Newest controlling increment sequence, τ in moment compensator* sc,tAnd τ* ca,tThe newest optimal control that respectively time delay compensator is chosen increases
Measure the corresponding time delay of sequence.
Networking tracing control closed-loop system are as follows: x (t+1)=Ax (t)+B [u (t-1)+Δ u (t)].
Compared with prior art, beneficial effects of the present invention: under the background of tracing control, to the network in control system
Inducement is analyzed, and the solution of network delay and packet loss in practical applications is provided.Meanwhile it is pre- in traditional network
It surveys on the basis of control method, the prediction error to introduce very little shortens prediction step as cost, improves predictive controller pair
The compensation effect of network delay is easier to realize compared to traditional neural network forecast control.
Detailed description of the invention
Attached drawing 1 is the integrated stand composition of neural network forecast control system.
Attached drawing 2 is the working principle diagram of networking Predictive Control System.
Attached drawing 3 is the feedback channel network random delay τ of preceding 50 sampling instantsscCurve.
Attached drawing 4 is the forward path network random delay τ of preceding 50 sampling instantscaCurve.
Attached drawing 5 is that the angular speed for the servo-control system that predictive controller is constituted follows curve.
Specific embodiment
The following further describes the specific embodiments of the present invention with reference to the drawings.
The features such as real network is due to topological structure, Internet resources are limited and packet transmission, generates three kinds of shapes to control system
The influence of formula: time delay, packet loss and data incorrect order.Wherein time delay is affected, and other two kinds can pass through data transmission mechanism etc.
Effect is converted to time delay.Consider that network inducement delay, neural network forecast control system integral frame are as shown in Fig. 1.
Neural network forecast controller is made of (shown in 1 dotted line of attached drawing) prediction generator and time delay compensator two parts, is passed
The neural network forecast control method of system needs designated lane transmission of control signals, finally constitutes closed loop by fixed gain state feedback system
System.The method of the present invention is not necessarily to designated lane, shortens prediction step, is finally realized with the state feedback form for determining structure variable element
Closed-loop control.
A kind of network control system tracking and controlling method based on predictive compensation, comprising the following steps:
Step 1: establishing network control system model
Constant multiple-input and multiple-output controlled system, state space description are as follows when a kind of discrete-time linear:
Wherein x (t) ∈ Rn、u(t)∈RmWith y (t) ∈ RlThe respectively state vector of controlled device, control input and control
Output, w (t) ∈ RqFor deterministic perturbation, A ∈ Rn×n,B∈Rn×m,Bw∈Rn×q,C∈Rl×n,Dw∈Rl×qFor sytem matrix, it is
System satisfaction (A, B) is fully controllable, and (C, D) is completely considerable.
Enable system output y (t) tracking external reference input y0(t) asymptotic tracking, and w (t) ≡ 0, i.e. tracking error e are realized
(t) meet:
System mode is not directly surveyed, and state observer observation state, state observer description are established are as follows:
Wherein: x (t+1 | t) is State Viewpoint measured value of the current time to subsequent time, x (t | t-1) and y (t | t-1) distinguish
It is previous moment to the State Viewpoint measured value and output observation at current time, L ∈ Rn*lFor observer gain.
Step 2: prediction generator design
Consider that each control module of closed-loop system meets time synchronization, and remove necessary status information, each data packet has
Time label record sending instant；There is random delay τ in feedback channel and forward pathscAnd τca, delay, τscAnd τcaIt is full respectively
FootThe upper boundNpFor forecast interval length, the upper boundNu- 1 is optimization siding-to-siding block length,
Nu≥Np；Tracking control unit exports control when calculating control amount and the acquisition of control input is in the presence of lagging, and predicts generator
Real-time time delay of the k time data packet in feedback channel is τsc,k, corresponding control output and control input are denoted asy(k-τsc,k) and u
(k-τsc,k)。
Choose tracking control system objective function be
J=[Y0(k)-Y(k|k-τsc,k)]TQ[Y0(k)-Y(k|k-τsc,k)]+ΔUT(k|k-τsc,k)RΔU(k|k-τsc,k) (4)
Wherein, Y (k | k- τsc,k) it is output sequence: Y (k | k- τsc,k)=[y (k+1 | k- τsc,k)…y(k+Np|k-τsc,k)]T,
ΔU(k|k-τsc,k) it is controlling increment sequence: Δ U (k | k- τsc,k)=[Δ u (k- τsc,k+1|k-τsc,k)……Δu(k+Nu-1|
k-τsc,k)]T, Y0(k) target value sequence: Y is followed for system0(k)=[y0(k+1)…y0(k+Np)T], R, Q are weight coefficient square
Battle array.
