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公开(公告)号：US08019701B2
公开(公告)日：2011-09-13
申请号：US12112750
申请日：2008-04-30
IPC分类号： G06N5/00
CPC分类号： G05B13/048 , G05B13/042 , G05B17/02
摘要： System and method for modeling a nonlinear process. A combined model for predictive optimization or control of a nonlinear process includes a nonlinear approximator, coupled to a parameterized dynamic or static model, operable to model the nonlinear process. The nonlinear approximator receives process inputs, and generates parameters for the parameterized dynamic model. The parameterized dynamic model receives the parameters and process inputs, and generates predicted process outputs based on the parameters and process inputs, where the predicted process outputs are useable to analyze and/or control the nonlinear process. The combined model may be trained in an integrated manner, e.g., substantially concurrently, by identifying process inputs and outputs (I/O), collecting data for process I/O, determining constraints on model behavior from prior knowledge, formulating an optimization problem, executing an optimization algorithm to determine model parameters subject to the determined constraints, and verifying the compliance of the model with the constraints.
摘要翻译： 用于建模非线性过程的系统和方法。 用于非线性过程的预测优化或控制的组合模型包括耦合到参数化动态或静态模型的非线性近似器，可操作以对非线性过程建模。 非线性近似器接收过程输入，并为参数化动态模型生成参数。 参数化动态模型接收参数和过程输入，并根据参数和过程输入生成预测过程输出，其中预测过程输出可用于分析和/或控制非线性过程。 组合模型可以通过识别过程输入和输出（I / O），收集过程I / O的数据，确定来自先验知识的模型行为的约束，制定优化问题，以基本上同时的方式进行训练， 执行优化算法以确定受限于确定的模型参数，并验证模型与约束的一致性。
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公开(公告)号：US20080235166A1
公开(公告)日：2008-09-25
申请号：US12112750
申请日：2008-04-30
IPC分类号： G06F15/18
CPC分类号： G05B13/048 , G05B13/042 , G05B17/02
摘要： System and method for modeling a nonlinear process. A combined model for predictive optimization or control of a nonlinear process includes a nonlinear approximator, coupled to a parameterized dynamic or static model, operable to model the nonlinear process. The nonlinear approximator receives process inputs, and generates parameters for the parameterized dynamic model. The parameterized dynamic model receives the parameters and process inputs, and generates predicted process outputs based on the parameters and process inputs, where the predicted process outputs are useable to analyze and/or control the nonlinear process. The combined model may be trained in an integrated manner, e.g., substantially concurrently, by identifying process inputs and outputs (I/O), collecting data for process I/O, determining constraints on model behavior from prior knowledge, formulating an optimization problem, executing an optimization algorithm to determine model parameters subject to the determined constraints, and verifying the compliance of the model with the constraints.
摘要翻译： 用于建模非线性过程的系统和方法。 用于非线性过程的预测优化或控制的组合模型包括耦合到参数化动态或静态模型的非线性近似器，可操作以对非线性过程建模。 非线性近似器接收过程输入，并为参数化动态模型生成参数。 参数化动态模型接收参数和过程输入，并根据参数和过程输入生成预测过程输出，其中预测过程输出可用于分析和/或控制非线性过程。 组合模型可以通过识别过程输入和输出（I / O），收集过程I / O的数据，确定来自先验知识的模型行为的约束，制定优化问题，以基本上同时的方式进行训练， 执行优化算法以确定受限于确定的模型参数，并验证模型与约束的一致性。
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公开(公告)号：US20050187643A1
公开(公告)日：2005-08-25
申请号：US10842157
申请日：2004-05-10
CPC分类号： G05B13/048 , G05B13/042 , G05B17/02
摘要： System and method for modeling a nonlinear process. A combined model for predictive optimization or control of a nonlinear process includes a nonlinear approximator, coupled to a parameterized dynamic or static model, operable to model the nonlinear process. The nonlinear approximator receives process inputs, and generates parameters for the parameterized dynamic model. The parameterized dynamic model receives the parameters and process inputs, and generates predicted process outputs based on the parameters and process inputs, where the predicted process outputs are useable to analyze and/or control the nonlinear process. The combined model may be trained in an integrated manner, e.g., substantially concurrently, by identifying process inputs and outputs (I/O), collecting data for process I/O, determining constraints on model behavior from prior knowledge, formulating an optimization problem, executing an optimization algorithm to determine model parameters subject to the determined constraints, and verifying the compliance of the model with the constraints.
