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1. 发明授权

US6047221A Method for steady-state identification based upon identified dynamics 失效
标题翻译：基于确定的动力学的稳态识别方法
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
IPC分类号： G05B23/02 , G05B13/02
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。

2. 发明授权

US07050866B2 Dynamic controller for controlling a system 有权
标题翻译：用于控制系统的动态控制器
公开(公告)号：US07050866B2
公开(公告)日：2006-05-23
申请号：US10847211
申请日：2004-05-17
申请人： Gregory D. Martin , Eugene Boe , Stephen Piche , James David Keeler , Douglas Timmer , Mark Gerules , John P. Havener
发明人： Gregory D. Martin , Eugene Boe , Stephen Piche , James David Keeler , Douglas Timmer , Mark Gerules , John P. Havener
IPC分类号： G05B13/02
CPC分类号： G05B17/02 , G05B13/048
摘要： A method for providing independent static and dynamic models in a prediction, control and optimization environment utilizes an independent static model (20) and an independent dynamic model (22). The static model (20) is a rigorous predictive model that is trained over a wide range of data, whereas the dynamic model (22) is trained over a narrow range of data. The gain K of the static model (20) is utilized to scale the gain k of the dynamic model (22). The forced dynamic portion of the model (22) referred to as the bi variables are scaled by the ratio of the gains K and k. The bi have a direct effect on the gain of a dynamic model (22). This is facilitated by a coefficient modification block (40). Thereafter, the difference between the new value input to the static model (20) and the prior steady-state value is utilized as an input to the dynamic model (22). The predicted dynamic output is then summed with the previous steady-state value to provide a predicted value Y. Additionally, the path that is traversed between steady-state value changes.
摘要翻译：在预测，控制和优化环境中提供独立的静态和动态模型的方法使用独立的静态模型（20）和独立的动态模型（22）。静态模型（20）是一种严格的预测模型，可在广泛的数据范围内进行训练，而动态模型（22）则是在窄范围的数据上进行训练。使用静态模型（20）的增益K来缩放动态模型（22）的增益k。被称为变量的模型（22）的强制动态部分通过增益K和k的比率来缩放。 b）对动态模型（22）的增益有直接的影响。这通过系数修改块（40）来促进。此后，将输入到静态模型（20）的新值与先前稳态值之间的差用作动态模型（22）的输入。然后将预测的动态输出与先前的稳态值相加以提供预测值Y.此外，在稳态值变化之间经过的路径。

3. 发明授权

US07047089B2 Kiln thermal and combustion control 有权
公开(公告)号：US07047089B2
公开(公告)日：2006-05-16
申请号：US10843160
申请日：2004-05-11
申请人： Gregory D. Martin , Eugene Boe , Stephen Piche , James David Keeler , Douglas Timmer , Mark Gerules , John P. Havener
发明人： Gregory D. Martin , Eugene Boe , Stephen Piche , James David Keeler , Douglas Timmer , Mark Gerules , John P. Havener
IPC分类号： G05B13/02
CPC分类号： G05B13/048 , G05B13/042 , G05B17/02
摘要： A method and apparatus for controlling a non-linear mill. A linear controller is provided having a linear gain k that is operable to receive inputs representing measured variables of the plant and predict on an output of the linear controller predicted control values for manipulatible variables that control the plant. A non-linear model of the plant is provided for storing a representation of the plant over a trained region of the operating input space and having a steady-state gain K associated therewith. The gain k of the linear model is adjusted with the gain K of the non-linear model in accordance with a predetermined relationship as the measured variables change the operating region of the input space at which the linear controller is predicting the values for the manipulatible variables. The predicted manipulatible variables are then output after the step of adjusting the gain k.

