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    • 1. 发明授权
    • Method and apparatus for intelligent control and monitoring in a process control system
    • 在过程控制系统中智能控制和监控的方法和装置
    • US08036760B2
    • 2011-10-11
    • US12238801
    • 2008-09-26
    • Ashish MehtaPeter WojsznisMarty J. LewisLarry O. JundtNathan W. Pettus
    • Ashish MehtaPeter WojsznisMarty J. LewisLarry O. JundtNathan W. Pettus
    • G05B13/02
    • G05B13/048G05B17/02G05B23/0243G05B23/0272
    • 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.
    • 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。
    • 2. 发明申请
    • METHOD AND APPARATUS FOR INTELLIGENT CONTROL AND MONITORING IN A PROCESS CONTROL SYSTEM
    • 用于智能控制和监控过程控制系统的方法和装置
    • US20090112335A1
    • 2009-04-30
    • US12238801
    • 2008-09-26
    • Ashish MEHTAPeter WojsznisMarty J. LewisLarry O. JundtNathan W. Pettus
    • Ashish MEHTAPeter WojsznisMarty J. LewisLarry O. JundtNathan W. Pettus
    • G05B13/04G06G7/66
    • G05B13/048G05B17/02G05B23/0243G05B23/0272
    • 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.
    • 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。
    • 3. 发明申请
    • Robust process model identification in model based control techniques
    • 基于模型的控制技术的鲁棒过程模型识别
    • US20070244575A1
    • 2007-10-18
    • US11403361
    • 2006-04-13
    • Wilhelm WojsznisAshish MehtaDirk Thiele
    • Wilhelm WojsznisAshish MehtaDirk Thiele
    • G05B13/02
    • G05B13/048G05B17/02
    • 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.
    • 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。
    • 6. 发明授权
    • Robust process model identification in model based control techniques
    • 基于模型的控制技术的鲁棒过程模型识别
    • US07840287B2
    • 2010-11-23
    • US11403361
    • 2006-04-13
    • Wilhelm K. WojsznisAshish MehtaDirk Thiele
    • Wilhelm K. WojsznisAshish MehtaDirk Thiele
    • G05B13/02G05B11/01G06F19/00G06F11/30G06F7/60G06F17/10G21C17/00H03F1/26H04B15/00
    • G05B13/048G05B17/02
    • 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.
    • 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。
    • 9. 发明申请
    • Generating a Savings Plan
    • 生成储蓄计划
    • US20090192947A1
    • 2009-07-30
    • US12019201
    • 2008-01-24
    • Matthew B. KenigsbergGlenn LarsenAshish Mehta
    • Matthew B. KenigsbergGlenn LarsenAshish Mehta
    • G06Q40/00
    • G06Q40/06
    • A user utilizing a user interface inputs debt information, income information and savings information. The user also inputs retirement information which includes aspects of the user's retirement plans (e.g., age of retirement, current age, etc.). The debt information, the income information, and the savings information are assigned to categories. The categories are prioritized to generate a savings plan that includes a savings prioritization (e.g., save into these types of accounts in this order) and/or a spending prioritization (e.g., spend from these types of accounts in this order). The savings plan and/or a quantified benefit of its use is displayed to the user.
    • 利用用户界面的用户输入债务信息,收入信息和储蓄信息。 用户还输入包括用户退休计划(例如,退休年龄,当前年龄等)的方面的退休信息。 债务信息,收入信息和储蓄信息分配给类别。 优先考虑这些类别以产生储蓄计划,其中包括节省优先次序(例如,以此顺序保存在这些类型的帐户中)和/或支出优先级(例如,按照这种顺序从这些类型的帐户支出)。 向用户显示储蓄计划和/或其使用的量化好处。