CN100465825C - Variable structural nonlinear model predictor controller - Google Patents

Variable structural nonlinear model predictor controller Download PDF

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CN100465825C
CN100465825C CNB2007101188643A CN200710118864A CN100465825C CN 100465825 C CN100465825 C CN 100465825C CN B2007101188643 A CNB2007101188643 A CN B2007101188643A CN 200710118864 A CN200710118864 A CN 200710118864A CN 100465825 C CN100465825 C CN 100465825C
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袁璞
张贵礼
金学兰
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袁璞
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Abstract

The invention relates to changeable structure non linear model predictive controller. It is made up of online real time configuration machine, non linear model predictive control calculator, control action output processor, controlled variable constrained optimizer, and operational variable constrained optimizer. The online real time configuration machine can self organize current proper controller structure by which the non linear time lag calculating and on line real time predictive calculating can be processed. It has the features of multi predictive time domain online correction, single value predictive control, state feedback, non linear weighting, multi-cycle control etc, forms multi variable changeable structure non linear model predictive controller and control system on model and structure to adapt controlled process, make it always be in optimize running state.

Description

Variable structural nonlinear model predictor controller
Technical field
The present invention relates to a kind of to continuous change procedure or equipment, particularly, carry out strategy, method and the variable structural nonlinear model predictor controller of Advanced Control (Advanced Control) to the continuous flow procedure of existing dcs (DCS) and the PID controller that comprises thereof.
Background technology
Because PID (ratio, integration, differential) controller is comparatively simple and practical, since last century the forties, in the production run widespread use it control automatically.Along with producing and science and technology development, the frequent variations of the especially production-scale continuous expansion and the market demand,, multivariate big to time lag, constraint is arranged, become the production run of structure, PID control is difficult to satisfy steadily control and the multivariate constraint is coordinated and the control requirement of change structure.
The model pre-estimating control (MPC) that later stage nineteen seventies occurs, utilize the controlled process dynamic mathematical models, state to controlled process future is estimated, and carry out optimum control according to this, for time lag big multivariable process a kind of effective control method is provided, as MAC, DMC, GPC, SFPC, or the like, corresponding commercially available software has also appearred, as IDCOM, DMC-Plus (Aspen Inc.), RMPCT-PROFIT (Honeywell Inc.), STAR (Invensys), VSUPCC, or the like, since the eighties in last century, obtained successful application in process of production.In application process, handling developed and improvement aspect constraint and the multivariate coordination, make MPC obtain more and more widely application.
Practical application has exposed some weakness of MPC: it is relatively poor that the first suppresses to survey interference capability, even do not control as PID.It two is the variation that is difficult to adapt to production run (non-linear and become structure).It three is for obtaining model, carrying out bigger interference to production.This is because most of MPC uses by the linearization dynamic number immunologing mathematics model of surveying the foundation of controlled process inputoutput data, adopts many-valued estimate, handle multivariate coordination, state and output variable constraint, it is comparatively complicated that the model online in real time is calculated, control cycle long (30-60 second, and the control cycle of PID is 0.5-1 second), the measured state variable information that can not utilize controlled process to provide again.Only based on the VSUPCC software of Chinese patent " 99105546.2 universal multi-variable quantity model pre-estimating coordinating control method ", employing is based on the state-space model of Analysis on Mechanism, need not disturb the test of production, can introduce the state variable feedback, improve and suppress interference capability, and adopt monodrome to estimate strategy, keeping under the identical control effect, online in real time is calculated greatly to be simplified, realized the multicycle control of control cycle 5-60 between second, improve the control effect, can better handle multivariate coordination problem.But it is also the same with other MPC, all be based on inearized model, and actual controlled process or equipment are nonlinear often, especially product demand difference, raw material variation, load variations, environmental change, or the like under the situation, making controlled process is to become structure and nonlinear, promptly needs to change with possible controlled variable quantity and applicable performance variable quantity; The accurate description of controlled process or equipment is non-linear, is difficult to obtain the control effect of satisfaction based on the MPC of inearized model.
Since the last century the eighties, many relevant research papers that utilize nonlinear model to carry out Prediction Control (NLMPC) have appearred, proposed on the linearization basis, to add non-linear correction, gain planning, multi-model, local adopt nonlinear model calculate, or the like still based on the algorithm of inearized model.Some algorithms based on nonlinear model have also appearred.The commercially available software of some nonlinear model predictors controls has appearred in the last century end and the beginning of this century, as the Pavilion of NOVA-NLC, the PAVILION of APOLLO, the PAS of Aspen, or the like.Because nonlinear model can not be used superposition principle, general no analytic solution, control strategy adopts and many-valuedly to estimate, state constraint, add that controlled process is multivariable, make calculating quite complicated, these softwares all will be made some approximate processing, and effect is restricted, and also do not have clear and definite solution to become the method for structure problem.
