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公开(公告)号：EP1498791B1
公开(公告)日：2007-06-20
申请号：EP04254226.6
申请日：2004-07-14
发明人： Fuller, James W.
CPC分类号： G06F17/16 , G05B13/048
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2.发明公开Increased bandwidth for plants with resonant modes using nonlinear notch filters 失效增加了对使用非线性的陷波滤波器具有共振模式的设备的带宽
公开(公告)号：EP0817377A3
公开(公告)日：1998-08-26
申请号：EP97110425.2
申请日：1997-06-25
发明人： Fuller, James W. , Nett, Carl N.
IPC分类号： H03H11/04
CPC分类号： G05B5/01
摘要： A control system includes a plant 56 having resonant modes and a controller 5 which receives plant input signals on lines 14,24 from the plant 56 and provides a controller output signal I, related to a filter output signal V, which controls the plant 6. The controller is provided with a non-linear notch filter 30 which receives a filter input signal x related to the plant input signals and provides the filter output signal V. The notch filter has at least one notch frequency near one of the resonant modes so as to attenuate one of the modes by a predetermined amount and has a phase lag a decade below the lowest notch frequency of said notch filter which is less than that of a corresponding linear notch filter, thereby allowing the control system 7 to exhibit faster time response and increased bandwidth than a system employing linear notch filters.
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公开(公告)号：EP1434116B1
公开(公告)日：2009-03-11
申请号：EP03257314.9
申请日：2003-11-19
发明人： Fuller, James W. , Das, Indraneel
IPC分类号： G05B19/048 , G05B13/04
CPC分类号： G05B13/042
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公开(公告)号：EP1498791A1
公开(公告)日：2005-01-19
申请号：EP04254226.6
申请日：2004-07-14
发明人： Fuller, James W.
CPC分类号： G06F17/16 , G05B13/048
摘要： An efficient method for solving a model predictive control problem is described. A large sparse matrix equation is formed based upon the model predictive control problem. The square root of H, Hr, is then formed directly, without first forming H. A square root (LSMroot) of a large sparse matrix of the large sparse matrix equation is then formed using Hr in each of a plurality of iterations of a quadratic programming solver, without first forming the large sparse matrix and without recalculating Hr in each of the plurality of iterations. The solution of the large sparse matrix equation is completed based upon LSMroot.
摘要翻译： 描述了一种解决模型预测控制问题的有效方法。 基于模型预测控制问题形成了一个大的稀疏矩阵方程。 然后直接形成H，Hr的平方根，而不首先形成H。然后使用Hr在二次方程的多个迭代中的每一个中形成大稀疏矩阵方程的大稀疏矩阵的平方根（LSMroot） 编程求解器，而不首先形成大的稀疏矩阵，并且在多个迭代中的每一个中不重新计算Hr。 大稀疏矩阵方程的解是基于LSMroot完成的。
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公开(公告)号：EP2428859B1
公开(公告)日：2016-11-02
申请号：EP11250795.9
申请日：2011-09-14
CPC分类号： G05B13/00 , G05B13/048
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公开(公告)号：EP2492762A1
公开(公告)日：2012-08-29
申请号：EP12157261.4
申请日：2012-02-28
IPC分类号： G05B13/00
CPC分类号： G05B17/02
摘要： A method and system for controlling a multivariable system. The method includes: (a) generating bias data (56) as a function of model error (66) in an on-board model (38); (b) updating a dynamic inversion algorithm (48) with one or more model terms (58) generated by the on-board model (38); (c) generating effector equation data (60) by processing reference value data (54) with the updated dynamic inversion algorithm (48), which effector equation data (60) is indicative of one or more goal equations and one or more limit equations, and which reference value data (54) is indicative of one or more goal values and one or more limit values and is determined as a function of predicted parameter data (62); (d) at least partially adjusting at least one of the reference value data (54) and predicted parameter data (62) for the model error (66) using the bias data (56); and (e) generating one or more effector signals (30) by processing the effector equation data (60) with an optimization algorithm.
摘要翻译： 一种用于控制多变量系统，该方法包括方法和系统：在一个板上模型（38）（a）产生偏差数据（56）为模型误差（66）的函数; 通过车载模型（38）产生的（B）与一个或多个模型项（58）更新动态逆算法（48）; （C）产生的效应方程的数据（60）通过处理基准值数据（54）与更新后的动态反演算法（48）的效应方程的数据（60）是指示一个或多个目标方程和一个或多个限制方程， 并且其参考值数据（54）是指示一个或多个目标的值和一个或更多个限制值的和为确定性的开采作为预测的参数的数据（62）的功能; （D）至少部分地调节所述参考值数据中的至少一个（54）和预测参数数据（62），用于模型误差（66），使用偏置数据（56）; 和（e）通过处理数据执行方程（60），在最优化算法生成一个或多个效应器信号（30）。
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公开(公告)号：EP2428859A2
公开(公告)日：2012-03-14
申请号：EP11250795.9
申请日：2011-09-14
IPC分类号： G05B13/00
CPC分类号： G05B13/00 , G05B13/048
摘要： A method for controlling a gas turbine engine (16) includes: generating model parameter data as a function of prediction error data, which model parameter data includes at least one model parameter that accounts for off-nominal operation of the engine; at least partially compensating an on-board model for the prediction error data using the model parameter data; generating model term data using the on-board model, wherein the on-board model includes at least one model term that accounts for the off-nominal operation of the engine; respectively updating one or more model parameters and one or more model terms of a model-based control algorithm with the model parameter data and model term data; and generating one or more effector signals using the model-based control algorithm.
摘要翻译： 一种用于控制燃气涡轮发动机（16）的方法包括：根据预测误差数据生成模型参数数据，该模型参数数据包括考虑到发动机的非标称运行的至少一个模型参数; 使用所述模型参数数据至少部分地补偿所述预测误差数据的车载模型; 使用所述车载模型生成模型项数据，其中所述车载模型包括考虑所述发动机的非标称操作的至少一个模型项; 分别用模型参数数据和模型项数据更新基于模型的控制算法的一个或多个模型参数和一个或多个模型项; 以及使用基于模型的控制算法产生一个或多个效应信号。
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公开(公告)号：EP2428859A3
公开(公告)日：2015-03-04
申请号：EP11250795.9
申请日：2011-09-14
CPC分类号： G05B13/00 , G05B13/048
摘要： A method for controlling a gas turbine engine (16) includes: generating model parameter data as a function of prediction error data, which model parameter data includes at least one model parameter that accounts for off-nominal operation of the engine; at least partially compensating an on-board model for the prediction error data using the model parameter data; generating model term data using the on-board model, wherein the on-board model includes at least one model term that accounts for the off-nominal operation of the engine; respectively updating one or more model parameters and one or more model terms of a model-based control algorithm with the model parameter data and model term data; and generating one or more effector signals using the model-based control algorithm.
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公开(公告)号：EP1434116A3
公开(公告)日：2005-06-22
申请号：EP03257314.9
申请日：2003-11-19
发明人： Fuller, James W. , Das, Indraneel
IPC分类号： G05B19/048 , G05B13/04
CPC分类号： G05B13/042
摘要： A method of controlling a multivariable system includes the step of receiving a plurality of sensor signals indicating current conditions of the system and receiving a plurality of commands. The desired dynamic response of the system is then determined based upon the commands and the sensor signals. The problem of controlling the system to achieve the desired dynamic response without violating numerous actuator and physical constraints is then formulated as a quadratic programming problem. By solving the quadratic programming problem, the effector commands are determined and the physical constraints are enforced.
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