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    • 6. 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。
    • 7. 发明授权
    • Multiple-input/multiple-output control blocks with non-linear predictive capabilities
    • 具有非线性预测能力的多输入/多输出控制块
    • US07272454B2
    • 2007-09-18
    • US10454937
    • 2003-06-05
    • Wilhelm K. WojsznisTerrence L. BlevinsAshish Mehta
    • Wilhelm K. WojsznisTerrence L. BlevinsAshish Mehta
    • G05B13/02
    • G05B13/027G05B13/048
    • A process controller that may be used to control a process having a set of process outputs effected by a set of process control input signals includes a multiple-input/multiple output controller that uses the process outputs to develop the set of process control input signals and a process model, which may be a non-linear process model, that receives the set of process control input signals to produce a prediction signal for one or more of the process outputs. The multiple-input/multiple-output control element includes another process model, which may be a standard linear process model, to develop a prediction vector for each of the process outputs and includes a correction unit that modifies the prediction vector for the one or more of the process outputs using the prediction signal for the one or more of the process outputs to thereby compensate for the non-linearities of the process.
    • 可以用于控制具有由一组过程控制输入信号影响的一组过程输出的过程的过程控制器包括多输入/多输出控制器,其使用过程输出来开发一组过程控制输入信号,以及 可以是非线性过程模型的过程模型,其接收一组过程控制输入信号以产生一个或多个过程输出的预测信号。 多输入/多输出控制元件包括可以是标准线性过程模型的另一过程模型,用于为每个过程输出开发预测向量,并且包括校正单元,其修改一个或多个 的过程输出使用用于一个或多个过程输出的预测信号,从而补偿过程的非线性。
    • 10. 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。