Summary of the invention:
The objective of the invention is to provide a kind of strategy and method based on nonlinear dynamic mathematical model, the nonlinear model predictor control strategy that adapts to controlled process and equipment characteristic and formation multivariate variable structural nonlinear model predictor controller (being called for short VSNMPC), the not only variation of (non-linear) adaptation controlled process on model, also structurally adapt to the variation of controlled process, make control system have more vitality, provide optimum control structure and control effect at any time.
The variable structural nonlinear model predictor controller that the present invention provides mainly comprises following five part compositions: online in real time configuration device 1; Nonlinear model predictor control counter 2; Performance variable output processor 3; Controlled variable constrained optimization device 4; Performance variable constrained optimization device 5.When enforcement is of the present invention, also need the following support equipment that is connected with variable structural nonlinear model predictor controller: slip-stick artist interface 9, operation interface 8, real-time data base 9, dcs 10, controlled process or equipment 6.These support equipments are to possess in controlled process or the equipment a bit, and other can adopt existing products.
More than its annexation of listed ingredient see Fig. 1.The input end of online in real time configuration device 1 connects slip-stick artist interface 7, operation interface 8, real-time data base 9, dcs 10, output terminal connects nonlinear model predictor control counter 2, controlled variable constrained optimization device 4 and performance variable constrained optimization device 5 input ends, also attended operation variable output processor 3 input ends.Controlled variable constrained optimization device 4 output terminals connect nonlinear model predictor control counter 2 input ends, nonlinear model predictor control counter 2 output terminals connect control action output processor 3 input ends, performance variable constrained optimization device 5 output terminal attended operation variable output processors 3 input ends, performance variable output processor 3 output terminals connect dcs 10, dcs 10 connects controlled device 6, and nonlinear model predictor control counter 2 connects real-time data base 9 and slip-stick artist interface 7.By online in real time configuration device 1 variation of detection controlled process structure in real time, determine the only control system structure of current time according to the multivariate co-ordination principle; Calculate by current optimum structure and corresponding nonlinear model by nonlinear model predictor control counter 2 and to provide current control action increment, provide actual adjustment amount by control action output processor 3 again, realize Advanced Control controlled process; Simultaneously, online in real time configuration device 1 gives the current controlled variable that is optimized and behaviour does variable, implements tuning by controlled variable constrained optimization device 4, performance variable constrained optimization device 5 respectively.
Major technique characteristics of the present invention and theing contents are as follows:
1. based on nonlinear state spatial model with multiple time lag:
X . ( t ) = dX ( t ) dt = F [ X ( t - τ A ) , U ( t - τ B ) , V ( t - τ F ) ] - - - ( 1 )
Y(t)=G[X(t),V(t)] (2)
Wherein: F, G are given functional vector
X ∈ R n(state variable SV) Y ∈ R r(controlled variable CV)
U ∈ R m(performance variable MV) V ∈ R q(can survey disturbance variable FV)
τ A, τ B, τ FBe respectively SV, MV, (be SV the retardation time of FV, MV, the function of FV) matrix is considered state variable, performance variable and can survey disturbance variable the influence of state variable is all had time lag, and be state variable, performance variable and the function that can survey disturbance variable retardation time, is the actual conditions of many controlled processes and equipment, also is one of principal feature of the present invention.
2. replace state variable constraint and multiple controlled variable setting with Region control
Except that the controlled variable of general indication, many state variables of controlled process need remain in the bound of permission.The present invention is converted to regional controlled variable with these variablees, when estimating it and will transfinite, it is implemented control, estimates when not transfiniting, not as controlled variable.Make the model pre-estimating control algolithm be converted into " not having constraint " shortcut calculation.
The present invention is divided into float area and two kinds of controlled variables of fixed area with regional controlled variable, and the bound of float area can be floated with other variable.The present invention also can be provided with " condition controlled variable ", promptly under certain condition just as controlled variable, to adapt to the demand that the controlled process multivariate is coordinated, can realize these control requirements by change structure control strategy provided by the invention.
