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    Systems and methods for extending the battery life of a wireless sensor in a building control system
    121.
    发明授权
    Systems and methods for extending the battery life of a wireless sensor in a building control system 有权

    公开(公告)号：US10042340B2

    公开(公告)日：2018-08-07

    申请号：US14989740

    申请日：2016-01-06

    Applicant: Johnson Controls Technology Company

    Inventor： Heidi A. Hofschulz ,  Robert D. Turney ,  Timothy C. Gamroth ,  Matthew J. Ellis

    IPC: G01M1/38 ,  G05B15/02 ,  G01D11/00 ,  G01D9/32 ,  H04W52/02 ,  H04W4/70

    Abstract: A building control system includes a wireless measurement device and a controller. The wireless measurement device measures a plurality of values of an environmental variable and uses the plurality of measured values to predict one or more future values of the environmental variable. The wireless device periodically transmits, at a transmission interval, a message that includes a current value of the environmental variable and the one or more predicted values of the environmental variable. The controller receives the message from the wireless device and parses the message to extract the current value and the one or more predicted future values of the environmental variable. The controller periodically and sequentially applies, at a controller update interval shorter than the transmission interval, each of the extracted values as an input to a control algorithm that operates to control the environmental variable.

    VARIABLE REFRIGERANT FLOW SYSTEM WITH MULTI-LEVEL MODEL PREDICTIVE CONTROL
    124.
    发明申请
    VARIABLE REFRIGERANT FLOW SYSTEM WITH MULTI-LEVEL MODEL PREDICTIVE CONTROL 审中-公开

    公开(公告)号：US20180004173A1

    公开(公告)日：2018-01-04

    申请号：US15635754

    申请日：2017-06-28

    Applicant: Johnson Controls Technology Company

    Inventor： Nishith R. Patel ,  Matthew J. Ellis ,  Michael J. Wenzel ,  Robert D. Turney ,  Brett M. Lenhardt

    IPC: G05B13/04 ,  F24F11/00 ,  G05D7/06

    Abstract: A model predictive control system is used to optimize energy cost in a variable refrigerant flow (VRF) system. The VRF system includes an outdoor subsystem and a plurality of indoor subsystems. The model predictive control system includes a high-level model predictive controller (MPC) and a plurality of low-level indoor MPCs. The high-level MPC performs a high-level optimization to generate an optimal indoor subsystem load profile for each of the plurality of indoor subsystems. The optimal indoor subsystem load profiles optimize energy cost. Each of the low-level indoor MPCs performs a low-level optimization to generate optimal indoor setpoints for one or more indoor VRF units of the corresponding indoor subsystem. The indoor setpoints can include temperature setpoints and/or refrigerant flow setpoints for the indoor VRF units.

    SYSTEM IDENTIFICATION AND MODEL DEVELOPMENT
    127.
    发明申请
    SYSTEM IDENTIFICATION AND MODEL DEVELOPMENT 审中-公开
    系统识别与模式开发

    公开(公告)号：US20160098022A1

    公开(公告)日：2016-04-07

    申请号：US14970187

    申请日：2015-12-15

    Applicant: Johnson Controls Technology Company

    Inventor： Michael J. Wenzel ,  Robert D. Turney

    IPC: G05B13/02

    CPC classification number: G05B13/0265 ,  G05B13/048 ,  G05B15/02 ,  G05B2219/2642 ,  G06F17/50 ,  G06F17/5004

    Abstract: A controller for a building system receives training data that includes input data and output data. The output data measures a state of the building system affected by both the input data and an extraneous disturbance. The controller performs a two-stage optimization process to identify system parameters and Kalman gain parameters of a dynamic model for the building system. During the first stage, the controller filters the training data to remove an effect of the extraneous disturbance from the output data and uses the filtered training data to identify the system parameters. During the second stage, the controller uses the non-filtered training data to identify the Kalman gain parameters. The controller uses the dynamic model with the identified system parameters and Kalman gain parameters to generate a setpoint for the building system. The building system uses the setpoint to affect the state measured by the output data.

    Abstract translation: 用于建筑系统的控制器接收包括输入数据和输出数据的训练数据。 输出数据测量受输入数据和外部干扰影响的建筑系统的状态。 控制器执行两阶段优化过程，以识别建筑系统的动态模型的系统参数和卡尔曼增益参数。 在第一阶段，控制器对训练数据进行过滤，以消除输出数据中的外部干扰的影响，并使用过滤的训练数据来识别系统参数。 在第二阶段期间，控制器使用未滤波的训练数据来识别卡尔曼增益参数。 控制器使用具有识别的系统参数和卡尔曼增益参数的动态模型来为建筑系统生成设定值。 建筑系统使用设定值来影响输出数据测量的状态。

    System identification and model development
    128.
    发明授权
    System identification and model development 有权
    系统识别和模型开发

    公开(公告)号：US09235657B1

    公开(公告)日：2016-01-12

    申请号：US13802233

    申请日：2013-03-13

    Applicant: Johnson Controls Technology Company

    Inventor： Michael J. Wenzel ,  Robert D. Turney

    IPC: G06F17/50

    CPC classification number: G05B13/0265 ,  G05B13/048 ,  G05B15/02 ,  G05B2219/2642 ,  G06F17/50 ,  G06F17/5004

    Abstract: Methods for system identification are presented using model predictive control to frame a gray-box parameterized state space model. System parameters are identified using an optimization procedure to minimize a first error cost function within a range of filtered training data. Disturbances are accounted for using an implicit integrator within the system model, as well as a parameterized Kalman gain. Kalman gain parameters are identified using an optimization procedure to minimize a second error cost function within a range of non-filtered training data. Recursive identification methods are presented to provide model adaptability using an extended Kalman filter to estimate model parameters and a Kalman gain to estimate system states.

    Abstract translation: 使用模型预测控制来呈现系统识别的方法来构建灰盒参数化状态空间模型。 使用优化程序来识别系统参数，以使过滤的训练数据的范围内的第一误差成本函数最小化。 使用系统模型中的隐式积分器以及参数化卡尔曼增益来解决干扰。 使用优化过程来识别卡尔曼增益参数，以使非滤波训练数据的范围内的第二误差成本函数最小化。 提出递归识别方法，以使用扩展卡尔曼滤波器来估计模型参数和卡尔曼增益来估计系统状态来提供模型适应性。

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