# US 7,610,108 B2 · Method and apparatus for attenuating error in dynamic and steady-state processes for prediction, control, and optimization > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 7,610,108 B2 - Document ID: US7610108B2 - Status: Granted - Kind: B2 - Application number: 11/359,295 - Filed: 2006-02-21 - Published: 2006-10-12 - Granted: 2009-10-27 - Earliest claimed: 1996-05-06 - Assignees: Rockwell Automation Technologies, Inc. - Inventors: Eugene Boe; Stephen Piche; Gregory D. Martin - Web page: https://patentgrep.com/patent/US7610108B2 ## CPC - G05B 13/042: {in which a parameter or coefficient is automatically adjusted to optimise the performance} - G05B 13/027: {using neural networks only} - G05B 13/048: {using a predictor} - G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G) ## Abstract A method for providing independent static and dynamic models in a prediction, control and optimization environment utilizes an independent static model (20) and an independent dynamic model (22). The static model (20) is a rigorous predictive model that is trained over a wide range of data, whereas the dynamic model (22) is trained over a narrow range of data. The gain K of the static model (20) is utilized to scale the gain k of the dynamic model (22). The forced dynamic portion of the model (22) referred to as the bi variables are scaled by the ratio of the gains K and k. The bi have a direct effect on the gain of a dynamic model (22). This is facilitated by a coefficient modification block (40). Thereafter, the difference between the new value input to the static model (20) and the prior steady-state value is utilized as an input to the dynamic model (22). The predicted dynamic output is then summed with the previous steady-state value to provide a predicted value Y. Additionally, the path that is traversed between steady-state value changes. ## Claims ### Claim 1 A method for predicting an output value from a received input value, the method comprising: on a physical computing device: modeling a set of static data received from a plant or process in a predictive static model over a first range, the static model having a static gain K and modeling static operation of the plant or process; modeling a set of dynamic data received from the plant or process in a predictive dynamic model over a second range smaller than the first range, the dynamic model having a dynamic gain and modeling dynamic operation of the plant or process over the second range, and the dynamic model being independent of operation of the static model; adjusting the dynamic gain of the dynamic model as a predetermined function of the static gain K of the static model to vary model parameters of the dynamic model; predicting the dynamic operation of the plant or process from an initial steady-state input value at a first time to a predicted steady-state input value at a second time to determine a predicted dynamic operation; comparing the predicted dynamic operation to a desired steady-state value of the plant or process at a final desired output value and generating an error as the difference therebetween; attenuating the error as a function of time between the first time and the second time; determining a change in the input value for input to said predicting the dynamic operation which is operable to vary the input value thereto; and varying the change in the input value in accordance with the determined change to minimize the attenuated error. ### Claim 2 (depends on 1) The method of claim 1, wherein said attenuating the error comprises: attenuating the error as a function of the values of the gain k at the first time and the second time. ### Claim 3 (depends on 2) The method of claim 2, wherein said attenuating the error as a function of the values of the gain k at the first time and the second time comprises: attenuating the error as a function of a ratio of the values of the gain k at the first time and the second time. ### Claim 4 (depends on 2) The method of claim 2, wherein said attenuating the error as a function of the values of the gain k at the first time and the second time comprises: attenuating the error according to a simple average of the values of the gain k at the first time and the second time. ### Claim 5 (depends on 2) The method of claim 2, wherein said attenuating the error as a function of the values of the gain k at the first time and the second time comprises: attenuating the error according to a larger absolute magnitude value of the values of the gain k at the first time and the second time. ### Claim 6 (depends on 2) The method of claim 2, wherein said attenuating the error as a function of the values of the gain k at the first time and the second time comprises: attenuating the error according to a weighted average of the values of the gain k at the first time and the second time. ### Claim 7 (depends on 2) The method of claim 2, wherein said attenuating the error as a function of the values of the gain k at the first time and the second time comprises: attenuating the error according to one or more values between the values of the gain k at the first time and the second time inclusively. ### Claim 8 (depends on 2) The method of claim 2, wherein said attenuating the error as a function of the values of the gain k at the first time and the second time comprises: attenuating the error according to a linear interpolation between the values of the gain k at the first time and the second time inclusively. ### Claim 9 (depends on 2) The method of claim 2, wherein said attenuating the error as a function of the values of the gain k at the first time and the second time comprises: attenuating the error according to a nonlinear interpolation between the values of the gain k at the first time and the second time inclusively. ### Claim 10 (depends on 1) The method of claim 1, further comprising: utilizing one or more of the static model and the dynamic model to control the plant or process. ### Claim 11 A computer-accessible memory medium for predicting an output value from a received input value, the memory medium comprising computer instructions executable by a processor to perform: modeling a set of static data received from a plant or process in a predictive static model over a first range, the static model having a static gain K and modeling static operation of the plant or process; modeling a set of dynamic data received from the plant or process in a