3. monodrome is estimated nonlinear weight control
The present invention adopts following optimization aim to calculate current control action:
Min ΔU ( k ) ∈ R m [ J ] : J U ( k ) ∈ R m = Σ i = 1 r E T ( P ) WE ( P ) - - - ( 3 )
Wherein: P=[p 1P r] TIt is the time domain of estimating of each controlled variable
E T(P)=[e 1(p 1) ... e r(p r)] be that controlled variable is estimated deviation
e i ( p i ) = Y i s - Y 0 i c ( k + p i / k )
Y 0 i c ( k + p i / k )
w iThe weighting coefficient of=the i controlled variable
The characteristics of this algorithm are:
1. to each controlled variable, only calculate the optimum control effect with a discreet value in its following a certain moment (estimating time domain P);
2. only calculate variation delta U (k)=U (the k)-U (k-1) of the control action of current time, after Constraints Processing, carry out;
More than two characteristics this algorithm is simplified greatly than common MPC algorithm, the appropriate selection estimated time domain P, can obtain identical control effect.
3. m≤r, promptly performance variable can be less than or equal the number of controlled variable, by weight matrices W each variable is coordinated;
4. nonlinear weight: weighting coefficient changes with the deviation of controlled variable or near the degree of constraint limit, so that a plurality of controlled variables are coordinated, each variable is not transfinited.
5. morbid state is handled: suitably select weighting coefficient and performance variable, the variable (morbid state) that makes this algorithm permission simple crosscorrelation is simultaneously as controlled variable, and system still can normally move.
4. have feedback of status and the nonlinear model predictor control algolithm of estimating feedforward
The nonlinear state spatial model that utilizes (1) (2) formula to provide, the value of estimating the time domain moment controlled variable future is estimated and revised in real time, and then calculate the optimum control effect of estimating deviation and satisfying above-mentioned optimization aim (3), be the basic ideas of Prediction Control algorithm.At the characteristics of nonlinear model predictor control, the present invention controls the time of running at each, does following calculating:
1. calculate to determine each of τ retardation time A, τ B, τ F
2. calculate the currency of each state variable with the state observer method, comprise and to survey and can not survey state variable.
3. the state variable of calculating with current actual measurement state variable or observation that can not survey is an initial value, calculates the step response of each controlled variable to each performance variable, and estimates time domain β relatively according to what set I, jThat determines the pairing of each controlled variable-performance variable estimates time domain p I, j
Provide and estimate the time domain valuation Y (k+P/k) of controlled variable constantly future.
Annotate: k+P/k represents that the state with current time k is an initial value, and following (k+P) value is constantly estimated.
4. calculate the relative step response matrix of current time:
S i,j(p i)=Y i,j(k+p i/k)-Y 0,i,j(k+p i/k)
Wherein: Y 0, i, j(k+p iIt is initial value that //k) calculated (can not survey) state variable with current actual measurement state variable or observation, at current and following performance variable constantly with can survey under the condition that disturbance variable remains unchanged, each controlled variable that is calculated by (1) (2) formula is at k+p constantly in future iThe time response.
5. the model pre-estimating value is done to estimate more the online in real time correction of time domain:
State when estimating time domain in the past with current time is an initial value, is estimated the value of calculating each controlled variable current time by (1) (2) formula:
Y i(k/k-p i), Y i(k-σ i/ k-p ii), Y i(k+ σ i/ k-p i+ σ i) i=1,2 ..., r provides the mean value that the current time difference is estimated time domain CV discreet value:
Y i ‾ ( k / k - p i ) = Y i ( k / k - p i ) + Y i ( k - σ i / k - p i - σ i ) + Y i ( k + σ i / k - p i + σ i ) 3
Wherein: σ iBe the positive integer that to set.
The corresponding online trim amount of discreet value is: δ M i = Y i ( k ) - Y i ‾ ( k / k - p i )
Through i controlled variable of online feedback correction at (k+p constantly in future i) discreet value be:
Y i c(k+p i/k)=Y 0i(k+p i/k)+δM i (6)
6. calculate and estimate deviation E:
Set point is controlled: E i = Y i s - Y i c ( k + p i / k )
To Region control:
When: LLM i < Y 0 i c ( k + p i / k ) < HLM i , E i=0;
When: Y i c(k+p i/ k)〉HLM i, E i=Y i c(k+p i/ k)-HLM i
When: Y i c(k+p i/ k)<LLM i, E i=Y i c(k+p i/ k)-LLM i
HLM i=upper limit LLM i=lower limit
7. calculate the weighting coefficient of each controlled variable of current time
W i=W iOW ie (7)
Wherein: w I0=initial weighting coefficients, w Ie=with the weighting coefficient of estimating the deviation size variation
8. the objective function that provides according to (3) formula, calculate and provide current control action:
ΔU(k)=U(k)-U(k-1)=[S T(P)WS(P)] -1S T(P)WE (8)
More than (1)-(8) formula, be the imbody of algorithm characteristic of the present invention.These algorithms are finished by " 2. nonlinear model predictor control counter " among Fig. 1.