摘要翻译： 用于建模非线性过程的系统和方法。 用于非线性过程的预测优化或控制的组合模型包括耦合到参数化动态或静态模型的非线性近似器，可操作以对非线性过程建模。 非线性近似器接收过程输入，并为参数化动态模型生成参数。 参数化动态模型接收参数和过程输入，并根据参数和过程输入生成预测过程输出，其中预测过程输出可用于分析和/或控制非线性过程。 组合模型可以通过识别过程输入和输出（I / O），收集过程I / O的数据，确定来自先验知识的模型行为的约束，制定优化问题，以基本上同时的方式进行训练， 执行优化算法以确定受限于确定的模型参数，并验证模型与约束的一致性。
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公开(公告)号：US20080208778A1
公开(公告)日：2008-08-28
申请号：US12112847
申请日：2008-04-30
CPC分类号： G05B13/048 , G05B13/042 , G05B17/02
摘要： System and method for modeling a nonlinear process. A combined model for predictive optimization or control of a nonlinear process includes a nonlinear approximator, coupled to a parameterized dynamic or static model, operable to model the nonlinear process. The nonlinear approximator receives process inputs, and generates parameters for the parameterized dynamic model. The parameterized dynamic model receives the parameters and process inputs, and generates predicted process outputs based on the parameters and process inputs, where the predicted process outputs are useable to analyze and/or control the nonlinear process. The combined model may be trained in an integrated manner, e.g., substantially concurrently, by identifying process inputs and outputs (I/O), collecting data for process I/O, determining constraints on model behavior from prior knowledge, formulating an optimization problem, executing an optimization algorithm to determine model parameters subject to the determined constraints, and verifying the compliance of the model with the constraints.
摘要翻译： 用于建模非线性过程的系统和方法。 用于非线性过程的预测优化或控制的组合模型包括耦合到参数化动态或静态模型的非线性近似器，可操作以对非线性过程建模。 非线性近似器接收过程输入，并为参数化动态模型生成参数。 参数化动态模型接收参数和过程输入，并根据参数和过程输入生成预测过程输出，其中预测过程输出可用于分析和/或控制非线性过程。 组合模型可以通过识别过程输入和输出（I / O），收集过程I / O的数据，确定来自先验知识的模型行为的约束，制定优化问题，以基本上同时的方式进行训练， 执行优化算法以确定受限于确定的模型参数，并验证模型与约束的一致性。
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5.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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6.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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7.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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公开(公告)号：US6047221A
公开(公告)日：2000-04-04
申请号：US943489
申请日：1997-10-03
申请人： Stephen Piche , James David Keeler , Eric Hartman , William D. Johnson , Mark Gerules , Kadir Liano
发明人： Stephen Piche , James David Keeler , Eric Hartman , William D. Johnson , Mark Gerules , Kadir Liano
CPC分类号： G05B17/02 , G05B13/048
摘要： A method for modeling a steady-state network in the absence of steady-state historical data. A steady-state neural network can be tied by impressing the dynamics of the system onto the input data during the training operation by first determining the dynamics in a local region of the input space, this providing a set of dynamic training data. This dynamic training data is then utilized to train a dynamic model, gain thereof then set equal to unity such that the dynamic model is now valid over the entire input space. This is a linear model, and the historical data over the entire input space is then processed through this model prior to input to the neural network during training thereof to remove the dynamic component from the data, leaving the steady-state component for the purpose of training. This provides a valid model in the presence of historical data that has a large content of dynamic behavior. A single dynamic model is required for each output variable in a multi-input multi-output steady-state model such that for each output there is a separate dynamic model required for pre-filtering. They are combined in a single network made up of multiple individual steady-state models for each output. The dynamic model can be identified utilizing a weighting factor for the gain to force the dynamic gain of the dynamic model to the steady-state gain by weighting the difference thereof during optimization of the dynamic model. The steady-state model is optimized utilizing gain constraints during the optimization procedure such that the gain of the network is prevented from exceeding the gain constraints.
