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1.发明授权Controlling a non-linear process with varying dynamics using non-linear model predictive control 有权使用非线性模型预测控制控制具有变化动力学的非线性过程公开(公告)号：US07599749B2
公开(公告)日：2009-10-06
申请号：US11678634
申请日：2007-02-26
申请人： Bijan Sayyarrodsari , Eric Hartman , Celso Axelrud , Kadir Liano
发明人： Bijan Sayyarrodsari , Eric Hartman , Celso Axelrud , Kadir Liano
IPC分类号： G05B13/02
CPC分类号： G05B13/042
摘要： The present invention provides a method for controlling nonlinear control problems within particle accelerators. This method involves first utilizing software tools to identify variable inputs and controlled variables associated with the particle accelerator, wherein at least one variable input parameter is a controlled variable. This software tool is further operable to determine relationships between the variable inputs and controlled variables. A control system that provides variable inputs to and acts on controller outputs from the software tools tunes one or more manipulated variables to achieve a desired controlled variable, which in the case of a particle accelerator may be realized as a more efficient collision.
摘要翻译： 本发明提供了一种用于控制粒子加速器内的非线性控制问题的方法。 该方法首先利用软件工具来识别与粒子加速器相关联的变量输入和控制变量，其中至少一个可变输入参数是受控变量。 该软件工具进一步可操作以确定可变输入和受控变量之间的关系。 向软件工具向控制器输出提供可变输入并作用于控制器输出的控制系统调整一个或多个操纵变量以实现期望的受控变量，其在粒子加速器的情况下可被实现为更有效的冲突。
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2.发明申请CONTROLLING A NON-LINEAR PROCESS WITH VARYING DYNAMICS USING NON-LINEAR MODEL PREDICTIVE CONTROL 有权使用非线性模型预测控制来控制具有变化动力学的非线性过程公开(公告)号：US20070198104A1
公开(公告)日：2007-08-23
申请号：US11678634
申请日：2007-02-26
申请人： Bijan Sayyarrodsari , Eric Hartman , Celso Axelrud , Kadir Liano
发明人： Bijan Sayyarrodsari , Eric Hartman , Celso Axelrud , Kadir Liano
IPC分类号： G05B13/02
CPC分类号： G05B13/042
摘要： The present invention provides a method for controlling nonlinear control problems within particle accelerators. This method involves first utilizing software tools to identify variable inputs and controlled variables associated with the particle accelerator, wherein at least one variable input parameter is a controlled variable. This software tool is further operable to determine relationships between the variable inputs and controlled variables. A control system that provides variable inputs to and acts on controller outputs from the software tools tunes one or more manipulated variables to achieve a desired controlled variable, which in the case of a particle accelerator may be realized as a more efficient collision.
摘要翻译： 本发明提供了一种用于控制粒子加速器内的非线性控制问题的方法。 该方法首先利用软件工具来识别与粒子加速器相关联的变量输入和控制变量，其中至少一个可变输入参数是受控变量。 该软件工具进一步可操作以确定可变输入和受控变量之间的关系。 向软件工具向控制器输出提供可变输入并作用于控制器输出的控制系统调整一个或多个操纵变量以实现期望的受控变量，其在粒子加速器的情况下可被实现为更有效的冲突。
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3.发明授权System and method of applying adaptive control to the control of particle accelerators with varying dynamics behavioral characteristics using a nonlinear model predictive control technology 有权使用非线性模型预测控制技术对具有不同动力学行为特征的粒子加速器的控制应用自适应控制的系统和方法公开(公告)号：US07184845B2
公开(公告)日：2007-02-27
申请号：US10731596
申请日：2003-12-09
申请人： Bijan Sayyarrodsari , Eric Hartman , Celso Axelrud , Kadir Liano
发明人： Bijan Sayyarrodsari , Eric Hartman , Celso Axelrud , Kadir Liano
IPC分类号： G05B13/02
CPC分类号： G05B13/042
摘要： The present invention provides a method for controlling nonlinear control problems within particle accelerators. This method involves first utilizing software tools to identify variable inputs and controlled variables associated with the particle accelerator, wherein at least one variable input parameter is a controlled variable. This software tool is further operable to determine relationships between the variable inputs and controlled variables. A control system that provides variable inputs to and acts on controller outputs from the software tools tunes one or more manipulated variables to achieve a desired controlled variable, which in the case of a particle accelerator may be realized as a more efficient collision.