5. become structure-determine current controller architecture automatically by " 1. online in real time configuration device "
The variation of controlled variable and available action variable quantity is the importance that controlled process changes, for adapting to this variation of controlled process, by " online in real time configuration device ", realize becoming the structural model Prediction Control, be that its major function of another characteristics of the present invention is as follows:
1. constantly detect the controlled process operation conditions in each control, determine following content in real time:
CV: the controlled variable that needs (estimated deviation or exceeded little deviation district, possessed certain condition) and possible (non-fault and be allowed to);
MV: the performance variable of available (comprise and allow that use, trouble-free, and the not super upper limit of correlated variables RV or not super lower limit) own;
SV: the surveyed state variable that can be used as (trouble-free) feedback of status;
FV: the actual measurement that can do (trouble-free) feedforward is disturbed or the observation disturbance variable;
2. when controlled variable breaks down, withdraw from advanced person's control (becoming conventional PID control) or (when fault is arranged) automatically and carry out " degradation " (changing control system structure) or " upgrading " (after fault is eliminated, reverting to original control system structure) processing automatically.
3. determine the optimum controlled variable-performance variable pairing of current time: provide in above detection on the basis of information, control priority and the control priority of optimizing priority, performance variable and optimization priority according to controlled variable, constantly control system is carried out structural coordination in each control, determine the optimal control system structure and the multivariate coordinate scheme of current time, calculating for the online in real time of above-mentioned model pre-estimating control action provides the foundation of determining to estimate time domain, control system structure, controlled variable weighting coefficient.
Whether 4. definite current time needs and may carry out constrained optimization and coordination to controlled variable-performance variable.
5. provide the information that controlled variable does not have performance variable, can be by the order of setting, (but in no performance variable time spent) withdraws from advanced control automatically, or (after having the available action variable again) recovers advanced control automatically.
6. the constrained optimization of controlled variable-performance variable and coordination
1. the constrained optimization of controlled variable: when requiring certain controlled variable to reach given optimization range, provide the information that related variable does not transfinite by " 1. online in real time configuration device " (see figure 1), by " 4. controlled variable constrained optimization device " (see figure 1) with the set-point of the controlled variable given optimal value of tuning progressively.In case forecast has variable to transfinite or reaches given optimization range, tuning stops at once.When not carrying out tuning, if related variable forecast transfinites, and when not having other means, can adjust the set-point of optimised controlled variable, related variable is not transfinited.Characteristics of the present invention are to be provided with to optimize the zone, in the time of in controlled variable reaches the optimization zone, promptly no longer adjust, and prevent vibration.
2. the unsteady coordination of controlled variable:
The controlled variable set-point can float with other variable by following two kinds of methods:
● proportional unsteady with the unsteady correlated variables of other controlled variable:
&Delta; SP fcv ( k ) = &Sigma; j = 1 N r &mu; j &Delta; R j ( k - &tau; j ) - - - ( 9 )
Wherein: μ j=CV float related coefficient (j=1 ..., Nr)
Δ SP Fcv(k)=SP Fcv(k)-SP Fcv(k-1) SP Fcv(k) be the current set-point of CV that floats
if:[R j(k)>R j.hlm]or[R j(k)<R j,llm]or[R j,llm=R j,hlm]
ΔR j(k)=R j(k)-R j(k-1)
else?if:[R j,hlm>R j(k)>R j,llm]ΔR j(k)=0
R j(k)=and the relevant variable currency that floats of a j CV, τ j=retardation time
N r=CV correlated variables the number of floating
R J, hlm, R J, llmBe respectively the bound of the unsteady correlated variables of CV
When needs float, remain unchanged behind the controlled variable set-point tracking measurement value certain hour.
3. the constrained optimization of performance variable: provide the performance variable that those need and may (do not need use as controlled variable control, non-fault, permission performance variable) optimization by " 1. online in real time configuration device ", according to the priority orders that the performance variable of setting is optimized, push performance variable to the optimal value (see figure 1) step by step by " 5. performance variable constrained optimization device " seriatim.Its method one is: change progressively that PID is given to make it move towards to optimize the zone, method two is to keep that PID is given to have certain deviation with measured value, makes PID output move towards to optimize regional.
