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    • 68. 发明授权
    • Control system for distributed sensors and actuators
    • 分布式传感器和执行器控制系统
    • US5365423A
    • 1994-11-15
    • US818008
    • 1992-01-08
    • Sujeet Chand
    • Sujeet Chand
    • G01M99/00G05B13/04G05B19/042G05B19/048G05B19/05G05B23/02G06F15/46
    • G05B19/054G05B19/0423G05B2219/24075G05B2219/25235G05B2219/2621
    • Conventional Boolean Logic Control is augmented to provide enhanced diagnostics, monitoring, and fail safe operation for dynamic systems having distributed discrete-valued sensors and actuators. A decentralized model of a controlled system defines behavior and timing models for both sensors and actuators, termed Control Elements (CEs). Each CE has a first model for transition from state 0 to 1, and a second model for transition from state 1 to 0. Each behavioral model is defined by an Event Signature comprising a sequence of state changes in neighboring CEs. A continuous evaluation of event signatures is performed to compute a probability that a given CE will change state. An Expectation Function is used to check and enforce the correct behavior of a CE. A statistical temporal model predicts delays in the states of a CE as a function of its previous and current delays. The distributed behavior and on-line timing models are used to detect and diagnose incorrect behavior and failures of decentralized sensors and actuators.
    • 增强了传统布尔逻辑控制,为具有分布式离散值传感器和执行器的动态系统提供增强的诊断,监控和故障安全操作。 受控系统的分散模型定义了称为控制元件(CE)的传感器和执行器的行为和时序模型。 每个CE具有用于从状态0转换到1的第一模型,以及用于从状态1转换到0的第二模型。每个行为模型由包括相邻CE中的状态变化序列的事件签名来定义。 执行事件签名的连续评估以计算给定CE将改变状态的概率。 期望功能用于检查和执行CE的正确行为。 统计时间模型预测CE的状态的延迟是其先前和当前延迟的函数。 分布式行为和在线定时模型用于检测和诊断分散式传感器和执行器的不正确行为和故障。
    • 69. 发明授权
    • Self-monitoring tuner for feedback controller
    • 用于反馈控制器的自监控调谐器
    • US5159547A
    • 1992-10-27
    • US598488
    • 1990-10-16
    • Sujeet Chand
    • Sujeet Chand
    • G05B13/02G05B13/04
    • G05B13/0275Y10S706/90Y10S706/906
    • An automatic tuner is provided for continuous, on-line tuning of proportional, integral, and derivative (PID) feedback controllers. The tuner compares the system input signals with the system response to generate estimates of system damping, frequency, and steady-state error, and then applies a set of if-then rules derived from mathematical and/or empirical analysis of the system parameters. The tuner represents each parameter of the rule-set by a "fuzzy" membership function, and an inference mechanism uses fuzzy logic for computing system outputs from the input values. The tuner also incorporates a self-monitoring mechanism to adjust the tuner output based on past performance. At each running cycle, the tuner computes a Euclidean distance between present values and desired values in the parameter-space represented by damping, frequency, and steady-state error. The output of the tuner is then scaled by a "reward factor" that is a function of the tuner effectiveness and consistency. If tuner effectiveness degrades, the self-monitoring mechanism diminishes the output of the tuner. If the tuner is operating with consistency, the self-monitoring mechanism increases the output of the tuner. Thus, the self-monitoring mechanism improves performance and robustness of the tuner by scaling the output to produce faster slew rates when the tuner is performing well and by reducing tuner output when performance is poor.