WO1996009573A2 - Dispositif pour la commande adaptative d'une section - Google Patents

Dispositif pour la commande adaptative d'une section Download PDF

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Publication number
WO1996009573A2
WO1996009573A2 PCT/DE1995/001223 DE9501223W WO9609573A2 WO 1996009573 A2 WO1996009573 A2 WO 1996009573A2 DE 9501223 W DE9501223 W DE 9501223W WO 9609573 A2 WO9609573 A2 WO 9609573A2
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WO
WIPO (PCT)
Prior art keywords
controller
parameters
neural network
route
control
Prior art date
Application number
PCT/DE1995/001223
Other languages
German (de)
English (en)
Other versions
WO1996009573A3 (fr
Inventor
Hans-Peter Preuss
Original Assignee
Siemens Aktiengesellschaft
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Siemens Aktiengesellschaft filed Critical Siemens Aktiengesellschaft
Publication of WO1996009573A2 publication Critical patent/WO1996009573A2/fr
Publication of WO1996009573A3 publication Critical patent/WO1996009573A3/fr

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Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only

Definitions

  • the invention relates to a device for adaptive control of a route according to the preamble of claim 1.
  • a control loop there consists of a comparator which forms a control difference from a comparison of a control variable measured on the route with a control variable, and a controller which determines a control variable for the route as a function of the control difference.
  • the basis of the PID controller parameterization is a step response of the controlled system, from which sampled values are obtained and stored at an appropriate time interval.
  • the measured curve is approximated by a special model approach, a PT n model, in order to be able to use a controller setting method tailored to it, the optimum amount.
  • the invention has for its object to provide a device for adaptive control of a route in which a model of the route can be dispensed with and which nevertheless ensures good control behavior.
  • the new device has the Merl ⁇ nal mentioned in the characterizing part of claim 1.
  • Advantageous further developments are given in the subclaims.
  • the invention has the advantage that it is possible with the neural network, directly from the discrete samples of the
  • Step response of the route as input variables is a suitable parameter, for example a PID controller, as an output large to calculate.
  • the neural network can be implemented on comparatively simple hardware, since its use only requires the creation of the sample values of the step change response serving as input variables and the one-off calculation of the neural network. Because of its generalization properties, the network immediately supplies suitable parameters of the controller for a largely arbitrary step response.
  • FIG. 1 shows a block diagram of a device according to the invention for adaptive control
  • FIG. 2 shows a basic illustration of a signal preprocessing and a neural network.
  • an adaptive control loop consists of a comparator 1, a controller 2, a system 3, a signal preprocessing 4 and a neural network 5.
  • a control variable x measured on the control system 3 is compared with a Reference variable w compared and thus a control difference xd is formed.
  • the course of the controlled variable x and the manipulated variable y is sampled and stored in the signal preprocessing 4.
  • the neural network 5 calculates an assignment of the parameters Kp, Tn and Tv of the PID controller 2 that is suitable for the route 3.
  • a step in the manipulated variable is advantageously applied.
  • any time course of the manipulated variable y is possible as an excitation of the route 3, which leads to a course of the controlled variable x which is characteristic of the route behavior. If a calculation of the controller parameters by the neural network 5 is to be possible for different courses of the manipulated variable y, in addition to samples of the course of the controlled variable x, samples of the manipulated variable y must also be supplied to the neural network 5 by the signal preprocessing 4.
  • the sampling time is fixed in the signal preprocessing 4. If, on the other hand, it is to be kept variable, it is required for the controller parameter calculation and must be passed on to the neural network 5 as an additional input variable.
  • a signal PI / PID is present at an input of a neural network 6 and is used to select the controller type. Further input signals are the discrete sample values of the controlled variable x obtained in signal preprocessing, which are obtained from the course of the step response, which is shown in a time diagram. With these input variables, the neural network 6 directly calculates the parameters Kp, Tn and Tv according to the selected controller type.
  • Multi-dimensional, non-linear relationships can, as is known for example from WO 94/06095, be modeled by artificial neural networks.
  • Such multi-dimensional, non-linear relationships the knowledge of which is the solution to the controller design problem, also exist between the sample values of the step response and the optimal ones Controller parameters of an associated PID controller.
  • These relationships can only be stated in analytical form in very special cases.
  • the setting rules for the optimum amount are such a special case.
  • PT n _ systems it provides rational functions with the parameters gain, K, time constant T and order n of the system as setting instructions for the parameters Kp, Tn and Tv of a PID controller, which are specified in the article mentioned at the beginning. In general spelling they read:
  • Kp f x (n, K, T)
  • Tn f2 (n, T)
  • Tv f 3 (n, T).
  • Kp g 1 (x 1 , ...,, ⁇ t),
  • An MLP (Multi Layer Perceptron) network is a neural network with ten inputs for the base values of the step response, one input for the time increment ⁇ t, possibly one input for the switching signal PI / PID and one output each for Kp, Tn, Tv suitable.
  • the sample values of the step responses of various analytical route models are first calculated and the controller parameters based on the known setting rules (e.g. optimum amount for PT n - Routes). Further learning data are obtained by control loop simulation and numerical optimization of the controller parameters.
  • the neural network is then trained with the learning data obtained in this way. The neural network therefore delivers optimal results for this data. It is then recommended to test the generalization properties of the network on lines for which no controller parameters were available as training data.
  • the result is a trained neural network, the internal parameters of which are fully known. It can therefore be implemented on comparatively simple hardware, since high computing power is only required for the learning process.
  • the use of the neural network in the control loop merely requires the creation of the sample values of the step change response serving as input variables and the one-off calculation of the network. Without any further computation-intensive optimization, the network immediately delivers good controller parameters that are optimal at the learned base values for any step response. A model of the route is no longer required for the actual adaptation.