4. 发明授权

US06735483B2 Method and apparatus for controlling a non-linear mill 有权
标题翻译：用于控制非线性磨机的方法和装置
公开(公告)号：US06735483B2
公开(公告)日：2004-05-11
申请号：US10314675
申请日：2002-12-09
申请人： Gregory D. Martin , Eugene Boe , Stephen Piche , James David Keeler , Douglas Timmer , Mark Gerules , John P. Havener
发明人： Gregory D. Martin , Eugene Boe , Stephen Piche , James David Keeler , Douglas Timmer , Mark Gerules , John P. Havener
IPC分类号： G05B1302
CPC分类号： G05B13/048 , G05B13/042 , G05B17/02
摘要： A method and apparatus for controlling a non-linear mill. A linear controller is provided having a linear gain k that is operable to receive inputs representing measured variables of the plant and predict on an output of the linear controller predicted control values for manipulatible variables that control the plant. A non-linear model of the plant is provided for storing a representation of the plant over a trained region of the operating input space and having a steady-state gain K associated therewith. The gain k of the linear model is adjusted with the gain K of the non-linear model in accordance with a predetermined relationship as the measured variables change the operating region of the input space at which the linear controller is predicting the values for the manipulatible variables. The predicted manipulatible variables are then output after the step of adjusting the gain k
摘要翻译：一种用于控制非线性磨机的方法和装置。提供具有线性增益k的线性控制器，其可操作以接收表示工厂的测量变量的输入，并且对线性控制器的输出预测用于控制工厂的操纵性变量的预测控制值。提供工厂的非线性模型，用于在工作输入空间的经过训练的区域上存储工厂的表示，并具有与其相关联的稳态增益K. 线性模型的增益k根据预定的关系用非线性模型的增益K进行调整，因为测量的变量改变了线性控制器预测操作变量的值的输入空间的操作区域。然后在调整增益k的步骤之后输出预测的操纵变量

5. 发明授权

US06438430B1 Kiln thermal and combustion control 有权
标题翻译：窑炉和燃烧控制
公开(公告)号：US06438430B1
公开(公告)日：2002-08-20
申请号：US09568190
申请日：2000-05-09
申请人： Gregory D. Martin , Eugene Boe , Stephen Piche , James David Keeler , Douglas Timmer , Mark Gerules , John P. Havener
发明人： Gregory D. Martin , Eugene Boe , Stephen Piche , James David Keeler , Douglas Timmer , Mark Gerules , John P. Havener
IPC分类号： G05B1302
CPC分类号： G05B13/048 , G05B13/042 , G05B17/02
摘要： A kiln thermal and combustion control. A predictive model is provided of the dynamics of selected aspects of the operation of the plant for modeling the dynamics thereof. The model has at least two discrete models associated therewith that model at least two of the selected aspects, the at least two discrete models having different dynamic responses. An optimizer receives desired values for the selected aspects of the operation of the plant modeled by the model and optimizes the inputs to the model to minimize error between the predicted and desired values. A control input device then applies the optimized input values to the plant after optimization thereof.
摘要翻译：窑炉和燃烧控制。提供了用于对其动态建模的工厂的操作的选定方面的动态的预测模型。所述模型具有至少两个与之相关的离散模型，所述至少两个所选择的方面模型，所述至少两个离散模型具有不同的动态响应。优化器接收由模型建模的工厂的操作的所选方面的期望值，并优化对模型的输入以最小化预测值和期望值之间的误差。然后，控制输入装置优化之后将优化的输入值应用于工厂。

6. 发明授权

US07110834B2 Kiln thermal and combustion control 有权
标题翻译：窑炉和燃烧控制
公开(公告)号：US07110834B2
公开(公告)日：2006-09-19
申请号：US10669028
申请日：2003-09-23
申请人： Gregory D. Martin , Eugene Boe , Stephen Piche , James David Keeler , Douglas Timmer , Mark Gerules , John P. Havener , Steven J. McGarel
发明人： Gregory D. Martin , Eugene Boe , Stephen Piche , James David Keeler , Douglas Timmer , Mark Gerules , John P. Havener , Steven J. McGarel
IPC分类号： G05B13/02
CPC分类号： G05B13/048 , G05B13/042 , G05B17/02
摘要： A kiln thermal and combustion control. A predictive model is provided of the dynamics of selected aspects of the operation of the system for modeling the dynamics thereof. The model has at least two discrete models associated therewith that model at least two of the selected aspects, the at least two discrete models having different dynamic responses. An optimizer receives desired values for the selected aspects of the operation of the system modeled by the model and optimizes the inputs to the model to minimize error between the predicted and desired values. A control input device then applies the optimized input values to the system after optimization thereof.
摘要翻译：窑炉和燃烧控制。提供了用于对其动力学进行建模的系统的操作的选定方面的动态的预测模型。所述模型具有至少两个与之相关的离散模型，所述至少两个所选择的方面模型，所述至少两个离散模型具有不同的动态响应。优化器接收由模型建模的系统的操作的所选方面的期望值，并优化对模型的输入以最小化预测值和期望值之间的误差。然后，控制输入装置优化后对系统施加优化的输入值。

