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    • 1. 发明授权
    • Optimization control method for shock absorber
    • 减震器优化控制方法
    • US06212466B1
    • 2001-04-03
    • US09484877
    • 2000-01-18
    • Sergei V. UlyanovTakahide Hagiwara
    • Sergei V. UlyanovTakahide Hagiwara
    • G05B1300
    • G05B13/0285B60G17/018B60G17/0182B60G2200/142B60G2202/135B60G2206/99B60G2400/821B60G2500/10B60G2600/1878B60G2600/1879G05B13/0265G05B13/027
    • A control system for optimizing the performance of a vehicle suspension system by controlling the damping factor of one or more shock absorbers is described. The control system uses a fitness (performance) function that is based on the physical laws of minimum entropy. The control system uses a fuzzy neural network that is trained by a genetic analyzer. The genetic analyzer uses a fitness function that maximizes information while minimizing entropy production. The fitness function uses a difference between the time differential of entropy from a control signal produced in a learning control module and the time differential of the entropy calculated by a model of the suspension system that uses the control signal as an input. The entropy calculation is based on a dynamic model of an equation of motion for the suspension system such that the suspension system is treated as an open dynamic system.
    • 描述了通过控制一个或多个减震器的阻尼系数来优化车辆悬架系统的性能的控制系统。 控制系统使用基于最小熵的物理定律的适应度(性能)函数。 控制系统使用由遗传分析仪训练的模糊神经网络。 遗传分析仪使用最大化信息的适应度函数,同时最小化熵产生。 适应度函数使用来自学习控制模块中产生的控制信号的熵的时间差与通过使用控制信号作为输入的悬架系统的模型计算的熵的时间微分之差。 熵计算基于悬架系统的运动方程的动态模型,使得悬架系统被视为开放动态系统。
    • 2. 发明授权
    • System for intelligent control of a vehicle suspension based on soft computing
    • 基于软计算的车辆悬架智能控制系统
    • US06463371B1
    • 2002-10-08
    • US09177169
    • 1998-10-22
    • Sergei V. UlyanovTakahide Hagiwara
    • Sergei V. UlyanovTakahide Hagiwara
    • G06F1700
    • B60G17/019B60G17/018B60G2600/09B60G2600/124B60G2600/14B60G2600/16B60G2600/17B60G2600/18B60G2600/90B60G2800/702B60G2800/91
    • A reduced control system suitable for control of an active suspension system as a controlled plant is described. The reduced control system is configured to use a reduced sensor set for controlling the suspension without significant loss of control quality (accuracy) as compared to an optimal control system with an optimum sensor set. The control system calculates the information content provided by the reduced sensor set as compared to the information content provided by the optimum set. The control system also calculates the difference between the entropy production rate of the plant and the entropy production rate of the controller. A genetic optimizer is used to tune a fuzzy neural network in the reduced controller. A fitness function for the genetic optimizer provides optimum control accuracy in the reduced control system by minimizing the difference in entropy production while maximizing the sensor information content.
    • 描述了适用于控制作为受控设备的主动悬挂系统的减少控制系统。 与具有最佳传感器组的最佳控制系统相比,减小的控制系统被配置为使用用于控制悬架的减小的传感器组而不显着损失控制质量(精度)。 与由最佳集合提供的信息内容相比,控制系统计算由缩小传感器组提供的信息内容。 控制系统还计算了工厂熵产生率与控制器熵产生率之间的差异。 遗传优化器用于调节减少控制器中的模糊神经网络。 遗传优化器的适应度函数通过最小化熵产生的差异,同时最大化传感器信息内容,在减少的控制系统中提供最佳的控制精度。
    • 3. 发明授权
    • Optimization control method for shock absorber
    • 减震器优化控制方法
    • US06496761B1
    • 2002-12-17
    • US09724581
    • 2000-11-28
    • Sergei V. UlyanovTakahide Hagiwara
    • Sergei V. UlyanovTakahide Hagiwara
    • B60G2300
    • G05B13/0285B60G17/018B60G17/0182B60G2200/142B60G2202/135B60G2206/99B60G2400/821B60G2500/10B60G2600/1878B60G2600/1879G05B13/0265G05B13/027
    • A control system for optimizing the performance of a vehicle suspension system by controlling the damping factor of one or more shock absorbers is described. The control system uses a fitness (performance) function that is based on the physical laws of minimum entropy. The control system uses a fuzzy neural network that is trained by a genetic analyzer. The genetic analyzer uses a fitness function that maximizes information while minimizing entropy production. The fitness function uses a difference between the time differential of entropy from a control signal produced in a learning control module and the time differential of the entropy calculated by a model of the suspension system that uses the control signal as an input The entropy calculation is based on a dynamic model of an equation of motion for the suspension system such that the suspension system is treated as an open dynamic system.