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  • Engineering & Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Health & Medical Sciences (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Feedback Control In General (AREA)

Abstract

L'invention concerne un dispositif pour la commande adaptative d'une section (3), comprenant un système de prétraitement de signal (4), au moyen duquel, à partir de la variation, dans le temps, de la grandeur contrôlée (x), des valeurs échantillonnées (a) peuvent être produites, et un réseau neuronal (5) calculant les paramètres (Kp, Tn, Tv) de l'unité de régulation (2) en fonction des valeurs échantillonnées (a). Le réseau neuronal (5) est entraîné au moyen des données apprises des paramètres (Kp, Tn, Tv) et des valeurs échantillonnées (a) de la réponse transitoire de section qui sont générées par des régulateurs de paramètres calculés analytiquement ou par une simulation de circuit de commande ou une optimisation numérique pour un ou plusieurs modèles de la section. Le réseau neuronal (5) fournit ainsi aux données apprises, des paramètres de contrôle optimum qui sont bien appropriés aux valeurs intermédiaires en raison de ses propriétés de généralisation. L'invention est appliquée dans la technologie d'automatisation.
PCT/DE1995/001223 1994-09-19 1995-09-07 Dispositif pour la commande adaptative d'une section WO1996009573A2 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE19944433332 DE4433332A1 (de) 1994-09-19 1994-09-19 Einrichtung zur adaptiven Regelung einer Strecke
DEP4433332.3 1994-09-19

Publications (2)

Publication Number Publication Date
WO1996009573A2 true WO1996009573A2 (fr) 1996-03-28
WO1996009573A3 WO1996009573A3 (fr) 1996-05-30

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Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/DE1995/001223 WO1996009573A2 (fr) 1994-09-19 1995-09-07 Dispositif pour la commande adaptative d'une section

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DE (1) DE4433332A1 (fr)
WO (1) WO1996009573A2 (fr)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5847952A (en) * 1996-06-28 1998-12-08 Honeywell Inc. Nonlinear-approximator-based automatic tuner
DE10302585B4 (de) * 2003-01-22 2004-12-30 Endress + Hauser Wetzer Gmbh + Co Kg Verfahren zum Einstellen eines Reglers
EP3825788B1 (fr) * 2019-11-19 2022-11-09 Asco Numatics GmbH Dispositif de réglage, système de réglage et procédé de réglage d'une grandeur physique d'un fluide

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1993012476A1 (fr) * 1991-12-18 1993-06-24 Honeywell Inc. Syntoniseur automatique a reseau neuronal en boucle fermee

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH03201008A (ja) * 1989-12-28 1991-09-02 Toshiba Corp ゲインスケジューリング制御装置

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1993012476A1 (fr) * 1991-12-18 1993-06-24 Honeywell Inc. Syntoniseur automatique a reseau neuronal en boucle fermee

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
PATENT ABSTRACTS OF JAPAN vol. 015 no. 469 (P-1281) ,27.November 1991 & JP,A,03 201008 (TOSHIBA CORP) 2.September 1991, *
PROCEEDINGS OF THE AMERICAN CONTROL CONFERENCE, SAN DIEGO, MAY 23 - 25, 1990, Bd. 3, 23.Mai 1990 INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS, Seiten 3023-3024, XP 000170168 SWINIARSKI R W 'NOVEL NEURAL NETWORK BASED SELF-TUNING PID CONTROLLER WHICH USES PATTERN RECOGNITION TECHNIQUE' *

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DE4433332A1 (de) 1996-03-21
WO1996009573A3 (fr) 1996-05-30

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