7. 发明授权

US06738677B2 Method and apparatus for modeling dynamic and steady-state processes for prediction, control and optimization 有权
标题翻译：用于预测，控制和优化的动态和稳态过程建模的方法和装置
公开(公告)号：US06738677B2
公开(公告)日：2004-05-18
申请号：US10302923
申请日：2002-11-22
申请人： Gregory D. Martin , Eugene Boe , Stephen Piche , James David Keeler , Douglas Timmer , Mark Gerules , John P. Havener
发明人： Gregory D. Martin , Eugene Boe , Stephen Piche , James David Keeler , Douglas Timmer , Mark Gerules , John P. Havener
IPC分类号： G05B1302
CPC分类号： G05B17/02 , G05B13/048
摘要： A method for providing independent static and dynamic models in a prediction, control and optimization environment utilizes an independent static model (20) and an independent dynamic model (22). The static model (20) is a rigorous predictive model that is trained over a wide range of data, whereas the dynamic model (22) is trained over a narrow range of data. The gain K of the static model (20) is utilized to scale the gain k of the dynamic model (22). The forced dynamic portion of the model (22) referred to as the bi variables are scaled by the ratio of the gains K and k. The bi have a direct effect on the gain of a dynamic model (22). This is facilitated by a coefficient modification block (40). Thereafter, the difference between the new value input to the static model (20) and the prior steady-state value is utilized as an input to the dynamic model (22). The predicted dynamic output is then summed with the previous steady-state value to provide a predicted value Y. Additionally, the path that is traversed between steady-state value changes.
摘要翻译：在预测，控制和优化环境中提供独立的静态和动态模型的方法使用独立的静态模型（20）和独立的动态模型（22）。静态模型（20）是一种严格的预测模型，可在广泛的数据范围内进行训练，而动态模型（22）则是在窄范围的数据上进行训练。使用静态模型（20）的增益K来缩放动态模型（22）的增益k。被称为双变量的模型（22）的强制动态部分通过增益K和k的比率来缩放。 bi对动态模型的获得有直接的影响（22）。这通过系数修改块（40）来促进。此后，将输入到静态模型（20）的新值与先前稳态值之间的差用作动态模型（22）的输入。然后将预测的动态输出与先前的稳态值相加以提供预测值Y.此外，在稳态值变化之间经过的路径。

8. 发明授权

US5933345A Method and apparatus for dynamic and steady state modeling over a desired path between two end points 失效
标题翻译：用于在两个端点之间的期望路径上进行动态和稳态建模的方法和装置
公开(公告)号：US5933345A
公开(公告)日：1999-08-03
申请号：US643464
申请日：1996-05-06
申请人： Gregory D. Martin , Eugene Boe , Stephen Piche , James David Keeler , Douglas Timmer , Mark Gerules , John P. Havener
发明人： Gregory D. Martin , Eugene Boe , Stephen Piche , James David Keeler , Douglas Timmer , Mark Gerules , John P. Havener
IPC分类号： G05B13/02 , G05B13/04 , G05B17/02 , G05B21/02
CPC分类号： G05B17/02 , G05B13/048
摘要： A method for providing independent static and dynamic models in a prediction, control and optimization environment utilizes an independent static model and an independent dynamic model. The static model is a rigorous predictive model that is trained over a wide range of data, whereas the dynamic model is trained over a narrow range of data. The gain K of the static model is utilized to scale the gain k of the dynamic model. The forced dynamic portion of the model referred to as the b.sub.i variables are scaled by the ratio of the gains K and k. The b.sub.i have a direct effect on the gain of a dynamic model. This is facilitated by a coefficient modification block. Thereafter, the difference between the new value input to the static model and the prior steady-state value is utilized as an input to the dynamic model. The predicted dynamic output is then summed with the previous steady-state value to provide a predicted value Y. Additionally, the path that is traversed between steady-state value changes.
摘要翻译：在预测，控制和优化环境中提供独立静态和动态模型的方法利用独立的静态模型和独立的动态模型。静态模型是一种严格的预测模型，可以在广泛的数据范围内进行训练，而动态模型则是通过窄范围的数据进行训练。利用静态模型的增益K来缩放动态模型的增益k。被称为bi变量的模型的强制动态部分通过增益K和k的比率来缩放。 bi对动态模型的增益有直接的影响。这通过系数修改块来促进。此后，将输入到静态模型的新值与先前的稳态值之间的差用作动态模型的输入。然后将预测的动态输出与先前的稳态值相加以提供预测值Y.此外，在稳态值变化之间经过的路径。

