公开(公告)号：US08036760B2

公开(公告)日：2011-10-11

申请号：US12238801

申请日：2008-09-26

Applicant: Ashish Mehta ,  Peter Wojsznis ,  Marty J. Lewis ,  Larry O. Jundt ,  Nathan W. Pettus

Inventor： Ashish Mehta ,  Peter Wojsznis ,  Marty J. Lewis ,  Larry O. Jundt ,  Nathan W. Pettus

IPC: G05B13/02

CPC classification number: G05B13/048 ,  G05B17/02 ,  G05B23/0243 ,  G05B23/0272

Abstract: A controller includes a control module to control operation of a process in response to control data, a plug-in module coupled to the control module as a non-layered, integrated extension thereof, and a model identification engine. The plug-in detects a change in the control data, and a collects the control data and data in connection with a condition of the process in response to the detected change. The model identification engine executes a plurality of model parameter identification cycles. Each cycle includes simulations of the process each having different simulation parameter values and each using the control data as an input, an estimation error calculation for each simulation based on an output of the simulation and based on the operating condition data, and a calculation of a model parameter value based on the estimation errors and simulation parameter values used in the simulation corresponding to each of the estimation errors.

Abstract translation: 控制器包括：控制模块，用于响应于控制数据控制过程的操作，作为其控制模块耦合到控制模块的插件模块，作为其非分层的集成扩展，以及模型识别引擎。 插件检测控制数据的变化，并且响应于检测到的改变而与控制数据和数据结合处理的条件。 模型识别引擎执行多个模型参数识别周期。 每个循环包括具有不同模拟参数值并且每个使用控制数据作为输入的每个模拟过程的模拟，基于模拟输出并基于操作条件数据的每个模拟的估计误差计算，以及计算 基于与每个估计误差对应的仿真中使用的估计误差和模拟参数值的模型参数值。

公开(公告)号：US20090112335A1

公开(公告)日：2009-04-30

申请号：US12238801

申请日：2008-09-26

Applicant: Ashish MEHTA ,  Peter Wojsznis ,  Marty J. Lewis ,  Larry O. Jundt ,  Nathan W. Pettus

Inventor： Ashish MEHTA ,  Peter Wojsznis ,  Marty J. Lewis ,  Larry O. Jundt ,  Nathan W. Pettus

IPC: G05B13/04 ,  G06G7/66

CPC classification number: G05B13/048 ,  G05B17/02 ,  G05B23/0243 ,  G05B23/0272

Abstract: A controller includes a control module to control operation of a process in response to control data, a plug-in module coupled to the control module as a non-layered, integrated extension thereof, and a model identification engine. The plug-in detects a change in the control data, and a collects the control data and data in connection with a condition of the process in response to the detected change. The model identification engine executes a plurality of model parameter identification cycles. Each cycle includes simulations of the process each having different simulation parameter values and each using the control data as an input, an estimation error calculation for each simulation based on an output of the simulation and based on the operating condition data, and a calculation of a model parameter value based on the estimation errors and simulation parameter values used in the simulation corresponding to each of the estimation errors.

Abstract translation: 控制器包括：控制模块，用于响应于控制数据控制过程的操作，作为其控制模块耦合到控制模块的插件模块作为其非分层的集成扩展，以及模型识别引擎。 插件检测控制数据的变化，并且响应于检测到的改变而与控制数据和数据结合处理的条件。 模型识别引擎执行多个模型参数识别周期。 每个循环包括具有不同模拟参数值并且每个使用控制数据作为输入的每个模拟过程的模拟，基于模拟输出并基于操作条件数据的每个模拟的估计误差计算，以及计算 基于与每个估计误差对应的仿真中使用的估计误差和模拟参数值的模型参数值。