It is minimised as target with objective function (4), original system augmented conversion is as follows:
Wherein,Δ u (t)=u (t)-u (t-1) augmented system sytem matrix is respectively as follows:
It is described as follows after the variation of observer augmentation:
Wherein, observer gainAnd
Based on the above augmented system, and output sequence Y (k | k- τsc,k) are as follows:
The state estimation value expression at following several moment are as follows:
Formula (8) are substituted into formula (7), are obtained:
WhereinFor Np× 1 block matrix,For lower triangular matrix, meet j-i
≤τsc,k- 1 and For the last updated State Viewpoint measured value of k moment state observer.
It can be calculated by following expression:
The objective function that formula (9) substitute into formula (4) description is sought into optimal value, is enabled:
By Δ U*(k|k-τsc,k) in the optimal control increment sequence of future time instance network compensator is sent to by forward path
Constitute closed loop, the controlling increment sequence of neural network forecast generator output are as follows:
Wherein Mτsc,kFor 1 × (Nu+τsc,k- 1) block matrix, from τsc,k+ 1 BOB(beginning of block) is unit matrix I ∈ Rm×m。
Formula (12) substitutes into formula (11) and obtains:
Wherein,
Step 3: the design of time delay compensator
Based on the above predictive control algorithm, neural network forecast generator compensates the random delay in feedback channel, and
Controlling increment sequence is sent to network delay compensator, realizes closed-loop control to determine the State Feedback Approach of structure variable element.Net
Network time delay compensator chooses current newest controlling increment sequence, before the controlling increment at current time is sent to actuator to compensate
Random delay into channel
The real-time time delay for having received data packet of time delay compensator t moment time delay compensator end buffer area is denoted as τ respectivelyi sc,tWith
τi ca,t| i=1,2 ..., then corresponding loopback delay τi t=τi sc,t+τi ca,t.If the newest optimal control that time delay compensator is chosen
The corresponding time delay of increment sequence is respectively τ* sc,tAnd τ* ca,t, then:
(τ* sc,t,τ* ca,t)=min (τi sc,t+τi ca,t)=min (τi t) i=1,2 ... (14)
T moment, newest controlling increment sequence is in compensatorThe then controlling increment at current time are as follows:
Wherein,For 1 × (Nu- 1) block matrix, τ* ca,tBlock is unit matrix I ∈ Rm×m。
Formula (15) display, the relevant parameter of controlling incrementWithIt is τ* sc,tAnd τ* ca,tFunction, can wait
Valence is that the variable element State Feedback Approach of fixed structure constitutes closed-loop system.
Networking tracing control closed-loop system is described such as formula (16), corresponding closed-loop control system working principle such as attached drawing 2
It is shown.
Embodiment:
Using a kind of network control system tracking and controlling method based on predictive compensation proposed by the present invention, to illustrate this
The validity of literary method considers the design of the Networked controller of DC servo motor control system.It is carried out by the period of 0.04s
Sampling, the discrete time model of servo-control system can be described by following formula:
The object that follows of the servo control system is the angular speed of motor, the sytem matrix of corresponding states spatial model
It is as follows:
Concrete methods of realizing is as follows:
By state Observer Design principle, withPOLE PLACEMENT USING is carried out for target, takes L=[0.1334
-0.0400 0.0541]T。
It is emulated, is taken referring to angular speed in MatlabI=1,2 ..., NpSystem initial value is provided that
Controlled device: x (0)=[0.1 0.1 0.1]T, u (0)=0；
State observer: x (0 | 0)=[0.1 0.1 0.1]T。
When forward path and feedback channel random delay meet respectivelyTime delayWhen, using present invention side
Method designs controller, takes Np=Nu=8.In simulation process, the network random delay actual conditions such as attached drawing of preceding 50 sampling instants
3 and attached drawing 4 shown in.