摘要翻译： 本发明提供了一种用于控制粒子加速器内的非线性控制问题的方法。 该方法首先利用软件工具来识别与粒子加速器相关联的变量输入和控制变量，其中至少一个可变输入参数是受控变量。 该软件工具进一步可操作以确定可变输入和受控变量之间的关系。 向软件工具向控制器输出提供可变输入并作用于控制器输出的控制系统调整一个或多个操纵变量以实现期望的受控变量，其在粒子加速器的情况下可被实现为更有效的冲突。
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4.发明授权公开(公告)号：US07039475B2
公开(公告)日：2006-05-02
申请号：US10730835
申请日：2003-12-09
申请人： Bijan Sayyarrodsari , Eric Hartman , Celso Axelrud , Kidir Liano
发明人： Bijan Sayyarrodsari , Eric Hartman , Celso Axelrud , Kidir Liano
IPC分类号： G05B13/02
CPC分类号： G05B13/027 , G05B13/048 , G05B17/02
摘要： The present invention provides a method for controlling nonlinear process control problems. This method involves first utilizing modeling tools to identify variable inputs and controlled variables associated with the process, wherein at least one variable input is a manipulated variable. The modeling tools are further operable to determine relationships between the variable inputs and controlled variables. A control system that provides inputs to and acts on inputs from the modeling tools tunes one or more manipulated variable inputs to achieve a desired result like greater efficiency, higher quality, or greater consistency.
摘要翻译： 本发明提供一种控制非线性过程控制问题的方法。 该方法包括首先利用建模工具来识别与该过程相关联的变量输入和控制变量，其中至少一个可变输入是操纵变量。 建模工具还可操作以确定可变输入和受控变量之间的关系。 为建模工具提供输入和输入的操作的控制系统调整一个或多个操作变量输入，以获得更高效率，更高质量或更高一致性的期望结果。
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5.发明授权公开(公告)号：US08452719B2
公开(公告)日：2013-05-28
申请号：US12825706
申请日：2010-06-29
CPC分类号： G05B11/32 , G05B13/042 , G05B17/02
摘要： The present disclosure provides novel techniques for defining empirical models having control, prediction, and optimization modalities. The empirical models may include neural networks and support vector machines. The empirical models may include asymptotic analysis as part of the model definition as allow the models to achieve enhanced results, including enhanced high-order behaviors. The high-order behaviors may exhibit gains that are non-zero trending, which may be useful for controller modalities.
摘要翻译： 本公开提供了用于定义具有控制，预测和优化模态的经验模型的新技术。 经验模型可以包括神经网络和支持向量机。 经验模型可以包括作为模型定义的一部分的渐近分析，允许模型实现增强的结果，包括增强的高阶行为。 高阶行为可能表现出非零趋势的收益，这可能对控制器模式有用。
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6.发明申请EXTRAPOLATING EMPIRICAL MODELS FOR CONTROL, PREDICTION, AND OPTIMIZATION APPLICATIONS 有权用于控制，预测和优化应用的提取实验模型公开(公告)号：US20110320386A1
公开(公告)日：2011-12-29
申请号：US12825706
申请日：2010-06-29
CPC分类号： G05B11/32 , G05B13/042 , G05B17/02
摘要： The present disclosure provides novel techniques for defining empirical models having control, prediction, and optimization modalities. The empirical models may include neural networks and support vector machines. The empirical models may include asymptotic analysis as part of the model definition as allow the models to achieve enhanced results, including enhanced high-order behaviors. The high-order behaviors may exhibit gains that are non-zero trending, which may be useful for controller modalities.