7. control action is exported " the 3. performance variable output processor " among processing-Fig. 1, has following function:
1. the conversion of model pre-estimating control action output: except that bound and the constraint of speed limit, during practical application, often with the controller of PID controller or two PID series connection as carrying out link, the present invention can be provided with and keep and do not keep two kinds of selections of PID closed-loop control.When not keeping the PID closed loop, the present invention provides and does not keep closed-loop control, allows to adjust pid parameter and impregnable transfer algorithm:
&Delta;MV ( k ) = 1 K pid [ &Delta;U ( k ) + PV ( k ) - PV ( k - 1 ) ] - - - ( 10 )
Δ U (k)=model pre-estimating calculates the adjustment amount of gained control action current time
Actual MV (PID the is given) adjustment amount of Δ MV (k)=current time
K PidThe enlargement factor of=PID controller
PV (k), the controlled variable of PV (k-1)=current and last control PID control constantly
SP(k)=SP(k-1)+ΔMV(k)
Wherein: SP (k)=current time PID controller set-point (11)
The two-way unperturbed of 2. advanced control and conventional PID control system switches: no matter switch to advanced control from conventional PID, or switch to conventional PID control (containing the automatic switchover of barrier for some reason or other reasons) by advanced person control, all keep the set-point of PID controller constant during switching.
3. output keeps: when the corresponding PID of performance variable (PID is given) institute not as the performance variable of current model pre-estimating control, and need remain on the certain numerical value, be called the output maintenance.
First kind of situation: because of performance variable and correlated variables reaches the upper limit or lower limit can not use as performance variable, but will keep performance variable and crucial directly related with performance variable variable thereof on bound, not continuing transfinites, and prevents the integration saturated phenomenon of PID; Or when performance variable and the crucial variable directly related with performance variable transfinite the adjustment limit value, still performance variable to be remained on the new bound.The present invention provides following algorithm (being limited to example on super):
If:[MV can not be to raising]
if{[OP(k)>(OP hlm+BL)]and?abs[SP(k-1)-PV(k)]≤2δ}
MV(k)=PV(k)-δ[OP(k)-(OP hlm+BL)]; (12)
else?MV(k)=SP(k-1)-δ;
SP(k)=MV(k);
Wherein: SP (k), PV (k) are respectively set-point and the measured values of (as performance variable) PID;
OP (k), OP Hlm(it is directly related to be called the key operation variable for the output of=PID controller
Variable) and limit value;
BL〉0, δ〉0 be respectively can online adjustment parameter.
Second kind of situation: variable does not transfinite, but is not selected as model pre-estimating control operation variable, and pid control circuit that neither the constrained optimization variable needs to keep PID output (variable valve) constant.The invention provides following two kinds of disposal routes:
● the given tracker measured value of PID controller;
● be changed to " condition " controlled variable;
4. to electing the performance variable of constrained optimization as, press the constrained optimization rule and adjust performance variable.
The controller that constitutes by the present invention is called VSNMPC, and its theory diagram is seen Fig. 1.
The present invention calculates through non-linear time lag, the nonlinear model online in real time is estimated calculating, estimate the online correction of time domain more, SINGLE PREDICTION PREDICTIVE CONTROL, feedback of status, nonlinear weight, the multicycle control algolithm, provide the adjustment amount of each control model pre-estimating control constantly, be converted to the set-point of PID controller commonly used again, form closed-loop control system, detect the running status of controlled process in real time, structure and action command according to current controlled process, the online in real time self-organization is fit to controller architecture and the corresponding controller parameter and the algorithm of present case, form multivariate and coordinate variable structure control system, not only on model but also structurally adapt to the variation of controlled process, the controlled variable real-time constraint is optimized and is floated and coordinate, the performance variable real time coordination is optimized, and makes controlled process all be in the optimization running status in all cases.
Description of drawings
Fig. 1 variable structural nonlinear model predictor control system (VSNMPC) composition frame chart
CV: controlled variable SV: state variable MV: performance variable
RV: the variable FV directly related: can survey or observable interference with performance variable
Fig. 2 nonlinear model predictor control system implementing procedure.
The application synoptic diagram of Fig. 3 VSNMPC on continuous process units.
Embodiment
Enforcement of the present invention can progressively be carried out in proper order by flow process shown in Figure 2.Wherein:
" 1. online in real time configuration device " contains data and reads in module 1.1, and performance variable availability judge module 1.2 can be surveyed or observable interference availability judge module 1.3, determines current controlled variable module 1.4, controlled variable-performance variable matching module 1.5.
" 2. nonlinear model predictor control counter " contains: state observation calculates and state variable availability judge module 2.1, determines weighting coefficient module 2.2, control law computing module 2.3.
