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    Controller for a laser using predictive models of materials processing
    4.
    发明授权
    Controller for a laser using predictive models of materials processing 有权
    使用材料加工预测模型的激光控制器

    公开(公告)号：US07324867B2

    公开(公告)日：2008-01-29

    申请号：US11048424

    申请日：2005-01-31

    申请人： William R. Dinauer ,  Thomas V. Weigman

    发明人： William R. Dinauer ,  Thomas V. Weigman

    IPC分类号： G06F19/00

    CPC分类号： B23K26/08 ,  B23K26/082 ,  G05B13/048 ,  G05B19/19 ,  G05B2219/42058 ,  G05B2219/45165

    摘要： Predictive models of physical parts of the laser processing system part determined. These predictive models are used to determine how the physical system will actually react. The predicted reaction from the models is used as feedback in order to produce the control signals. These physical models therefore adjust to the operation of the system, much in the way that actual feedback would adjust the operation of the system. However, the system may be used at faster speeds, where the actual feedback could not be produced fast enough. Different kinds of modeling are described, including in-position feedback which models sharp movements of the laser system, trajectory models which superimpose the commanded curve over the predicted actual curve to determine errors in trajectory, and constant/variable energy density controls.

    摘要翻译： 确定激光加工系统部件物理部件的预测模型。 这些预测模型用于确定物理系统将如何实际反应。 将来自模型的预测反应用作反馈以产生控制信号。 因此，这些物理模型可以适应系统的运行，而实际反馈的方式与调整系统的运行方式相当。 然而，系统可以以更快的速度使用，其中实际的反馈不能足够快地产生。 描述了不同种类的建模，包括对激光系统的急剧移动进行模拟的位置反馈，将指令曲线叠加在预测实际曲线上以确定轨迹误差的轨迹模型以及恒定/可变能量密度控制。

    Plant-wide optimization including batch operations
    5.
    发明授权
    Plant-wide optimization including batch operations 有权

    公开(公告)号：US12038737B2

    公开(公告)日：2024-07-16

    申请号：US17084157

    申请日：2020-10-29

    申请人： Honeywell International Inc.

    发明人： Joseph Lu

    IPC分类号： G05B19/4155 ,  G06N5/022

    CPC分类号： G05B19/4155 ,  G06N5/022 ,  G05B2219/32077 ,  G05B2219/42058

    摘要： Constraints are received on initial components and intermediate components. Information is received on the products to be produced including a quantity of each of the products to be produced and a specification that specifies how the intermediate components are to be combined to form each of the products. An optimization is performed that includes the continuous conversion of initial components into the intermediate components as well as subsequent production of the products, subject to the constraints on each of the initial components, the constraints on each of the intermediate components, and the quantity of each of the products to be produced.

    Adaptive distributed analytics system
    6.
    发明授权
    Adaptive distributed analytics system 有权

    公开(公告)号：US12013680B2

    公开(公告)日：2024-06-18

    申请号：US18146049

    申请日：2022-12-23

    申请人： Incucomm, Inc.

    发明人： Matthew Bowers ,  Christopher Niblo ,  Shane Poage ,  James Robinson ,  Steven D. Roemerman ,  John P. Volpi ,  Randal Allen ,  Eric Haney

    IPC分类号： G06F17/10 ,  G05B13/04 ,  G05B19/406 ,  G06N5/022

    CPC分类号： G05B19/406 ,  G05B13/048 ,  G06N5/022 ,  G05B2219/34477 ,  G05B2219/42058

    摘要： An aggregation layer subsystem, and method of operation thereof, for use with an architect subsystem and a plurality of edge processing devices in a distributed analytics system, wherein each edge processing device is adapted to monitor and control the operation of at least one monitored system according to a first analytic model, the aggregation layer subsystem comprising: a processor and memory, the memory containing instructions which, when executed by the processor, enables the aggregation layer subsystem to: receive a second analytic model from the architect subsystem, the second analytic model based on characteristics of at least one monitored system associated with at least one of the plurality of edge processing devices; receive monitored system information from each of the plurality of edge processing devices; and, provide control signals to the at least one monitored system, via one of the edge processing devices, according to the second analytic model in response to the monitored system information.

    Model predictive control using wireless process signals
    7.
    发明授权
    Model predictive control using wireless process signals 有权

    公开(公告)号：US10061286B2

    公开(公告)日：2018-08-28

    申请号：US14825725

    申请日：2015-08-13

    申请人： FISHER-ROSEMOUNT SYSTEMS, INC.

