Source: http://www.google.com/patents/US6745088?dq=6437692
Timestamp: 2017-05-27 12:56:31
Document Index: 573977029

Matched Legal Cases: ['art 2102', 'art 2104', 'art 2106', 'art 2202', 'art 2204', 'art 2206', 'art 2302', 'art 2304', 'art 2306', 'art 2402', 'art 2404', 'art 2406', 'application No. 60']

Patent US6745088 - Multi-variable matrix process control - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inPatentsComputer-implemented system and method for controlling a processing apparatus having at least one independently controlled manipulated variable and at least one controlled variable responsive to the manipulated variable, using a robust multi-variable controller which defines an expected variation in...http://www.google.com/patents/US6745088?utm_source=gb-gplus-sharePatent US6745088 - Multi-variable matrix process controlAdvanced Patent SearchTry the new Google Patents, with machine-classified Google Scholar results, and Japanese and South Korean patents.Publication numberUS6745088 B2Publication typeGrantApplication numberUS 09/878,711Publication dateJun 1, 2004Filing dateJun 11, 2001Priority dateJun 30, 2000Fee statusPaidAlso published asCA2411378A1, CN1449511A, EP1299778A2, US20020016640, WO2002003150A2, WO2002003150A3Publication number09878711, 878711, US 6745088 B2, US 6745088B2, US-B2-6745088, US6745088 B2, US6745088B2InventorsRonald A. GagneOriginal AssigneeThe Dow Chemical CompanyExport CitationBiBTeX, EndNote, RefManPatent Citations (10), Non-Patent Citations (2), Referenced by (43), Classifications (25), Legal Events (4) External Links: USPTO, USPTO Assignment, EspacenetMulti-variable matrix process control
ΔU=(A t WA)−1 ·A t W·E Equation 10
ΔU=(A t WA)−1 ·A t W·F(models, Y, . . . ) Equation 11
For each CV there are at least two predictions: the one from the Fast Model CVfast and the one from the Slow Model CVslow. Optionally, the prediction from the Reference Model is used along with at least one limit model. The predictions are obtained as in traditional DMC practice with: CV  ( i , k ) = ∑ j = 1 N  ∑ l = 1 np  ( a  ( i , j , l ) * Δ   U  ( j , k + l - 1 ) . Equation 12 However, in the preferred embodiments, there are at least two prediction blocks: one for the Slow Model and one for the Fast Model. It is also preferable to maintain the prediction from the Reference Model, but, again, this is optional.
Another possible function (that has the benefit of being continuous) is the following as obtained by defining a function φ to normalize variations in the predicted CV's: Φ = Y - ( CV fast + CV slow ) 2  CV fast - CV slow  ; Equation 21 it is possible to calculate the desired Y* according to Y * =  CV fast + CV slow 2 + ( Y - ( CV fast + CV slow 2 ) ) *  ( 1 - 1 1 + a   Φ n ) . Equation 22 The value of “a” and “n” are adjusted to provide a behavior that fits statistical expectation. For example, a=1.5 and n=4 produces a function that has desirable effects:
The function term ( 1 - 1 1 + a   Φ n ) Equation 23 is a confidence function that is zero (or close to zero) when the measured output is within the Slow and Fast predictions. In the first example, the confidence interval is defined by CVmax and CVmin. In both cases, the function is a scaling sensitivity factor. The function also jumps (increases) toward unity when the measured output moves away from the predictions; this indicates a confidence that the measured output indicates a strong deviation in the plant (apparatus in operation) needing correction. Also, CVmean (i.e., (CVfast+CVslow)/2) is optionally replaced by CVreference (if available).
ΔU=(A t WA)−1 ·A t W(Y*−CV mean). Equation 24
The model prediction based on the Reference Model at time tk for each CV(i) can be written as: CV  ( i , k ) = ∑ j = 1 N  ∑ l = 1 np  ( a  ( i , j , l ) * Δ   U  ( j , k + l - 1 ) Equation 25 and this is transformed into a larger sampled time interval and into a difference equation: Δ   CV s  ( i , k ) =  ∑ j = 1 N  ∑ l = 1 r  ( a  ( i , j , s * l ) - a  ( i , j , s * ( l - 1 ) ) ) * Δ   U s  ( j , k + l - 1 ) Equation 26 where: N is the number of MV's and FF's
Optionally, a part of the proposed scaling sensitivity factor determines the V function: V  ( i , k ) = ( 1 1 + a   Φ n ) . Equation 35 Whatever the choice, this function is chosen by the user to eliminate (or minimize) the effect of model mismatch caused by model parameters other than gain by using the predictions obtained from at least two limit models. The selected function varies according to the characteristics of the process such as (a) a deterministic process with few disturbances or (b) a process characterized by strong stochastic disturbances. The function V has the effect of (a) screening data containing the most valuable information for adapting the process gains and (b) rejecting some of the transients and disturbances introducing errors in the adaptation process.