" 3. performance variable output processor " contains: performance variable keeps module 3.1, SP tracking module 3.2, output processing module 3.3.
Read in module 1.1 by data and read the controlled variable that the slip-stick artist sets, performance variable, the variable directly related with performance variable, state variable can be surveyed or the numerical value of observable interference; Read the order that comes into operation of controller that slip-stick artist and operator set.
Do to judge by performance variable availability judge module 1.2:
1. whether the PID controller as performance variable is cut to " CAS " or " long-range given " (reading information by operation interface).
2. performance variable and the variable directly related non-fault (providing) whether by RTDB with performance variable.
3. whether performance variable is allowed to come into operation, and whether performance variable and the variable directly related with performance variable not super bound (slip-stick artist interface).
Whether other PID system that 4. guarantees the normal operation of performance variable drops into closed-loop control (RTDB).
Provide information such as " can freely adjust ", " can only to raise ", " can only to downward modulation ", " unavailable " according to above judgement.
Can survey or observable interference availability judge module 1.3: allowing use and trouble-free actual measurement interference or observation to disturb can be as feed forward variable.
State observation calculates and 2.1 pairs of all state variables of state variable availability judge module are all observed calculating with model.Calculate if actual measurement state variable and observation (can not survey) state variable are carried out model pre-estimating, constitute feedback of status.If when the actual measurement state variable has fault, or the slip-stick artist sets when not allowing to use, and uses the observation computing mode and carries out model pre-estimating.
Determine current controlled variable module 1.4: in all controlled variables that controlling schemes is set, according to the current controlled variable that drops into control of following conditional decision:
1. whether allow to come into operation (reading the order that comes into operation respectively by slip-stick artist interface and operator interface)
2. whether the crucial controlled variable (slip-stick artist's setting) that this controlled variable is relevant comes into operation
3. non-fault, and available performance variable is arranged.
Determine controlled variable weighting coefficient module 2.2: weighting coefficient is the function that controlled variable (when current and future operation variable is constant) is estimated deviation E, need carry out model pre-estimating and calculate.
Controlled variable-performance variable matching module 1.5: according to current permission and may drop into the controlled variable of control, performance variable, can survey or controlled variable-performance variable pairing and priority that observable interference and slip-stick artist set, determine current only controlled variable-performance variable pairing, determine four kinds of application of performance variable: control, constrained optimization, constraint keep and SP follows the tracks of; Determine to push to the controlled variable of optimal value simultaneously.
Control law computing module 2.3:, calculate the optimum control effect by (1)-(8) formula according to controlled variable-performance variable pairing that online configuration is determined.
Performance variable keeps module 3.1: when reaching the constraint limit performance variable can not be used because of the correlated variables of performance variable, the variable directly related with performance variable remained on the constraint limit, prevent the influence that the PID integration is saturated.Or keep PID output constant under certain condition.
SP tracking module 3.2: when the PID controller as performance variable does not switch to " CAS " or " long-range given ", make performance variable output tracking PID given on the spot, switch to this control by PID control to guarantee unperturbed ground.
Performance variable constrained optimization device 5: press performance variable constrained optimization control law and adjust performance variable.
Performance variable output processing module 3.3: to above " control operation variable ", " performance variable maintenance ", " SP tracking " exports to DCS and operator interface after the performance variable under " performance variable optimization " various situations is handled.
Controlled variable constrained optimization device 4: press controlled variable constrained optimization control law and adjust the controlled variable set-point.
For implementing the present invention, also need following support equipment:
10.DCS or other has the system of PID control, realizes the control that this controller provides by this system.
9. real-time data base RTDB: gather the controlled process variable by data-interface by DCS, comprise controlled variable, performance variable, the variable directly related with performance variable, state variable can be surveyed or observable interference and other related data, is the foundation of calculating and controlling.
7. slip-stick artist interface: controller is set and monitored, comprise the used variable controlled variable of this controller, performance variable, the variable directly related with performance variable, state variable can be surveyed or the setting of observable interference, the setting of all variable bounds, speed limit and the order that comes into operation, the setting of model and model parameter and controller parameter, the setting of controlled variable-performance variable pairing and priority, or the like.All setting datas all can leave among the RTDB, can adjust when controller moves.Simultaneously, controller is given RTDB with its running state data, so that the operation of controller is monitored.Relevant data among demonstration of trend display and the record RTDB monitors controller and controlled process ruuning situation.