公开(公告)号：US20070244575A1

公开(公告)日：2007-10-18

申请号：US11403361

申请日：2006-04-13

Applicant: Wilhelm Wojsznis ,  Ashish Mehta ,  Dirk Thiele

Inventor： Wilhelm Wojsznis ,  Ashish Mehta ,  Dirk Thiele

IPC: G05B13/02

CPC classification number: G05B13/048 ,  G05B17/02

Abstract: A robust method of creating process models for use in controller generation, such as in MPC controller generation, adds noise to the process data collected and used in the model generation process. In particular, a robust method of creating a parametric process model first collects process outputs based on known test input signals or sequences, adds random noise to the collected process data and then uses a standard or known technique to determine a process model from the collected process data. Unlike existing techniques for noise removal that focus on clean up of non-random noise prior to generating a process model, the addition of random, zero-mean noise to the process data enables, in many cases, the generation of an acceptable parametric process model in situations where no process model parameter convergence was otherwise obtained. Additionally, process models created using this technique generally have wider confidence intervals, therefore providing a model that works adequately in many process situations without needing to manually or graphically change the model.

Abstract translation: 创建用于控制器生成过程模型（例如MPC控制器生成）的可靠方法为模型生成过程中收集和使用的过程数据增加了噪音。 特别地，创建参数过程模型的可靠方法首先基于已知的测试输入信号或序列收集过程输出，将随机噪声添加到收集的过程数据，然后使用标准或已知技术从收集的过程中确定过程模型 数据。 与在生成过程模型之前关注清除非随机噪声的噪声去除技术不同，在过程数据中添加随机的零均值噪声能够在许多情况下产生可接受的参数过程模型 在没有获得过程模型参数收敛的情况下。 此外，使用此技术创建的过程模型通常具有更宽的置信区间，因此提供了一个可在许多过程情况下正常工作的模型，无需手动或图形地更改模型。

公开(公告)号：US08208403B2

公开(公告)日：2012-06-26

申请号：US11964111

申请日：2007-12-26

Applicant: Ashis Sarkar ,  Dhaval V. Thaker ,  Ashish Mehta

Inventor： Ashis Sarkar ,  Dhaval V. Thaker ,  Ashish Mehta

IPC: H04L12/28

CPC classification number: H04L45/00 ,  H04L41/5054 ,  H04L41/5064 ,  H04L45/30 ,  H04L45/42 ,  H04M3/42348

Abstract: A device receives, from a customer, a request for an end-to-end path through a network, determines parameters of a query based on the request and path criteria, and executes the query on a database of network elements capable of being included in the end-to-end path. The device also selects one or more of the network elements provided in the database based on results of the query, and reserves, in the database, the one or more selected network elements for the end-to-end path.

Abstract translation: 设备从客户接收通过网络的端到端路径的请求，基于请求和路径标准来确定查询的参数，并且在能够被包括在网络中的网元的数据库上执行查询 端到端路径。 该设备还基于查询的结果来选择在数据库中提供的一个或多个网络元素，并且在数据库中保留用于端对端路径的一个或多个所选择的网络元件。

公开(公告)号：US08046096B2

公开(公告)日：2011-10-25

申请号：US12782946

申请日：2010-05-19

Applicant: David R. Denison ,  Marty James Lewis ,  Peter Wojsznis ,  Ashish Mehta

Inventor： David R. Denison ,  Marty James Lewis ,  Peter Wojsznis ,  Ashish Mehta

IPC: G06F19/00

CPC classification number: G05B13/042 ,  G05B13/0275 ,  G05B13/048 ,  G05B19/4185 ,  G05B2219/31121 ,  G05B2219/31378 ,  G05B2219/31422 ,  Y02P90/18 ,  Y02P90/86

Abstract: A process control system integrates the collection and analysis of process control data used to perform certain computationally expensive process control functions, like adaptive model generation and tuning parameter generation, in the same control device in which one or more of the process control routines are implemented, to thereby provide for faster and more efficient support of the process control routines. This system replaces a layered approach using multiple processing devices by integrating an analytical server which performs computationally expensive analyses used by one or more control routines directly into the real-time control device in which the one or more control routines are located. This integration provides the ability to analyze large quantities of data for multiple process loops controlled by a particular device in a fast and efficient manner.

