Boots – shoes – and leggings
Patent
1992-10-08
1994-09-13
Smith, Jerry
Boots, shoes, and leggings
364153, 364164, G05B 1304, G05B 1302
Patent
active
053474465
DESCRIPTION:
BRIEF SUMMARY
TECHNICAL FIELD
This invention relates to a model predictive control apparatus adapted for predicting future movement or trend of a control response on the basis of a dynamic characteristic model of a controlled system to calculate manipulated variables while taking such a prediction in consideration, and an input device for a model predictive control, which is adapted to carry out setting of the operation condition, manipulated variables, or the like at this model predictive control apparatus.
BACKGROUND ART
In recent years, in the process control system, there have been frequently used model predictive control technologies to construct a linear discrete time model on the basis of an impulse response or a step response of the process in order to conduct optimal control/operation while satisfying a large number of limits imposed on the process (plant) to calculate in sequence, from a predictive equation or formula derived from this model, such an optimal manipulated variables to minimize an cost function in quadratic form relating to deviations from reference or objective values of controlled variable's future values and manipulated variable's future values.
These technologies aim at determining manipulated variables applied at the present time point in order that a controlled variable future value to follow a reference value trajectory as close as possible is provided under a necessary minimum change in the manipulated variable.
Such model predictive control apparatus has merits as recited below. time can be realized, a future reference value, multi-variable control system as well, controlled system, it is possible to easily design a control system from a step response, for example. system (process) into the predictive model, fine control can be expected, operation of a controlled system (e.g., upper and lower limiters, change rate limiters, etc.) into control rule, and the like.
Until now, various predictive control systems have been proposed. These systems are explained, e.g., in the following literatures. Control (Japanese) Vol. 28, No. 11, pp. 996-1004 (1989), and Control, Automatica 25-6 pp. 859 (1989), etc. Particularly, in the literature (2), a Generalized Predictive Control (GPC) including various model predictive control systems has been proposed. In accordance with this control system, when a future reference value y* is given, a control response future value y(k+i)(i=1, . . . , Np) is predicted on the basis of a model of a controlled system (process) to calculate or determine a manipulated variable increment .DELTA.u(K) which minimizes a control cost function indicating control performance: ##EQU1## where parameter L is a prediction starting time, parameter Np is a prediction horizon, parameter Nu is a control horizon, parameter .lambda. is a weighting factor or coefficient, and D(Z.sup.-1) is a pole assignment polynomial.
In this control technology, since a predictive equation for determining future values of controlled variables or manipulated variables is represented by a function relating to past controlled variables or manipulated variables, prediction is carried out every time on the basis of past controlled variables or manipulated variables so that a controlled variable's future value becomes closer to a corresponding reference or objective value to determine a manipulated value applied at that time point. At this time, it is necessary to determine manipulated variables in order to satisfy the limit conditions relating to controlled variables/manipulated variables.
Meanwhile, as a general method of determining an optimal solution to minimize an cost function in a quadratic form while satisfying the limit conditions or constraints, there is a quadratic programming (QP) (With respect to QP, see Konno and Yamashita "Non-linear Programming" (Nikka Giren), Sekine "Mathematical Programming" (Iwanani Shoten), etc.). In order to use this QP, the cost function and the constraints must relate to only manipulated variables which are a parameter to be optimized.
However, as the constraints impo
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Iino Yutaka
Ohya Junko
Kabushiki Kaisha Toshiba
Smith Jerry
Trammell Jim
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