This forms the matrix “C” used to solve the following minimization of sum-of-squared error: S  ( i ) = ∑ k  ( λ ( k ) * V  ( i , k ) ) * E  ( i , k ) 2 = ∑ k  H  ( k ) * E  ( i , k ) 2 , Equation 37 the summation being done on available historical data E  ( i , k ) =  Δ   Y s  ( i , k ) - Δ   CV s  ( i , k ) =  Δ   Y s  ( i , k ) - ∑ j = 1 N  ∑ l = 1 r  Δ   G  ( i , j ) *  ( a  ( i , j , l ) - a  ( i , j , l - 1 ) ) * Δ   U s  ( j , k + l - 1 ) . Equations 38-39 To minimize S(i), a set of changes in gain is introduced to provide multipliers of the actual “a” values of each model. These are the ΔG(i,j) multiplying each term of the summation (Equation 39). Therefore, there are as many ΔG as there are models. For future reference, ΔU and ΔCV quantities are collectively denoted as “delta-s quantities”.
ΔG(i)=(C t HC)−1 ·C t HΔY(i) (repeated for each CV i). Equation 40
M=M·ΔG(i). Equation 41
ΔG=(C t HC)−1 ·C t HΔY Equation 42
For eigenvalues: G reference=T−1 ·λ·T; Equation 43
For singular values: G reference =U·Σ·V. Equation 44
For eigenvalues: G* ref,slow,fast =T −1 ·λ*·T; Equation 45
For singular values: G* ref,slow,fast =U·Σ*·V. Equation 46
For eigenvalues: A=T·ΔCV s and B=T·ΔMV s; Equation 53
For singular values: A−U −1 ·ΔCV s and B=V·ΔMV s. Equation 54
When divergence is detected, changes in the model gains G(i,j) (corresponding to the value of λ or Σ, but with the sign inverted) are introduced to produced λd or Σd. This means that (for eigenvalues) the following transformation is accomplished for any small eigenvalues (other eigenvalues may have been adapted as well): ( λ λ …  + ɛ …  λ ) ⇒ ( λ λ …  - ɛ …  λ ) . Equation 55 The new vector, denoted as λd with inverted characteristic values, is then used to back-calculate the final gains:
For eigenvalues: G d ref,slow,fast =T −1·λd T; Equation 56
For singular values: G d ref,slow,fast =U·Σ d ·V. Equation 57
Turning now to FIG. 17, Controller Operation 1700 shows the operational process of MV Determination Logic 206 and Controller 402. In History Update Step 1702, data for existing MV, FF, and CV variables is transmitted to History Block 414 for archival and use in Adaptation Block 416. The history is built for N input (CV) variables, L feed-forward (FF) variables, and M output (MV) variables. In Adaptation Decision Step 1704, an estimated process disturbance value (from the Controlled Variable magnitude) and the estimated modeling error value are calculated. The estimated model error value and the estimated process disturbance error value are then used to determine the need for further adaptation of either tuning data or model data. Details in Model Adaptation Step 1722 (given a YES answer from Adaptation Decision Step 1704) are discussed in Adaptation Methodology Detail 1800 of FIGS. 18A-18E. In CV Prediction Step 1706, the Primary (Reference), Slow, and Fast Models are used to predict steady-state Controlled Variable values. The predictions are done for N input (CV) variables, L feed-forward (FF) variables, and M output (MV) variables. Further detail in this is shown in Future CV Requirement Definition Detail 1900 of FIG. 19. In Steady-State MV Definition Step 1708, Linear Program 426 is called to define steady-state Manipulated Variable values. These values are defined for N input (CV) variables and M output (MV) variables. In Dynamic Matrix Build Step 1710, the Dynamic (ATA) Matrix is rebuilt if tuning is to be changed, if the models are to be changed, or if this is the first execution instance of the process of Controller 402. The ATA Matrix has a dimension of M×M, where M=N (CV variables) multiplied by the number of future MV moves for each MV. In Future CV Requirement Definition Step 1712, necessary future shifts in Controlled Variable values are determined from setpoints and other predicted future values as acquired from the database of Control Computer Logic 120 in CV Data Acquisition Step 1720. Further detail in Future CV Requirement Definition Step 1712 is shown in CV Prediction Detail 2000 of FIG. 20. The shifts are determined for M output variables. In MV Change Definition Step 1714, the ATE matrix and Dynamic Matrix are solved to define incremental changes in Manipulated Variables. In MV Implementation Step 1716, the incremental Manipulated Variable changes are implemented and the process returns to Data Acquisition Step 1718; this affects each of N input variables. In Data Acquisition Step 1718, MV, CV, and FF variables are read from Control Computer Logic 120 (via Communication Interface 106). In CV Data Acquisition Step 1720, setpoints and other predicted future values are acquired from the database of Control Computer Logic 120 along with necessary future shifts in Controlled Variable values.