8. operation interface:, comprise the come into operation switch and the state that comes into operation of each controlled variable, the availability of performance variable and the state that comes into operation, the adjustment of controlled variable set-point, the failure message of controlled variable etc. for the operator provides the interface that this controller is operated.Usually operator interface can be realized at the operating terminal of DCS or other classical control system.
Above support equipment all can utilize existing commercial equipment and software to realize.
Application examples:
Fig. 3 is the general structure that VSNMPC of the present invention uses in petrochemical production device, wherein:
1. the variable structural nonlinear model predictor controller VSNMPC that provides for the present invention
2. be that soft instrument 4. based on nonlinear model is data input/output interface (Data I/O)
5. be that PID controller (in DCS) 6. is controlled production run table
7. be that slip-stick artist interface 8. is operation interface (in the DCS operating side)
9. be that real-time data base RTDB 10. is DCS
The controlled variable of different process units and performance variable are as shown in Table 1.
Table one: VSNMPC is application examples in petrochemical production device

Claims (6)

1. variable structural nonlinear model predictor controller, by online in real time configuration device (1), nonlinear model predictor control counter (2), performance variable output processor (3), controlled variable constrained optimization device (4), (5) five parts of performance variable constrained optimization device are formed, the support equipment that need connect during enforcement comprises slip-stick artist interface (7), operation interface (8), real-time data base (9), dcs (10), controlled process or equipment (6), it is characterized in that: detect the variation of controlled process structure in real time by online in real time configuration device (1), determine the only control system structure of current time; Calculate by the corresponding nonlinear model of current structure by nonlinear model predictor control counter (2) again and provide current control action increment, realize Advanced Control through performance variable output processor (3), simultaneously, online in real time configuration device (1) gives current needs and controlled variable that may be optimized and performance variable, implement tuning by controlled variable constrained optimization device (4), performance variable constrained optimization device (5), form the multivariate variable structural nonlinear model predictor controller.
2. variable structural nonlinear model predictor controller according to claim 1 is characterized in that: online in real time configuration device (1) includes data and reads in module (1.1); Performance variable availability judge module (1.2); Actual measurement is disturbed or observation disturbance variable availability judge module (1.3); Determine the module (1.4) of current needs and possible controlled variable; Controlled variable and performance variable matching module (1.5); Controlled variable and performance variable matching module (1.5) are read in module (1.1) according to data, performance variable availability judge module (1.2), the information that actual measurement is disturbed or the module (1.4) of observation disturbance variable availability judge module (1.3) and definite current needs and possible controlled variable provides, multivariate association rule, controlled variable and performance variable priority, controlled variable estimates deviation and controlled process morbid state is handled, the only controller architecture of the current time that provides: comprising: the used controlled variable of current controller, performance variable and actual measurement are disturbed or the observation disturbance variable, current controlled variable and the performance variable that needs optimization, the current performance variable that needs maintenance and follow the tracks of.
3. variable structural nonlinear model predictor controller according to claim 1, it is characterized in that: nonlinear model predictor control counter (2) is estimated the future value of calculating controlled variable, is estimated deviation and Prediction Control effect according to having the nonlinear state spatial model that lags behind state and input time, comprising:
(1) based on nonlinear state spatial model with multiple time lag:
X . ( t ) = dX ( t ) dt = F [ X ( t - &tau; A ) , U ( t - &tau; B ) , V ( t - &tau; F ) ]
Y(t)=G[X(t),V(t)]
Wherein: F, G are given functional vector
X ∈ R nState variable Y ∈ R rControlled variable
U ∈ R mPerformance variable V ∈ R qCan survey disturbance variable
τ A, τ B, τ FBe respectively state variable, performance variable can be surveyed matrix retardation time of disturbance variable
(2) be the function of state and input variable time lag, calculates constantly in each control and determine;
(3) constantly calculate all state variables in each control: in calculating,, then, form feedback of status with calculating in its substitution model if state variable can be surveyed or non-fault; To can not surveying state variable, or out of order measured state variable, calculate with calculated value substitution model;
(4) constantly calculate to-be and controlled variable discreet value in each control by the nonlinear state spatial model, and controlled variable relative step response S (P) that the performance variable step is changed:
Wherein: S I, j(p i)=Y I, j(k+p i/ k)-Y 0, i, j(k+p i/ k)
Y I.j(k+p i/ k)=make j performance variable after current time generation step changes, the p in future that calculates with the nonlinear state spatial model jI controlled variable value during individual control cycle;