摘要翻译： 在没有稳态历史数据的情况下建模稳态网络的方法。 稳态神经网络可以通过在训练操作期间通过首先确定输入空间的局部区域中的动力学来将系统的动力学压印到输入数据上，从而提供一组动态训练数据。 然后利用该动态训练数据来训练动态模型，然后将其增益设置为等于1，使动态模型现在在整个输入空间上有效。 这是一个线性模型，然后在整个输入空间中的历史数据在通过该模型输入到神经网络之前通过该模型进行处理，以在训练期间从数据中移除动态分量，将稳态分量留在目的 训练。 这在存在具有大量动态行为的历史数据的情况下提供了有效的模型。 在多输入多输出稳态模型中，每个输出变量都需要单个动态模型，因此对于每个输出，都需要一个单独的动态模型来进行预滤波。 它们组合在由每个输出的多个单独稳态模型组成的单个网络中。 可以利用增益的加权因子来识别动态模型，以通过在动态模型的优化期间加权其差异来将动态模型的动态增益强制为稳态增益。 在优化过程中利用增益约束优化稳态模型，使得网络的增益被阻止超过增益约束。
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9.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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10.发明申请Laterally-actuated dispenser with one-way valve for storing and dispensing metered amounts of substances 有权具有用于存储和分配计量的物质的单向阀的侧向致动的分配器公开(公告)号：US20060065677A1
公开(公告)日：2006-03-30
申请号：US11237599
申请日：2005-09-27
申请人： Daniel Py , Julian Chan , David Fellows , Eric Hartman , Bing He
发明人： Daniel Py , Julian Chan , David Fellows , Eric Hartman , Bing He
IPC分类号： B67D5/40
CPC分类号： B65D83/0094 , A45D34/04 , A45D2200/055 , A45D2200/056 , A61M35/003 , B05B11/00416 , B05B11/3015 , B05B11/3032 , B05B11/3033 , B05B11/3094 , B65B1/04 , B65D83/0033 , G01F11/025 , G01F11/08
摘要： A dispenser for dispensing a substance, such as a pharmaceutical, ophthalmic, dermatological, cosmeceutical, cosmetic or other product, has a body defining a variable-volume storage chamber for storing the product. A dispensing portion of the dispenser is connected with the body and defines a bore coupled in fluid communication with the storage chamber for receiving substance therefrom, and an outlet aperture coupled in fluid communication with the bore. A one-way valve of the dispenser includes an axially-extending valve seat and an axially-extending flexible valve cover seated on the valve seat and defining a normally-closed, axially-extending seam therebetween forming a fluid-tight seal between the valve cover and valve seat. The flexible valve cover is movable relative to the valve seat and the seam is connectable in fluid communication with the outlet aperture to allow the passage of substance through the seam and out of the dispenser. The dispenser further comprises an actuator and piston assembly including a piston receivable within the bore, a manually-engageable actuator connected to the piston for moving the piston between first and second positions relative to the bore to dispense substance within the bore through the outlet aperture, and an integral, dome-shaped spring coupled to at least one of the piston and the actuator for biasing the piston in a direction from one of the first and second positions toward the other.
摘要翻译： 用于分配物质（例如药物，眼科，皮肤病学，药妆，化妆品或其他产品）的分配器具有限定用于存储产品的可变容积储存室的主体。 分配器的分配部分与主体连接并且限定了与储存室流体连通以接收物质的孔以及与孔流体连通的出口孔。 分配器的单向阀包括轴向延伸的阀座和位于阀座上的轴向延伸的柔性阀盖，并且在其间限定一个常闭的轴向延伸的接缝，形成在阀盖之间的流体密封 和阀座。 柔性阀盖可相对于阀座移动，并且接缝可与出口孔流体连通以允许物质通过接缝并从分配器中通过。 分配器还包括致动器和活塞组件，其包括可接收在孔内的活塞，连接到活塞的可手动接合的致动器，用于相对于孔移动活塞在第一和第二位置之间，以通过出口孔分配孔内的物质， 以及联接到所述活塞和所述致动器中的至少一个的整体的圆顶形弹簧，用于沿从所述第一位置和所述第二位置之一向另一个方向偏压所述活塞。
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