FIG. 21 presents output 2100 from a simulator for a regular DMC operating in a situation of model mismatch in modeling parameters. Output 2100 contains time chart 2102 for the actions of CV1 and MV1 time chart 2104 for the actions of CV2 and MV2, and time chart 2106 for the actions of CV3 and MV3. FIG. 21 shows the regular DMC in the situation of model mismatch between the simulation of the operating apparatus and the model used in the controller (the case is a 3×3 with model mismatch in only the first model; in this regard, the dead time is incorrectly modeled). The move suppression factor is set to unity. Equal concern errors are also all unity. The simulation is normalized internally so that all values of CV and MV begin at 50.0. The process gains are usually in the 0 to 3.0 range. The figure shows the instability induced in the DMC by a single small error in the dead time model in only one model out of 9 (i.e. 3×3). Each other model parameter is strictly equal between the apparatus simulation and the model of the controller. The dead time error in the first model is about 20%. This error makes the controller unstable in all the 3 CV's; the traditional cure is to increase move suppression but at the expense of controller reaction time to external disturbance and to set point changes.
FIG. 22 presents output 2200 from a simulator for the robust controller of the preferred embodiments operating in the situation of model mismatch of FIG. 21. Output 2200 contains time chart 2202 for the actions of CV1 and MV1, time chart 2204 for the actions of CV2 and MV2, and time chart 2206 for the actions of CV3 and MV3. FIG. 22 shows the robust controller in face of the same model mismatch as FIG. 21 (the case is the same 3×3 with model mismatch in only the first model; the dead time is again incorrectly modelized). The move suppression factor is set to unity. Equal concern errors are also all unity. The simulation is normalized internally so that all values of CV and MV start at 50.0. The figure shows the resulting stability of the robust multivariable controller. The controller is not gaining stability at the expense of the reaction time, and, therefore, disturbances and set point changes can be handled faster than the regular DMC. The robustness is so substantial that move suppression can be zero (i.e. turned off) if there is any reason to do so.
FIG. 23 presents output 2300 from a simulator for a regular DMC operating in a situation of model mismatch in gains. Output 2300 contains time chart 2302 for the actions of CV1 and MV1, time chart 2304 for the actions of CV2 and MV2, and time chart 2306 for the actions of CV3 and MV3. FIG. 23 shows the regular DMC without adaptation. The case is a 3×3 with model mismatch in only the gains of all models. The move suppression factor is set to unity. Equal concern errors are also all unity. The simulation is normalized internally so that all values of CV and MV start at 50.0. The process gains are usually in the 0 to 3.0 range with errors in the range 0 to 50%. The figure shows the instability induced in the DMC by the gain errors. The DMC controller is unstable in all the 3 CV's.
FIG. 24 presents output 2400 from a simulator for the robust controller of the preferred embodiments operating in the situation of model mismatch of FIG. 23. Output 2400 contains time chart 2402 for the actions of CV1 and MV1, time chart 2404 for the actions of CV2 and MV2, and time chart 2406 for the actions of CV3 and MV3. FIG. 24 shows the robust controller with adaptation. The case is the same 3×3 as used in FIG. 23 with model mismatch in only the gains of all models. The move suppression factor is set to unity. Equal concern errors are also all unity. The simulation is normalized internally so that all values of CV and MV start at 50.0. The process gains are usually in the 0 to 3.0 range with errors in the range 0 to 50%. The figure shows some initial instability in the robust controller (area of 2408); this is a learning period. After this learning period, the controller exhibits nearly perfect response to set point changes since it derives, from past data, the correct model gains that match the actual process to the control model. Note also, in comparing the peak in the area of 2408 of FIG. 24 with the comparable peak area of 2308 of FIG. 23, that the controller of FIG. 24 shows less overshoot above the setpoint SP; this demonstrates the efficiency with which the described embodiment of the multi-model controller reacts to the operating system.