    发明人： Wilhelm K. Wojsznis ,  Terrence L. Blevins ,  Mark J. Nixon ,  John M. Caldwell

    IPC分类号： G05B19/042 ,  G05B13/04 ,  G05B17/02

    CPC分类号： G05B19/0423 ,  G05B13/048 ,  G05B17/02 ,  G05B2219/23292 ,  G05B2219/24075 ,  G05B2219/31121 ,  G05B2219/33192 ,  G05B2219/42058

    摘要： A multiple-input/multiple-output control routine in the form of a model predictive control (MPC) routine operates with wireless or other sensors that provide non-periodic, intermittent or otherwise delayed process variable measurement signals at an effective rate that is slower than the MPC controller scan or execution rate. The wireless MPC routine operates normally even when the measurement scan period for the controlled process variables is significantly larger than the operational scan period of the MPC controller routine, while providing control signals that enable control of the process in a robust and acceptable manner. During operation, the MPC routine uses an internal process model to simulate one or more measured process parameter values without performing model bias correction during the scan periods at which no new process parameter measurements are transmitted to the controller. When a new measurement for a particular process variable is available at the controller, the model prediction and simulated parameter values are updated with model bias correction based on the new measurement value, according to traditional MPC techniques.

    Discrepancy detection apparatus and methods for machine learning
    8.
    发明授权
    Discrepancy detection apparatus and methods for machine learning 有权
    差异检测装置和机器学习方法

    公开(公告)号：US09248569B2

    公开(公告)日：2016-02-02

    申请号：US14088258

    申请日：2013-11-22

    申请人： BRAIN CORPORATION

    发明人： Patryk Laurent ,  Jean-Baptiste Passot ,  Filip Ponulak ,  Eugene Izhikevich

    IPC分类号： B25J9/16

    CPC分类号： B25J9/163 ,  G05B13/026 ,  G05B2219/40512 ,  G05B2219/42058 ,  G05D1/0088 ,  G05D2201/02 ,  G06N3/049 ,  G06N99/005

    摘要： A robotic device may comprise an adaptive controller configured to learn to predict consequences of robotic device's actions. During training, the controller may receive a copy of the planned and/or executed motor command and sensory information obtained based on the robot's response to the command. The controller may predict sensory outcome based on the command and one or more prior sensory inputs. The predicted sensory outcome may be compared to the actual outcome. Based on a determination that the prediction matches the actual outcome, the training may stop. Upon detecting a discrepancy between the prediction and the actual outcome, the controller may provide a continuation signal configured to indicate that additional training may be utilized. In some classification implementations, the discrepancy signal may be used to indicate occurrence of novel (not yet learned) objects in the sensory input and/or indicate continuation of training to recognize said objects.

    摘要翻译： 机器人设备可以包括被配置为学习预测机器人设备的动作的后果的自适应控制器。 在训练期间，控制器可以接收基于机器人对命令的响应获得的计划和/或执行的电动机命令和感觉信息的副本。 控制器可以基于命令和一个或多个现有的感觉输入来预测感觉结果。 预测的感觉结果可能与实际结果进行比较。 根据预测与实际结果的匹配，培训可能会停止。 在检测到预测和实际结果之间的差异时，控制器可以提供被配置为指示可以利用附加训练的连续信号。 在一些分类实现中，差异信号可以用于指示感觉输入中的新颖（尚未学习）的对象的发生和/或指示用于识别所述对象的训练的继续。

    ADAPTIVE DISTRIBUTED ANALYTICS SYSTEM
    10.
    发明公开
    ADAPTIVE DISTRIBUTED ANALYTICS SYSTEM 审中-公开

    公开(公告)号：US20240280959A1

    公开(公告)日：2024-08-22

    申请号：US18438739

    申请日：2024-02-12

    申请人： Incucomm, Inc.

    发明人： Matthew Bowers ,  Christopher Niblo ,  Shane Poage ,  James Robinson ,  Steven D. Roemerman ,  John P. Volpi ,  Randal Allen ,  Eric Haney

    IPC分类号： G05B19/406 ,  G05B13/04 ,  G06N5/022

    CPC分类号： G05B19/406 ,  G05B13/048 ,  G06N5/022 ,  G05B2219/34477 ,  G05B2219/42058

    摘要： A distributed analytics system to control an operation of a monitored system, and method of operation thereof, including an architect subsystem and an edge processing device. The edge subsystem includes an edge processing device associated with the monitored system. The architect subsystem is configured to deploy an analytic model to the edge processing device based on characteristics of the monitored system. The edge processing device is configured to receive the analytic model and independently perform predictive and prescriptive analytics on dynamic input data associated with the monitored system, provide control signals to the monitored system according to the predictive and prescriptive analytics, and provide information to the architect subsystem, including monitored system responses to the control signals. The architect subsystem is configured to modify the analytic model to improve system performance of the monitored system.