Y 0, i, j(k+p i/ k)=at current time with keep later on performance variable and disturbance variable constant, the p in future that calculates with the nonlinear state spatial model iI controlled variable value during individual control cycle;
(5) estimate time domain and control cycle by what S (P) determined the pairing of each controlled variable-performance variable, form self-adjusting multicycle control strategy;
(6) estimate the online correction of time domain: the online in real time correction is carried out in the controlled variable discreet value more:
Y i c ( k + p i / k ) = Y 0 i ( k + p i / k ) + &delta; M i p i=estimate time domain
δM i=Y i(k)-Y i(k/k-p i)
Y &OverBar; i ( k / k - p i ) = Y i ( k / k - p i ) + Y i ( k - &sigma; i / k - p i - &sigma; i ) + Y i ( k + &sigma; i / k - p i + &sigma; i ) 3
(7) constraint of controlled process state variable bound is converted to regional controlled variable, and multiple controlled variables such as set point control, fixed area control, float area control and condition are set;
(8) SINGLE PREDICTION PREDICTIVE CONTROL is calculated: calculate each controlled variable constantly in each control and estimate the time domain adjustment amount Δ U (k) that estimates deviation E and Prediction Control constantly in future:
E T(P)=[e 1(p 1) ... e r(p r)] be that controlled variable is estimated deviation
Y sThe set-point of=the i controlled variable CV, Region control are zone boundary or upper lower limit value
The adjustment amount of Prediction Control is:
ΔU(k)=U(k)-U(k-1)=[S T(P)WS(P)] -1S T(P)WE
w iThe weighting coefficient of=the i controlled variable
W i=estimate the weighting coefficient of deviation size variation with controlled variable CV
4. variable structural nonlinear model predictor controller according to claim 1 is characterized in that: performance variable output processor (3) comprises the processing of all possible performance variable:
(1) when performance variable is selected as the adjustment variable of model pre-estimating control, if the practical operation means are the set-points of PID in the PID control system, have to keep and do not keep two kinds of selections of PID closed-loop control, do not keep closed-loop control, allow to adjust pid parameter and impregnable transfer algorithm is as follows:
&Delta;MV ( k ) = 1 K pid [ &Delta;U ( k ) + PV ( k ) - PV ( k - 1 ) ]
Δ U (k)=model pre-estimating calculates the adjustment amount of gained control action current time
The practical operation variable adjustment amount that Δ MV (k)=current time PID is given
K PidThe enlargement factor of=PID controller
PV (k), the controlled variable of PV (k-1)=current and last control PID control constantly
SP 1(k)=SP 1(k-1)+Δ MV (k) SP 1(k)=current time PID controller set-point
(2) when performance variable is selected as output and keeps: when performance variable and actual measurement is disturbed or observation disturbance variable when reaching the upper limit or lower limit and can not use as control, need hold it on the upper lower limit value, or when PID is not selected as performance variable and PID output need be remained unchanged, control law computing module (2.3) is chosen to be model pre-estimating control output and keeps, given by adjusting PID, make the given tracking measurement according to certain rules of PID or adjust PID according to certain rules given, or be made as the condition controlled variable, reach output and keep; The two-way unperturbed of advanced control and conventional PID control system switches: no matter switch to advanced control from conventional PID control, or switch to conventional PID control by advanced person's control, all keep the set-point of PID controller constant during switching.
5. variable structural nonlinear model predictor controller according to claim 1, it is characterized in that: for controlled variable constrained optimization device (4): when online in real time configuration device (1) need to determine the controlled variable constrained optimization, with the controlled variable set-point given optimal value of tuning progressively, in case forecast has variable to transfinite or reaches given optimization range, tuning stops at once, when not carrying out tuning, if the related variable forecast is transfinited, and when not having other means, can adjust the set-point of optimised controlled variable, related variable is not transfinited, when controlled variable reaches in the optimization zone of setting, promptly no longer adjust, prevent vibration, when online in real time configuration device (1) is defined as the controlled variable universal time coordinated that need float, the set-point of controlled variable can float with other variable by following two kinds of methods: float with other controlled variable is proportional; When needs float, remain unchanged behind the controlled variable set-point tracking measurement value certain hour.
6. variable structural nonlinear model predictor controller according to claim 1, it is characterized in that: for performance variable constrained optimization device (5): when online in real time configuration device (1) determines that performance variable need retrain coordination optimization, to needs and the performance variable that may optimize, according to the optimization order of setting, push performance variable to optimal value step by step by " performance variable constrained optimization device " seriatim, its method one is: change progressively that PID is given to make it move towards to optimize the zone, method two is to keep that PID is given to have certain deviation with measured value, makes PID output move towards to optimize regional.
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