Abstract translation: 过程控制系统将用于执行某些计算昂贵的过程控制功能（例如自适应模型生成和调整参数生成）的过程控制数据的收集和分析集成在其中实现一个或多个过程控制例程的相同控制设备中， 从而为过程控制程序提供更快更有效的支持。 该系统通过将分析服务器集成在一个或多个控制例程所在的实时控制设备中，将分析服务器集成到一个或多个控制例程所使用的计算上昂贵的分析中来代替使用多个处理设备的分层方法。 该集成提供了以快速和有效的方式分析由特定设备控制的多个过程循环的大量数据的能力。

公开(公告)号：US07840287B2

公开(公告)日：2010-11-23

申请号：US11403361

申请日：2006-04-13

Applicant: Wilhelm K. Wojsznis ,  Ashish Mehta ,  Dirk Thiele

Inventor： Wilhelm K. Wojsznis ,  Ashish Mehta ,  Dirk Thiele

IPC: G05B13/02 ,  G05B11/01 ,  G06F19/00 ,  G06F11/30 ,  G06F7/60 ,  G06F17/10 ,  G21C17/00 ,  H03F1/26 ,  H04B15/00

CPC classification number: G05B13/048 ,  G05B17/02

Abstract: A robust method of creating process models for use in controller generation, such as in MPC controller generation, adds noise to the process data collected and used in the model generation process. In particular, a robust method of creating a parametric process model first collects process outputs based on known test input signals or sequences, adds random noise to the collected process data and then uses a standard or known technique to determine a process model from the collected process data. Unlike existing techniques for noise removal that focus on clean up of non-random noise prior to generating a process model, the addition of random, zero-mean noise to the process data enables, in many cases, the generation of an acceptable parametric process model in situations where no process model parameter convergence was otherwise obtained. Additionally, process models created using this technique generally have wider confidence intervals, therefore providing a model that works adequately in many process situations without needing to manually or graphically change the model.

Abstract translation: 创建用于控制器生成过程模型（例如MPC控制器生成）的可靠方法为模型生成过程中收集和使用的过程数据增加了噪音。 特别地，创建参数过程模型的可靠方法首先基于已知的测试输入信号或序列收集过程输出，将随机噪声添加到收集的过程数据，然后使用标准或已知技术从收集的过程中确定过程模型 数据。 与在生成过程模型之前关注清除非随机噪声的噪声去除技术不同，在过程数据中添加随机的零均值噪声能够在许多情况下产生可接受的参数过程模型 在没有获得过程模型参数收敛的情况下。 此外，使用此技术创建的过程模型通常具有更宽的置信区间，因此提供了一个可在许多过程情况下正常工作的模型，无需手动或图形地更改模型。

公开(公告)号：US07242989B2

公开(公告)日：2007-07-10

申请号：US10449437

申请日：2003-05-30

Applicant: Terrence L. Blevins ,  Ashish Mehta

Inventor： Terrence L. Blevins ,  Ashish Mehta

IPC: G05B13/02 ,  G06F7/60 ,  G06F17/10

CPC classification number: G05B13/027 ,  G05B17/02

Abstract: A method and apparatus that generates an estimate of a property of a batch process uses a non-parametric model to generate a plurality of rate of reaction estimates associated with the batch process. Each rate of reaction estimate may correspond, for example, to a particular time during the batch process. The plurality of rate of reaction estimates are then integrated to generate an estimate of a property of the batch at the particular time.

Abstract translation: 产生批处理过程的属性估计的方法和装置使用非参数模型来产生与批处理相关联的多个反应估计率。 每个反应估计速率可以例如对应于批处理期间的特定时间。 然后将多个反应率估计值进行积分以产生在特定时间的批次的性质的估计。

公开(公告)号：US08065251B2

公开(公告)日：2011-11-22

申请号：US12238773

申请日：2008-09-26

Applicant: Ashish Mehta ,  John M. Caldwell

Inventor： Ashish Mehta ,  John M. Caldwell

IPC: G06F17/00

CPC classification number: G05B17/02 ,  G05B13/048

Abstract: A method of managing a process model history having process models stored therein, includes organizing the process models according to first and second priority criteria, wherein each process model is represented according to a combination of a value in connection with the first and second priority criteria. The representation may be coordinate values in a multi-dimensional space having dimensions corresponding to the first and second priority criteria. A degree of separation or relationship to a common point of reference is calculated for each process model, where the point of reference is a value in connection with the first and second priority criteria. A process model may be removed or selected for deletion based on the degree of separation or proximity in relation to the point of reference, subject to the total number of process models identified for the same control routine, and the total number of process models identified for the same operational region.