摘要翻译： 本公开提供了用于定义具有控制，预测和优化模态的经验模型的新技术。 经验模型可以包括神经网络和支持向量机。 经验模型可以包括作为模型定义的一部分的渐近分析，允许模型实现增强的结果，包括增强的高阶行为。 高阶行为可能表现出非零趋势的收益，这可能对控制器模式有用。
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公开(公告)号：US20070282766A1
公开(公告)日：2007-12-06
申请号：US11418971
申请日：2006-05-05
申请人： Eric Hartman , Carl Schweiger , Bijan Sayyarrodsari , W. Johnson
发明人： Eric Hartman , Carl Schweiger , Bijan Sayyarrodsari , W. Johnson
IPC分类号： G06N3/02
CPC分类号： G06N99/005 , G05B13/048 , G06K9/6269
摘要： System and method for training a support vector machine (SVM) with process constraints. A model (primal or dual formulation) implemented with an SVM and representing a plant or process with one or more known attributes is provided. One or more process constraints that correspond to the one or more known attributes are specified, and the model trained subject to the one or more process constraints. The model includes one or more inputs and one or more outputs, as well as one or more gains, each a respective partial derivative of an output with respect to a respective input. The process constraints may include any of: one or more gain constraints, each corresponding to a respective gain; one or more Nth order gain constraints; one or more input constraints; and/or one or more output constraints. The trained model may then be used to control or manage the plant or process.
摘要翻译： 用于训练具有过程约束的支持向量机（SVM）的系统和方法。 提供了使用SVM实现并且表示具有一个或多个已知属性的工厂或过程的模型（原始或双重配方）。 指定对应于一个或多个已知属性的一个或多个过程约束，并且该模型受制于一个或多个过程约束。 该模型包括一个或多个输入和一个或多个输出，以及一个或多个增益，每个增益相对于相应输入的输出的相应偏导数。 过程约束可以包括以下任何一个：一个或多个增益约束，每个对应于相应的增益; 一个或多个第N阶增益约束; 一个或多个输入约束; 和/或一个或多个输出约束。 然后可以使用经过训练的模型来控制或管理植物或过程。
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公开(公告)号：US07599897B2
公开(公告)日：2009-10-06
申请号：US11418971
申请日：2006-05-05
IPC分类号： G06N5/00
CPC分类号： G06N99/005 , G05B13/048 , G06K9/6269
摘要： System and method for training a support vector machine (SVM) with process constraints. A model (primal or dual formulation) implemented with an SVM and representing a plant or process with one or more known attributes is provided. One or more process constraints that correspond to the one or more known attributes are specified, and the model trained subject to the one or more process constraints. The model includes one or more inputs and one or more outputs, as well as one or more gains, each a respective partial derivative of an output with respect to a respective input. The process constraints may include any of: one or more gain constraints, each corresponding to a respective gain; one or more Nth order gain constraints; one or more input constraints; and/or one or more output constraints. The trained model may then be used to control or manage the plant or process.
摘要翻译： 用于训练具有过程约束的支持向量机（SVM）的系统和方法。 提供了使用SVM实现并且表示具有一个或多个已知属性的工厂或过程的模型（原始或双重配方）。 指定对应于一个或多个已知属性的一个或多个过程约束，并且该模型受制于一个或多个过程约束。 该模型包括一个或多个输入和一个或多个输出，以及一个或多个增益，每个增益相对于相应输入的输出的相应偏导数。 过程约束可以包括以下任何一个：一个或多个增益约束，每个对应于相应的增益; 一个或多个第N阶增益约束; 一个或多个输入约束; 和/或一个或多个输出约束。 然后可以使用经过训练的模型来控制或管理植物或过程。
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