Patent CitationsCited PatentFiling datePublication dateApplicantTitleUS4349869Oct 1, 1979Sep 14, 1982Shell Oil CompanyDynamic matrix control methodUS4698745 *Feb 7, 1985Oct 6, 1987Kabushiki Kaisha ToshibaProcess control apparatus for optimal adaptation to a disturbanceUS5394322 *Jul 22, 1993Feb 28, 1995The Foxboro CompanySelf-tuning controller that extracts process model characteristicsUS5432885 *Sep 24, 1992Jul 11, 1995Mitsubishi Denki Kabushiki KaishaRecurrent fuzzy inference apparatusUS6253113 *Aug 20, 1998Jun 26, 2001Honeywell International IncControllers that determine optimal tuning parameters for use in process control systems and methods of operating the sameUS6278899 *Oct 6, 1998Aug 21, 2001Pavilion Technologies, Inc.Method for on-line optimization of a plantUS6381504 *Dec 31, 1998Apr 30, 2002Pavilion Technologies, Inc.Method for optimizing a plant with multiple inputsUS6438532 *Jan 23, 1998Aug 20, 2002Kabushiki Kaisha ToshibaAdjustment rule generating and control method and apparatusUS6529814 *May 14, 2001Mar 4, 2003Nissan Motor Co., Ltd.System and method for controlling vehicle velocity and inter-vehicle distanceUS6587108 *Jul 1, 1999Jul 1, 2003Honeywell Inc.Multivariable process matrix display and methods regarding same* Cited by examinerNon-Patent CitationsReference1U.S. Patent Ser. No. 09/482,386, filed Jan. 12, 2000.2U.S. Provisional application No. 60/215,453, filed Jun. 30, 2000.Referenced byCiting PatentFiling datePublication dateApplicantTitleUS6952620 *Sep 30, 2003Oct 4, 2005Sap AktiengesellschaftDeclaring application dataUS7039559 *Mar 10, 2003May 2, 2006International Business Machines CorporationMethods and apparatus for performing adaptive and robust predictionUS7113834 *Apr 21, 2003Sep 26, 2006Fisher-Rosemount Systems, Inc.State based adaptive feedback feedforward PID controllerUS7203554 *Mar 16, 2004Apr 10, 2007United Technologies CorporationModel predictive controller with life extending controlUS7231264 *Mar 19, 2004Jun 12, 2007Aspen Technology, Inc.Methods and articles for detecting, verifying, and repairing collinearity in a model or subsets of a modelUS7317953Dec 2, 2004Jan 8, 2008Fisher-Rosemount Systems, Inc.Adaptive multivariable process controller using model switching and attribute interpolationUS7444191Oct 4, 2005Oct 28, 2008Fisher-Rosemount Systems, Inc.Process model identification in a process control systemUS7451004 *Sep 30, 2005Nov 11, 2008Fisher-Rosemount Systems, Inc.On-line adaptive model predictive control in a process control systemUS7551969Sep 25, 2006Jun 23, 2009Fisher-Rosemount Systems, Inc.State based adaptive feedback feedforward PID controllerUS7660649 *Jul 5, 2005Feb 9, 2010Optimal Innovations Inc.Resource management using calculated sensitivitiesUS7738975Oct 2, 2006Jun 15, 2010Fisher-Rosemount Systems, Inc.Analytical server integrated in a process control networkUS7856280 *Aug 2, 2006Dec 21, 2010Emerson Process Management Power & Water Solutions, Inc.Process control and optimization technique using immunological conceptsUS7856281 *Nov 7, 2008Dec 21, 2010Fisher-Rosemount Systems, Inc.On-line adaptive model predictive control in a process control systemUS8036760Sep 26, 2008Oct 11, 2011Fisher-Rosemount Systems, Inc.Method and apparatus for intelligent control and monitoring in a process control systemUS8046096May 19, 2010Oct 25, 2011Fisher-Rosemount Systems, Inc.Analytical server integrated in a process control networkUS8280533Jun 22, 2009Oct 2, 2012Fisher-Rosemount Systems, Inc.Continuously scheduled model parameter based adaptive controllerUS8332057Mar 20, 2009Dec 11, 2012University Of New BrunswickMethod of multi-dimensional nonlinear controlUS8509924Dec 10, 2010Aug 13, 2013Emerson Process Management Power & Water Solutions, Inc.Process control and optimization technique using immunological conceptsUS8527252 *Jul 28, 2006Sep 3, 2013Emerson Process Management Power & Water Solutions, Inc.Real-time synchronized control and simulation within a process plantUS8706267Oct 28, 