predictive dynamic model over a second range smaller than the first range, the dynamic model having a dynamic gain k and modeling dynamic operation of the plant or process over the second range, and the dynamic model being independent of operation of the static model; adjusting the dynamic gain k of the dynamic model as a predetermined function of the static gain K of the static model to vary model parameters of the dynamic model; predicting the dynamic operation of the plant or process from an initial steady-state input value at a first time to a predicted steady-state input value at a second time to determine a predicted dynamic operation; comparing the predicted dynamic operation to a desired steady-state value of the plant or process at a final desired output value and generating an error as the difference therebetween; attenuating the error as a function of time between the first time and the second time; determining a change in the input value for input to said predicting the dynamic operation which is operable to vary the input value thereto; and varying the change in the input value in accordance with the determined change to minimize the attenuated error. ### Claim 12 (depends on 11) The memory medium of claim 11, wherein said attenuating the error comprises: attenuating the error as a function of the values of the gain k at the first time and the second time. ### Claim 13 (depends on 12) The memory medium of claim 12, wherein said attenuating the error as a function of the values of the gain k at the first time and the second time comprises: attenuating the error as a function of a ratio of the values of the gain k at the first time and the second time. ### Claim 14 (depends on 12) The memory medium of claim 12, wherein said attenuating the error as a function of the values of the gain k at the first time and the second time comprises: attenuating the error according to a simple average of the values of the gain k at the first time and the second time. ### Claim 15 (depends on 12) The memory medium of claim 12, wherein said attenuating the error as a function of the values of the gain k at the first time and the second time comprises: attenuating the error according to a larger absolute magnitude value of the values of the gain k at the first time and the second time. ### Claim 16 (depends on 12) The memory medium of claim 12, wherein said attenuating the error as a function of the values of the gain k at the first time and the second time comprises: attenuating the error according to a weighted average of the values of the gain k at the first time and the second time. ### Claim 17 (depends on 12) The memory medium of claim 12, wherein said attenuating the error as a function of the values of the gain k at the first time and the second time comprises: attenuating the error according to one or more values between the values of the gain k at the first time and the second time inclusively. ### Claim 18 (depends on 12) The memory medium of claim 12, wherein said attenuating the error as a function of the values of the gain k at the first time and the second time comprises: attenuating the error according to a linear interpolation between the values of the gain k at the first time and the second time inclusively. ### Claim 19 A system for predicting an output value from a received input value, comprising: a processor; a predictive static model implemented by the processor, wherein the predictive static models has a static gain K and models static operation of the plant or process, and wherein the predictive static model comprises a set of static data over a first range received from a plant or process; a dynamic model coupled to the predictive static model and implemented by the processor, wherein the dynamic model has a dynamic gain k and models dynamic operation of the plant or process, and wherein the dynamic model comprises a set of dynamic data over a second range smaller than the first range received from the plant or process; wherein the dynamic model is operable to: adjust the dynamic gain k as a predetermined function of the static gain K of the static model to vary model parameters of the dynamic model; predict the dynamic operation of the plant or process from an initial steady-state input value at a first time to a predicted steady-state input value at a second time to determine a predicted dynamic operation; compare the predicted dynamic operation to a desired steady-state value of the plant or process at a final desired output value and generate an error as the difference therebetween; attenuate the error as a function of time between the first time and the second time; determine a change in the input value for input to the dynamic model which is operable to vary the input value thereto; and vary the change in the input value in accordance with the determined change to minimize the attenuated error. ### Claim 20 A system, comprising: means for modeling a set of static data received from a plant or process in a predictive static model over a first range, the static model having a static gain K and modeling static operation of the plant or process; means for modeling a set of dynamic data received from the plant or process in a predictive dynamic model over a second range smaller than the first range, the dynamic model having a dynamic gain k and modeling dynamic operation of the plant or process over the second range, and the dynamic model being independent of operation of the static model; means for adjusting the dynamic gain k of the dynamic model as a predetermined function of the static gain K of the static model to vary model parameters of the dynamic model; means for predicting the dynamic operation of the plant or process from an initial steady-state input value at a first time to a predicted steady-state input value at a second time to determine a predicted dynamic operation; means for comparing the predicted dynamic operation to a desired steady-state value of the plant or process at a final desired output value and generating an error as the difference therebetween; means for attenuating the error as a function of time between the first time and the second time; means for determining a change in the input value for input to said predicting the dynamic operation which is operable to vary the input value thereto; and means for varying the change in the input value in accordance with the determined change to minimize the attenuated error.