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    • 2. 发明申请
    • Adaptive control system having direct output feedback and related apparatuses and methods
    • 具有直接输出反馈的自适应控制系统及相关装置和方法
    • US20050182499A1
    • 2005-08-18
    • US11105826
    • 2005-04-12
    • Anthony CaliseNaira HovakimyanMoshe Idan
    • Anthony CaliseNaira HovakimyanMoshe Idan
    • G05B13/02G06F15/18
    • G05B13/027
    • An adaptive control system (ACS) uses direct output feedback to control a plant. The ACS uses direct adaptive output feedback control developed for highly uncertain nonlinear systems, that does not rely on state estimation. The approach is also applicable to systems of unknown, but bounded dimension, whose output has known, but otherwise arbitrary relative degree. This includes systems with both parameter uncertainty and unmodeled dynamics. The result is achieved by extending the universal function approximation property of linearly parameterized neural networks to model unknown system dynamics from input/output data. The network weight adaptation rule is derived from Lyapunov stability analysis, and guarantees that the adapted weight errors and the tracking error are bounded.
    • 自适应控制系统(ACS)使用直接输出反馈来控制设备。 ACS使用针对高度不确定的非线性系统开发的直接自适应输出反馈控制,不依赖于状态估计。 该方法也适用于未知但有界尺寸的系统,其输出已知,但以任意相对度计。 这包括具有参数不确定性和未建模动力学的系统。 结果是通过将线性参数化神经网络的通用函数近似属性扩展到来自输入/输出数据的未知系统动力学来实现。 网络权重适应规则是从李亚普诺夫稳定性分析中得出的,并保证适应的权重误差和跟踪误差是有界的。
    • 5. 发明申请
    • Adaptive observer and related method
    • 自适应观察者及相关方法
    • US20050137724A1
    • 2005-06-23
    • US10961883
    • 2004-10-08
    • Naira HovakimyanAnthony CaliseVenkatesh Madyastha
    • Naira HovakimyanAnthony CaliseVenkatesh Madyastha
    • G05B13/02G05B13/04G06G7/00G06F15/18
    • G05B13/027G05B13/04
    • A disclosed apparatus comprises an adaptive observer that has an adaptive element to augment a linear observer to enhance its ability to control a nonlinear system. The adaptive element comprises a first, and optionally a second, nonlinearly parameterized neural network unit, the inputs and output layer weights of which can be adapted on line. The adaptive observer generates the neural network units' teaching signal by an additional linear error observer of the nominal system's error dynamics. The adaptive observer has the ability to track an observed system in the presence of unmodeled dynamics and disturbances. The adaptive observer comprises a delay element incorporated in the adaptive element in order to provide delayed values of an actual output signal and a control signal to the neural network units.
    • 所公开的装置包括自适应观测器,其具有用于增强线性观察者以增强其控制非线性系统的能力的自适应元件。 自适应元件包括第一和非必需的第二非线性参数化神经网络单元,其输入和输出层权重可以在线适配。 自适应观测器通过名义系统误差动力学的附加线性误差观测器产生神经网络单元的教学信号。 自适应观测器具有在存在未建模的动力学和扰动的情况下跟踪观测系统的能力。 自适应观察器包括并入自适应元件中的延迟元件,以便向神经网络单元提供实际输出信号和控制信号的延迟值。
    • 7. 发明授权
    • Adaptive control system having direct output feedback and related apparatuses and methods
    • 具有直接输出反馈的自适应控制系统及相关装置和方法
    • US07418432B2
    • 2008-08-26
    • US11105826
    • 2005-04-12
    • Anthony J. CaliseNaira HovakimyanMoshe Idan
    • Anthony J. CaliseNaira HovakimyanMoshe Idan
    • G05B13/02G06E1/00G06E3/00G06F15/18G06G7/00
    • G05B13/027
    • An adaptive control system (ACS) uses direct output feedback to control a plant. The ACS uses direct adaptive output feedback control developed for highly uncertain nonlinear systems, that does not rely on state estimation. The approach is also applicable to systems of unknown, but bounded dimension, whose output has known, but otherwise arbitrary relative degree. This includes systems with both parameter uncertainty and unmodeled dynamics. The result is achieved by extending the universal function approximation property of linearly parameterized neural networks to model unknown system dynamics from input/output data. The network weight adaptation rule is derived from Lyapunov stability analysis, and guarantees that the adapted weight errors and the tracking error are bounded.
    • 自适应控制系统(ACS)使用直接输出反馈来控制设备。 ACS使用针对高度不确定的非线性系统开发的直接自适应输出反馈控制,不依赖于状态估计。 该方法也适用于未知但有界尺寸的系统,其输出已知,但以任意相对度计。 这包括具有参数不确定性和未建模动力学的系统。 结果是通过将线性参数化神经网络的通用函数近似属性扩展到来自输入/输出数据的未知系统动力学来实现。 网络权重适应规则是从李亚普诺夫稳定性分析中得出的,并保证适应的权重误差和跟踪误差是有界的。