The servo control system output valve of the method for the present invention design follows situation as shown in Fig. 5, as shown in Figure 5,
The servo-system that the present invention designs can be successfully tracked near the k=17 moment referring to angular speed.The present invention is introducing the pre- of very little
It surveys under error condition, shortens prediction step, networking Predictive Control System is enabled to tolerate bigger time delay.
The above are preferred embodiments of the present invention, is not intended to limit the present invention in any form, all foundations
Technical spirit of the invention any simple modification, equivalent change and modification made to the above embodiment, belong to inventive technique
In the range of scheme.
Claims (1)
1. a kind of network control system tracking and controlling method based on predictive compensation, which is characterized in that neural network forecast controller
It is made of prediction generator and time delay compensator two parts, specifically includes the following steps:
(1) constant multiple-input and multiple-output controlled system, augmented system state space description when a kind of discrete-time linear are as follows:
Wherein,Δ u (t)=u (t)-u (t-1)；x(t)∈Rn、u(t)∈RmWith y (t) ∈ RlRespectively quilt
Control state vector, control input and the control output of object, A ∈ Rn×n,B∈Rn×m,C∈Rl×nFor coefficient matrix；System meets
(A, B) is fully controllable, and (A, C) is completely considerable；System exports y (t) tracking external reference and inputs y0(t) asymptotic tracking is realized, i.e.,
Tracking error e (t) meets:State is not directly
It surveys, establishes state observer:
Wherein, x (t+1 | t) is State Viewpoint measured value of the current time to subsequent time, x (t | t-1) and y (t | t-1) respectively before
State Viewpoint measured value and output observation of one moment to current time, L ∈ Rn*lFor observer gain；Augmentation observer gain
(2) the controlling increment sequence of k moment neural network forecast generator output are as follows:
Wherein, For 1 × (Nu+τsc,k- 1) block matrix, from τsc,k+ 1 piece is opened
Begin to be unit matrix I ∈ Rm×m；Real-time time delay of the k time data packet in feedback channel is τsc,k, NpFor forecast interval length,
Nu- 1 is optimization siding-to-siding block length；For Np× 1 block matrix, For lower triangular matrix,
Meet j-i≤τsc,k- 1 andY0(k) target value sequence: Y is followed for system0(k)=[y0(k+1)…y0
(k+Np)]T；R, Q is weight coefficient matrix；System output and control input are respectively y (k- τsc,k) and u (k- τsc,k),For the last updated State Viewpoint measured value of k moment state observer:
(3) controlling increment of time delay compensator t moment are as follows:
Wherein,For 1 × (Nu- 1) block matrix, theBlock is unit matrix I ∈ Rm×m,For t moment benefit
Repay newest controlling increment sequence, τ in device* sc,tAnd τ* ca,tThe respectively newest optimal control increment sequence of time delay compensator selection
Corresponding time delay；Networking tracing control closed-loop system are as follows: x (t+1)=Ax (t)+B [u (t-1)+Δ u (t)].
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| CN107451704A (en) * | 2017-09-15 | 2017-12-08 | 厦门大学 | A kind of multiple time delay intelligent grid economic load dispatching method based on PREDICTIVE CONTROL |
| CN109283916B (en) * | 2018-10-15 | 2021-02-02 | 浙江工业大学 | Data packet loss compensation method for multi-sensor networked control system |
| CN109856970B (en) * | 2018-12-19 | 2022-01-25 | 哈尔滨理工大学 | Finite time stabilization method with network-induced bounded time lag and data loss |
| CN109613830B (en) * | 2019-01-31 | 2020-04-10 | 江南大学 | Model prediction control method based on decreasing prediction step length |
| CN111045331B (en) * | 2019-12-25 | 2022-05-13 | 北方工业大学 | Networked control system and prediction output tracking control method |
| CN111061154B (en) * | 2019-12-25 | 2022-05-13 | 北方工业大学 | Incremental networked prediction control method and system for engineering control |
| CN111077781B (en) * | 2019-12-25 | 2022-05-13 | 北方工业大学 | Networked control system and output tracking control method thereof |
| CN111025913B (en) * | 2019-12-25 | 2022-05-13 | 北方工业大学 | Networked predictive control method and system for engineering control |
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