    • 描述了通过控制一个或多个减震器的阻尼系数来优化车辆悬架系统的性能的控制系统。 控制系统使用基于最小熵的物理定律的适应度(性能)函数。 控制系统使用由遗传分析仪训练的模糊神经网络。 遗传分析仪使用最大化信息的适应度函数,同时最小化熵产生。 适应度函数使用来自学习控制模块中产生的控制信号的熵的时间差与通过使用控制信号作为输入的悬架系统的模型计算的熵的时间微分之差。熵计算基于 在悬架系统的运动方程的动态模型上,使得悬架系统被视为开放动态系统。
    • 4. 发明申请
    • Intelligent electronically-controlled suspension system based on soft computing optimizer
    • 基于软计算优化器的智能电子控制悬架系统
    • US20060293817A1
    • 2006-12-28
    • US11159830
    • 2005-06-23
    • Takahide HagiwaraSergei PanfilovSergei Ulyanov
    • Takahide HagiwaraSergei PanfilovSergei Ulyanov
    • B60G17/018G06F17/00
    • B60G17/018B60G17/0152B60G2500/10B60G2600/187B60G2600/1879
    • A Soft Computing (SC) optimizer for designing a Knowledge Base (KB) to be used in a control system for controlling a suspension system is described. The SC optimizer includes a fuzzy inference engine based on a Fuzzy Neural Network (FNN). The SC Optimizer provides Fuzzy Inference System (FIS) structure selection, FIS structure optimization method selection, and teaching signal selection and generation. The user selects a fuzzy model, including one or more of: the number of input and/or output variables; the type of fuzzy inference model (e.g., Mamdani, Sugeno, Tsukamoto, etc.); and the preliminary type of membership functions. A Genetic Algorithm (GA) is used to optimize linguistic variable parameters and the input-output training patterns. A GA is also used to optimize the rule base, using the fuzzy model, optimal linguistic variable parameters, and a teaching signal. The GA produces a near-optimal FNN. The near-optimal FNN can be improved using classical derivative-based optimization procedures. The FIS structure found by the GA is optimized with a fitness function based on a response of the actual suspension system model of the controlled suspension system. The SC optimizer produces a robust KB that is typically smaller that the KB produced by prior art methods.
    • 描述了用于设计用于控制悬架系统的控制系统中的知识库(KB)的软计算(SC)优化器。 SC优化器包括基于模糊神经网络(FNN)的模糊推理机。 SC优化器提供模糊推理系统(FIS)结构选择,FIS结构优化方法选择和教学信号选择与生成。 用户选择模糊模型,包括以下一个或多个:输入和/或输出变量的数量; 模糊推理模型的类型(例如,Mamdani,Sugeno,Tsukamoto等); 和初步类型的会员职能。 遗传算法(GA)用于优化语言变量参数和输入 - 输出训练模式。 GA也用于优化规则库,使用模糊模型,最优语言变量参数和教学信号。 GA产生近乎最佳的FNN。 可以使用经典的基于导数的优化程序来改进近似最优的FNN。 GA发现的FIS结构通过基于受控悬架系统的实际悬架系统模型的响应的适应度函数进行了优化。 SC优化器产生通常比现有技术方法产生的KB更小的鲁棒KB。