2008Apr 22, 2014Fisher-Rosemount Systems, Inc.Process model identification in a process control systemUS8756039 *Dec 3, 2010Jun 17, 2014Fisher-Rosemount Systems, Inc.Rapid process model identification and generationUS20030195641 *Apr 21, 2003Oct 16, 2003Wojsznis Wilhelm K.State based adaptive feedback feedforward PID controllerUS20040143815 *Sep 30, 2003Jul 22, 2004Markus CherdronDeclaring application dataUS20040181370 *Mar 10, 2003Sep 16, 2004International Business Machines CorporationMethods and apparatus for performing adaptive and robust predictionUS20040249481 *Mar 19, 2004Dec 9, 2004Qinsheng ZhengMethods and articles for detecting, verifying, and repairing collinearity in a model or subsets of a modelUS20050149209 *Dec 2, 2004Jul 7, 2005Fisher-Rosemount Systems, Inc.Adaptive multivariable process controller using model switching and attribute interpolationUS20050209713 *Mar 16, 2004Sep 22, 2005Fuller James WModel predictive controller with life extending controlUS20060015194 *Mar 19, 2004Jan 19, 2006Qingsheng ZhengMethods and articles for detecting, verifying, and repairing collinearity in a model or subsets of a modelUS20070021850 *Sep 25, 2006Jan 25, 2007Fisher-Rosemount Systems, Inc.State Based Adaptive Feedback Feedforward PID ControllerUS20070078529 *Sep 30, 2005Apr 5, 2007Fisher-Rosemount Systems, Inc.On-line adaptive model predictive control in a process control systemUS20070100477 *Mar 19, 2004May 3, 2007Qingsheng ZhengMethods and articles for detecting, verifying, and repairing collinearity in a model or subsets of a modelUS20070142936 *Oct 2, 2006Jun 21, 2007Fisher-Rosemount Systems, Inc.Analytical Server Integrated in a Process Control NetworkUS20080027704 *Jul 28, 2006Jan 31, 2008Emerson Process Management Power & Water Solutions, Inc.Real-time synchronized control and simulation within a process plantUS20080125881 *Aug 2, 2006May 29, 2008Emerson Process Management Power & Water Solutions, Inc.Process control and optimization technique using immunological conceptsUS20090058185 *Jul 7, 2008Mar 5, 2009Optimal Innovations Inc.Intelligent Infrastructure Power Supply Control SystemUS20090143872 *Nov 7, 2008Jun 4, 2009Fisher-Rosemount Systems, Inc.On-Line Adaptive Model Predictive Control in a Process Control SystemUS20090265021 *Mar 20, 2009Oct 22, 2009University Of New BrunswickMethod of multi-dimensional nonlinear controlUS20090319060 *Jun 22, 2009Dec 24, 2009Fisher-Rosemount Systems, Inc.Continuously Scheduled Model Parameter Based Adaptive ControllerUS20100228363 *May 19, 2010Sep 9, 2010Fisher-Rosemount Systems, Inc.Analytical server integrated in a process control networkUS20110144772 *Dec 10, 2010Jun 16, 2011Emerson Process Management Power & Water Solutions, Inc.Process control and optimization technique using immunological conceptsUS20110218782 *Dec 3, 2010Sep 8, 2011Fisher-Rosemount Systems, Inc.Rapid process model identification and generationCN102193500A *Mar 2, 2011Sep 21, 2011费希尔-罗斯蒙特系统公司Rapid process modelidentification and generationCN102193500B *Mar 2, 2011Aug 3, 2016费希尔-罗斯蒙特系统公司快速过程模型识别和生成* Cited by examinerClassifications U.S. Classification700/29, 700/34, 700/71, 318/561, 703/2, 700/46, 700/67, 700/28, 700/31International ClassificationG06F11/14, G05B11/32, G05B13/02, G05B17/02, G05B11/36, G05B13/04Cooperative ClassificationG05B17/02, G05B13/042, G05B13/048, G05B11/36, G05B11/32European ClassificationG05B11/32, G05B13/04D, G05B17/02, G05B11/36, G05B13/04BLegal EventsDateCodeEventDescriptionMar 11, 2004ASAssignmentOwner name: DOW CHEMICAL COMPANY, THE, MICHIGANFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:GAGNE, RONALD A.;REEL/FRAME:015060/0581Effective date: 20000726Sep 21, 2007FPAYFee paymentYear of fee payment: 4Sep 19, 2011FPAYFee paymentYear of fee payment: 8Nov 19, 2015FPAYFee paymentYear of fee payment: 12RotateOriginal ImageGoogle Home - Sitemap - USPTO Bulk Downloads - Privacy Policy - Terms of Service - About Google Patents - Send FeedbackData provided by IFI CLAIMS Patent Services