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    Controlling Sway of Elevator Rope Using Movement of Elevator Car
    1.
    发明申请
    Controlling Sway of Elevator Rope Using Movement of Elevator Car 有权
    使用电梯车的运动控制电梯绳的摇摆

    公开(公告)号：US20150008075A1

    公开(公告)日：2015-01-08

    申请号：US13933290

    申请日：2013-07-02

    申请人： Mitsubishi Electric Corporation ,  Mitsubishi Electric Research Laboratories, Inc.

    发明人： Mouhacine Benosman ,  Daiki Fukui ,  Seiji Watanabe ,  Daisuke Nakazawa

    IPC分类号： B66B5/02

    CPC分类号： B66B7/06

    摘要： A method reduces a sway of an elevator rope supporting an elevator car within an elevator system using an elevator sheave. The method controls, using a movement of the elevator sheave, a tension of the elevator rope according to a control law of the tension of the elevator rope between a first point and a second point. The first point is associated with a contact of the elevator rope with the elevator sheave. The second point is associated with a contact of the elevator rope with the elevator car or a counterweight of the elevator car. The control law is a function of one or combination of a relative position, a relative velocity and a relative acceleration between the first and the second points.

    摘要翻译： 一种方法减少了使用电梯滑轮在电梯系统内支撑电梯轿厢的电梯绳索的摆动。 该方法根据电梯绳索在第一点和第二点之间的张力的控制规律来控制电梯滑轮的运动，电梯绳索的张​​力。 第一点与电梯绳索与电梯滑轮的接触有关。 第二点与电梯绳索与电梯轿厢或电梯轿厢的配重的接触相关联。 控制律是第一和第二点之间的相对位置，相对速度和相对加速度之一或组合的函数。

    Controlling sway of elevator rope using movement of elevator car
    4.
    发明授权
    Controlling sway of elevator rope using movement of elevator car 有权
    使用电梯轿厢的运动控制电梯绳索的摇摆

    公开(公告)号：US09475674B2

    公开(公告)日：2016-10-25

    申请号：US13933290

    申请日：2013-07-02

    申请人： Mitsubishi Electric Research Laboratories, Inc. ,  Mitsubishi Electric Corporation

    发明人： Mouhacine Benosman ,  Daiki Fukui ,  Seiji Watanabe ,  Daisuke Nakazawa

    IPC分类号： B66B1/28 ,  B66B7/06

    CPC分类号： B66B7/06

    摘要： A method reduces a sway of an elevator rope supporting an elevator car by controlling a tension of the elevator rope. The tension is controlled using a movement of the elevator sheave according to a control law of the tension of the elevator rope between two points. The first point is associated with a contact of the elevator rope with the elevator sheave. The second point is associated with a contact of the elevator rope with the elevator car or a counterweight of the elevator car. The control law is a function of one or combination of a relative position, a relative velocity and a relative acceleration between the first and the second points.

    摘要翻译： 通过控制电梯绳索的张​​力，一种方法减少了支撑电梯轿厢的电梯绳索的摆动。 使用电梯滑轮的运动根据电梯绳索在两点之间的张力的控制规律来控制张力。 第一点与电梯绳索与电梯滑轮的接触有关。 第二点与电梯绳索与电梯轿厢或电梯轿厢的配重的接触相关联。 控制律是第一和第二点之间的相对位置，相对速度和相对加速度之一或组合的函数。

    Simulation-in-the-loop Tuning of Robot Parameters for System Modeling and Control
    6.
    发明申请
    Simulation-in-the-loop Tuning of Robot Parameters for System Modeling and Control 有权

    公开(公告)号：US20220305646A1

    公开(公告)日：2022-09-29

    申请号：US17214810

    申请日：2021-03-27

    申请人： Mitsubishi Electric Research Laboratories, Inc.

    发明人： Siddarth Jain ,  Jeroen van Baar ,  Radu Ioan Corcodel ,  Alan Sullivan ,  Mouhacine Benosman

    IPC分类号： B25J9/16 ,  B25J13/08

    摘要： A system for parameter tuning for robotic manipulators is provided. The system includes an interface configured to receive a task specification, a plurality of physical parameters, and a plurality of control parameters, wherein the interface is configured to communicate with a real-world robot via a robot controller. The system further includes a memory to store computer-executable programs including a robot simulation module, a robot controller, and an auto-tuning module a processor, in connection with the memory. In this case, the processor is configured to acquire, in communication with the real-world robot, state values of the real-world robot, state values of the robot simulation module, simultaneously update, by use of a predetermined optimization algorithm with the auto-tuning module, an estimate of one or more of the physical, and said control parameters, and store the updated parameters.

    Apparatus and Method for Controlling a System Having Uncertainties in its Dynamics
    7.
    发明申请
    Apparatus and Method for Controlling a System Having Uncertainties in its Dynamics 有权

    公开(公告)号：US20220121188A1

    公开(公告)日：2022-04-21

    申请号：US17123701

    申请日：2020-12-16

    申请人： Mitsubishi Electric Research Laboratories, Inc.

    发明人： Mouhacine Benosman ,  Reazul Russel ,  Jeroen van Baar

    IPC分类号： G05B23/02 ,  G05B17/02

    摘要： A controller for controlling a system having uncertainties in its dynamics subject to constraints on an operation of the system is provided. The controller is configured to acquire historical data of the operation of the system, and determine, for the system in a current state, a current control action transitioning a state of the system from the current state to a next state. The current control action is determined according to a robust and constraint Markov decision process (RCMDP) that uses the historical data to optimize a performance cost of the operation of the system subject to an optimization of a safety cost enforcing the constraints on the operation, wherein a state transition for each of state and action pairs in the performance cost and the safety cost is represented by a plurality of state transitions capturing the uncertainties of the dynamics of the system.

    System and Method for Robust Optimization for Trajectory-Centric ModelBased Reinforcement Learning
    8.
    发明申请
    System and Method for Robust Optimization for Trajectory-Centric ModelBased Reinforcement Learning 有权

    公开(公告)号：US20210178600A1

    公开(公告)日：2021-06-17

    申请号：US16712329

    申请日：2019-12-12

    申请人： Mitsubishi Electric Research Laboratories, Inc.

    发明人： Devesh Jha ,  Patrik Kolaric ,  Arvind Raghunathan ,  Mouhacine Benosman ,  Diego Romeres

    IPC分类号： B25J9/16 ,  G06N20/00 ,  G06N3/04

    摘要： A controller for optimizing a local control policy of a system for trajectory-centric reinforcement learning is provided. The controller includes performing steps of learning a stochastic predictive model for the system using a set of data collected during trial and error experiments performed using an initial random control policy, estimating mean prediction and uncertainty associated, determining a local set of deviations of the system using the learned stochastic system model, from a nominal system state upon use of a control input at a current time-step, determining a system state with a worst-case deviation, determining a gradient of the robustness constraint, providing and solving a robust policy optimization problem using non-linear programming to obtain system trajectory and stabilizing local policy simultaneously, updating the control data according to the solved optimization problem, and output the updated control data via the interface.