9. 发明授权

US06625501B2 Kiln thermal and combustion control 有权
公开(公告)号：US06625501B2
公开(公告)日：2003-09-23
申请号：US10218970
申请日：2002-08-14
申请人： Gregory D. Martin , Eugene Boe , Stephen Piche , James David Keeler , Douglas Timmer , Mark Gerules , John P. Havener
发明人： Gregory D. Martin , Eugene Boe , Stephen Piche , James David Keeler , Douglas Timmer , Mark Gerules , John P. Havener
IPC分类号： G05B1918
CPC分类号： G05B13/048 , G05B13/042 , G05B17/02
摘要： A kiln thermal and combustion control. A predictive model is provided of the dynamics of selected aspects of the operation of the plant for modeling the dynamics thereof. The model has at least two discrete models associated therewith that model at least two of the selected aspects, the at least two discrete models having different dynamic responses. An optimizer receives desired values for the selected aspects of the operation of the plant modeled by the model and optimizes the inputs to the model to minimize error between the predicted and desired values. A control input device then applies the optimized input values to the plant after optimization thereof.

10. 发明授权

US06487459B1 Method and apparatus for modeling dynamic and steady-state processes for prediction, control and optimization 有权
标题翻译：用于预测，控制和优化的动态和稳态过程建模的方法和装置
公开(公告)号：US06487459B1
公开(公告)日：2002-11-26
申请号：US09250432
申请日：1999-02-16
申请人： Gregory D. Martin , Eugene Boe , Stephen Piche , James David Keeler , Douglas Timmer , Mark Gerules , John P. Havener
发明人： Gregory D. Martin , Eugene Boe , Stephen Piche , James David Keeler , Douglas Timmer , Mark Gerules , John P. Havener
IPC分类号： G05B1302
CPC分类号： G05B17/02 , G05B13/048
摘要： A method for providing independent static and dynamic models in a prediction, control and optimization environment utilizes an independent static model (20) and an independent dynamic model (22). The static model (20) is a rigorous predictive model that is trained over a wide range of data, whereas the dynamic model (22) is trained over a narrow range of data. The gain K of the static model (20) is utilized to scale the gain k of the dynamic model (22). The forced dynamic portion of the model (22) referred to as the bi variables are scaled by the ratio of the gains K and k. The bi have a direct effect on the gain of a dynamic model (22). This is facilitated by a coefficient modification block (40). Thereafter, the difference between the new value input to the static model (20) and the prior steady-state value is utilized as an input to the dynamic model (22). The predicted dynamic output is then summed with the previous steady-state value to provide a predicted value Y. Additionally, the path that is traversed between steady-state value changes.
摘要翻译：在预测，控制和优化环境中提供独立的静态和动态模型的方法使用独立的静态模型（20）和独立的动态模型（22）。静态模型（20）是一种严格的预测模型，可在广泛的数据范围内进行训练，而动态模型（22）则是在窄范围的数据上进行训练。使用静态模型（20）的增益K来缩放动态模型（22）的增益k。被称为双变量的模型（22）的强制动态部分通过增益K和k的比率来缩放。 bi对动态模型的获得有直接的影响（22）。这通过系数修改块（40）来促进。此后，将输入到静态模型（20）的新值与先前稳态值之间的差用作动态模型（22）的输入。然后将预测的动态输出与先前的稳态值相加以提供预测值Y.此外，在稳态值变化之间经过的路径。

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