In many control system design exercises which employ optimization, the bulk of the computational effort is devoted to the evaluation of the objectives of the optimization at each iteration, This paper demonstrates how...
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In many control system design exercises which employ optimization, the bulk of the computational effort is devoted to the evaluation of the objectives of the optimization at each iteration, This paper demonstrates how, using a new gateway, parallel processing can be used Within the framework of existing computer aided control system design tools to compute these objective values. In the first instance, a simple mapping of objectives to processors is employed. In the likely event that this yields a poorly balanced processor utilisation, this strategy is extended upon by using parallel processing in the evaluation of individual ohjective functions.
作者：
Völker, F.Dr.-Ing. Frank Völker (1958)
VDI studied mechanical engineering special subjects “powerengineering automatic control engineering and mechanical science” at the University-GH-Duisburg/Germany where he got his Dipl.-Ing. degree in 1984. From 1984 to 1990 he has been a scientific assistant at the department “Konstruktionslehre und Fördertechnik” special subject “computer simulation of the performance characteristics of driving systems” at the same University where he received his Dr.-Ing. degree in 1990. In 1990 he joined Klöckner-Moeller GmbH Bonn/Germany as research engineer at the department “Forschung”. Since 1992 he is manager of Energy Management Systems at this company. (Klöckner-Moeller GmbH Head Office Energy Management Systems Hein-Moeller-Str. 7-11 D-53115 Bonn T +49228/602 - 11 62 Fax +49228/602- 11 94)
A discrete thermal network is given as an equivalent circuit of the steady‐state thermal performance characteristics of circuit breakers. The immediate environment of the enclosure and the feeders of the unenclosed s...
Various SISO feedback control techniques have been applied successfully to muscle relaxant anaesthesia in simulations and clinical trials. SISO generalised predictive control (GPC) altogether with self-organising cont...
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Various SISO feedback control techniques have been applied successfully to muscle relaxant anaesthesia in simulations and clinical trials. SISO generalised predictive control (GPC) altogether with self-organising control using fuzzy logic theory (SOFLC) are among these techniques. A multivariable model combining muscle relaxation (paralysis) and anaesthesia (unconsciousness) has been identified. The multivariable version of GPC in its basic form as well as its different extensions to include model following and observer filter polynomials is outlined in addition to the multivariable version of SOFLC. Both of these strategies are applied to the previous model whose parameters were chosen according to a Monte-Carlo method. The robustness of both control strategies is investigated and the results presented and discussed, enabling a comparison to be made between self-adaptive and self-organising techniques. It is concluded that, when a detailed mathematical model structure is available, GPC provides better control than SOFLC.
The derivations of orthogonal least-squares algorithms based on the principle of Hsia's method and generalized least-squares are presented. Extensions to the case of non-linear stochastic systems are discussed and...
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The derivations of orthogonal least-squares algorithms based on the principle of Hsia's method and generalized least-squares are presented. Extensions to the case of non-linear stochastic systems are discussed and the performance of the algorithms is illustrated with the identification of both simulated systems and linear models of an electric arc furnace and a gas furnace.
Correlation-based model validity tests are introduced to monitor the operation of non-linear adaptive noise cancellation filters and to detect whether the filters are operating correctly or incorrectly. The tests are ...
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Correlation-based model validity tests are introduced to monitor the operation of non-linear adaptive noise cancellation filters and to detect whether the filters are operating correctly or incorrectly. The tests are derived for a NARMAX (Non-linear Autor-Regressive Moving Average model with eXogenous inputs) filter design based on a Sub-Optimal Least Squares (SOLS) estimation algorithm. Simulation studies are included to illustrate the performance of the new tests.
The second part of this paper describes a decentralized fuzzy controller structure for dealing with the multivariable control of human blood pressure. It consists of rule-based fuzzy controllers and a simple compensat...
The second part of this paper describes a decentralized fuzzy controller structure for dealing with the multivariable control of human blood pressure. It consists of rule-based fuzzy controllers and a simple compensator unit. The reasoning algorithms used by the fuzzy controllers are based on the unified approximate reasoning model derived by the authors in Part 1. The problem involves two cases: simultaneous control of the arterial pressure and systemic venous pressure, and simultaneous regulation of arterial pressure and cardiac output. Eight reasoning algorithms are chosen for comparisons which are based upon the control performance and the performance robustness. A number of simulation results show that the blood pressure can be controlled successfully by the proposed controller despite the presence of strong interactive effects between the variables. In addition, some useful conclusions about reasoning methods are drawn from the comparative studies.
Instead of seeking a structure mapping from a fuzzy reasoning system to a neural network, this paper is intended to find a functional mapping from a fuzzy logic-based algorithm to the network-based approach. By viewin...
Instead of seeking a structure mapping from a fuzzy reasoning system to a neural network, this paper is intended to find a functional mapping from a fuzzy logic-based algorithm to the network-based approach. By viewing the given rule-base as defining a global linguistic association constrained by fuzzy sets, approximate reasoning is implemented here by a Backpropagation Neural Network (BNN) with the aid of fuzzy set theory. By paying particular attention to the generalization capability of the BNN, the underlying principles have been examined in detail using two examples: a small demonstration at the linguistic level, and a more realistic problem of multivariable fuzzy control of blood pressure. The simulation results not only indicate the feasibility of the BNN-based approach, but also reveal some deeper similarities which exist in the two methods, which may have some important implications for future studies into fuzzy control. In addition, this work may be considered as another application example of the BNN in the case of continuous outputs and on a relatively larger scale (in the second example the BNN has 26 inputs and 13 outputs, with a total of 2013 weights and thresholds).
When designing a self-tuning controller for multivariable systems a proper representation of the model structure is important, particularly if the interactions between loops are significant. A popular transfer functio...
When designing a self-tuning controller for multivariable systems a proper representation of the model structure is important, particularly if the interactions between loops are significant. A popular transfer function structure used to describe multivariable processes is the P-canonical form structure where loop interactions are treated as feedforward couplings. However, polynomial-based controllers can also be applied to multivariable systems by designing several single-input single-output controllers, and compensation for cross-coupling between the different loops can be achieved by treating these interactions as feedforward measurable disturbances. This is the theme of this paper which considers the extension of the Generalized Predictive control algorithm (GPC) to this technique. Following a derivation of the control strategy, called Generalized Predictive control with Feedforward (GPCF), it is applied to a realistic nonlinear model for anaesthesia in a series of simulations. These results are compared with those obtained using the multivariable GPC version with a P-canonical form representation for the discrete multivariable model. The GPCF scheme is shown, in this case, to offer advantages over the multivariable GPC in terms of transient responses, interaction reduction, control quality, and computational burden.
The major roles which are the concern of a clinical anaesthetist are those of drug-induced unconsciousness, muscle relaxation, and analgesia (ie pain relief). The first two roles are concentrated in the operating thea...
ISBN:
(纸本)0780307852
The major roles which are the concern of a clinical anaesthetist are those of drug-induced unconsciousness, muscle relaxation, and analgesia (ie pain relief). The first two roles are concentrated in the operating theatre, whereas the third role is mainly concentrated in post-operative conditions. Each of these roles has been researched in recent years for the possibility of automated drug-infusion via feedback strategies, but only the former two will be considered in this paper.
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