Abstract translation: 一种管理具有存储在其中的过程模型的过程模型历史的方法包括根据第一和第二优先级标准组织过程模型，其中根据与第一和第二优先级标准相结合的值的组合来表示每个过程模型。 该表示可以是具有对应于第一和第二优先级标准的尺寸的多维空间中的坐标值。 对于每个过程模型计算与共同参考点的分离程度或关系，其中参考点是与第一和第二优先级标准相关的值。 可以基于相对于参考点的分离程度或接近程度来删除或选择过程模型以进行删除，除了为同一控制程序识别的过程模型的总数以及为 相同的作业区域。

公开(公告)号：US20100228363A1

公开(公告)日：2010-09-09

申请号：US12782946

申请日：2010-05-19

Applicant: David R. Denison ,  Marty J. Lewis ,  Peter Wojsznis ,  Ashish Mehta

Inventor： David R. Denison ,  Marty J. Lewis ,  Peter Wojsznis ,  Ashish Mehta

IPC: G05B19/04

CPC classification number: G05B13/042 ,  G05B13/0275 ,  G05B13/048 ,  G05B19/4185 ,  G05B2219/31121 ,  G05B2219/31378 ,  G05B2219/31422 ,  Y02P90/18 ,  Y02P90/86

Abstract: A process control system integrates the collection and analysis of process control data used to perform certain computationally expensive process control functions, like adaptive model generation and tuning parameter generation, in the same control device in which one or more of the process control routines are implemented, to thereby provide for faster and more efficient support of the process control routines. This system replaces a layered approach using multiple processing devices by integrating an analytical server which performs computationally expensive analyses used by one or more control routines directly into the real-time control device in which the one or more control routines are located. This integration provides the ability to analyze large quantities of data for multiple process loops controlled by a particular device in a fast and efficient manner.

Abstract translation: 过程控制系统将用于执行某些计算昂贵的过程控制功能（例如自适应模型生成和调整参数生成）的过程控制数据的收集和分析集成在其中实现一个或多个过程控制例程的相同控制设备中， 从而为过程控制程序提供更快更有效的支持。 该系统通过将分析服务器集成在一个或多个控制例程所在的实时控制设备中，将分析服务器集成到一个或多个控制例程所使用的计算上昂贵的分析中来代替使用多个处理设备的分层方法。 该集成提供了以快速和有效的方式分析由特定设备控制的多个过程循环的大量数据的能力。

公开(公告)号：US20090105855A1

公开(公告)日：2009-04-23

申请号：US12238773

申请日：2008-09-26

Applicant: Ashish MEHTA ,  John M. Caldwell

Inventor： Ashish MEHTA ,  John M. Caldwell

IPC: G05B19/02 ,  G06F17/30 ,  G06F9/46

CPC classification number: G05B17/02 ,  G05B13/048

Abstract: A method of managing a process model history having process models stored therein, includes organizing the process models according to first and second priority criteria, wherein each process model is represented according to a combination of a value in connection with the first and second priority criteria. The representation may be coordinate values in a multi-dimensional space having dimensions corresponding to the first and second priority criteria. A degree of separation or relationship to a common point of reference is calculated for each process model, where the point of reference is a value in connection with the first and second priority criteria. A process model may be removed or selected for deletion based on the degree of separation or proximity in relation to the point of reference, subject to the total number of process models identified for the same control routine, and the total number of process models identified for the same operational region.

Abstract translation: 一种管理具有存储在其中的过程模型的过程模型历史的方法包括根据第一和第二优先级标准组织过程模型，其中根据与第一和第二优先级标准相结合的值的组合来表示每个过程模型。 该表示可以是具有对应于第一和第二优先级标准的尺寸的多维空间中的坐标值。 对于每个过程模型计算与共同参考点的分离程度或关系，其中参考点是与第一和第二优先级标准相关的值。 可以基于相对于参考点的分离程度或接近程度来删除或选择过程模型以进行删除，除了为同一控制程序识别的过程模型的总数以及为 相同的作业区域。