Fuzzy-Petri-Net Reasoning Supervisory Controller and Estimating States of Markov Chain Models

Markov chain models are efficient tools for representing stochastic discrete event processes with wide applications in decision and control. A novel approach to fuzzy-Petri-net reasoning generated solution to initial or another state in Markov-chain models is proposed. Reasoning is performed by a fuzzy-Petri-net supervisory controller employing a fuzzy-rule production system design and a fuzzy-Petri-net reasoning algorithm, which has been developed and implemented in C++. The reasoning algorithm implements calculation of the degrees of fulfilment for all the rules and their appropriate assignment to places of Petri net representation structure. The reasoning process involves firing active transitions and calculating degrees of fulfilment for the output places, which represent propositions in the knowledge base, and determining of fuzzy-distributions for output variables as well as their defuzzified values. Finally, these values are transferred to assign the state of Markov-chain decision model in terms of transition probabilities

Genelleştirilmiş birbirine bağlı benzer sistemler: dağıtılmış çıkış takip kontrolü

Bu çalışmada yapısında benzerlikler taşıyan genelleştirilmiş birbiriyle bağlantılı sistemlerin dağıtılmış takip kontrolü, doğrusal tanımlayıcı modeller yardımıyla incelenmiş ve çözülmüştür. Bağlantıların ve alt sistemlerin doğrusal olmadığı ve yapısal belirsizliklere sahip olduğu kabul edilmiştir. Tasarlanan dayanıklı kontrolörlerle, birbirine bağlı alt sistemlerden oluşan bu sistem referansı asimptotik olarak izler, ayrıca kontrol edilen sistemde darbe etkisi görülmez. Takip kontrolörlerin yapıları birbirlerine benzerlik gösterirler, bu sayede gerçeklemesi, yok edilmesi ve tekrar üretilmesi oldukça kolaydır. Bu çalışmanın sonucu, genelleştirilmiş sistemlerin benzerliği varsayımı üzerine kurulmuştur

A memory ann computing structure for nonlinear systems emulation identification

Currently, almost all efforts for using artificial neural networks for control oriented process identification are based on feed-forward networks. Provided the system order or the upper limit of the order is known, a neural network design is feasible for which all the collection of previous values of the inputs and outputs of the system to be identified can be used as input data to train in the network computing structures to learn the input-output map. This work reports on a novel technique that makes use of memory artificial neural network architecture that can learn and transform so as to emulate any non-linear input-output map for multi-input-multi-output systems when no prior knowledge on specific system features exists

Exponential synchronization of complex delayed dynamical networks with general topology

The global exponential synchronization of complex delayed dynamical networks possessing general topology is investigated in this contribution. The network model considered can represent both the directed and undirected weighted networks. Novel delay-dependent linear controllers are designed via Lyapunov stability theory and appropriate property of the coupling matrix. It is shown that the controlled network is globally exponentially synchronized with a given convergence rate. Two examples of typical dynamical networks with coupling delays of this class, one possesses directed and the other with undirected coupling topology, both having a Lorenz system at each node, have been used to demonstrate and verify the novel control design proposed. © 2007 IEEE.published_or_final_versio

Robust control of SVC: A new adaptive backstepping method

Dimirovski, Georgi M. (Dogus Author)A new extended adaptive backstepping algorithm for general nonlinear systems in parametric feedback form is proposed. The method preserves useful nonlinearities and the real-time estimation of uncertainty parameter, and does not follow the classical certainty equivalence principle. Then the proposed method is applied to nonlinear adaptive control of Static VAR Compensator (SVC) of a single-machine infinite-bus system containing some unknown parameters. This novel adaptation mechanism is introduced into power systems, and a novel adaptive control law for this SMIB system is presented. The simulation results demonstrate that the proposed method is better than the design based on classical adaptive backstepping in terms of properties of stability and parameter estimation. Hence, it will be an alternative to practice engineering and applications. In addition, this algorithm is applicable to other control systems.National Natural Science Foundation of China (60574013，60274009

Comparative analysis of Kolmogorov ANN and process characteristic input-output modes

In the past decades, representation models of dynamical processes have been developed via both traditional math-analytical and less traditional computational-intelligence approaches. This challenge to system sciences goes on because essentially involves the mathematical approximation theory. A comparison study based on cybernetic input-output view in the time domain on complex dynamical processes has been carried out. An analytical decomposition representation of complex multi-input-multi-output thermal processes is set relative to the neural-network approximation representations, and shown that theoretical background of both emanates from Kolmogorov's theorem. The findings provided a new insight as well as highlighted the efficiency and robustness of fairly simple industrial digital controls, designed and implemented in the past, inherited from input-output decomposition model approximation employed

Time series in forecasting and decision: an experiment in elman nn models

The paper examines the role of analytical tools in analysis of economic statistical data (commonly referred to as econometry) and artificial neural network (ANN) models for time series processing in forecasting, decision and control. The emphasis is put on the comparative analysis of classical econometric approach of pattern recognition (Box-Jenkins approach) and neural network models, especially the class of recurrent ones and Elman ANN in particular. A comprehensive experiment in applying the latter modeling has been carried out, some specific applications software developed, and a number of benchmark series from the literature processed. This paper reports on comparison findings in favor of Elman ANN modeling, and on the use of a designed program package that encompasses routines for regression, ARIMA and ANN analysis of time series. The analysis is illustrated by two sample examples known as difficult to model via any technique

Model reference adaptive integral sliding mode control for switched delay systems

Makalenin ilk sayfası kayda eklenmiştir.This paper proposes a strategy of model reference adaptive integral sliding mode variable structure control to solve the tracking problem for a class of uncertain switched systems with time-varying delay. A stable integral sliding surface is first constructed. An adaptive control technique is used to adapt the unknown upper bounds of perturbations. Furthermore, adaptive variable structure controllers are employed such that the switched delay system containing perturbations with unknown upper bounds tracks the reference model under arbitrary switching signals. Finally, a numerical example is given to illustrate the effectiveness of the proposed design method

Reliable H∞ control for a class of switched nonlinear systems

Dimirovski, Georgi M. (Dogus Author)This paper focuses on the problem of reliable H∞ control for a class of switched nonlinear systems with actuator failures among a prespecified subset of actuators. In existing works, the reliable H∞ design methods are all based on a basic assumption that the never failed actuators must stabilize the given system. But when actuators suffer ”serious failure”– the never failed actuators can not stabilize the given system, the standard design methods of reliable H∞ control do not work. Based on the switching technique, the problem can be solved by means of switching among subsystems or finite candidate controllers.16th Triennial World Congress of International, Federation of Automatic Control, IFAC 200

Global robust H∞ control for non-minimum-phase uncertain nonlinear systems without strict triangular structure

Dimirovski, Georgi M. (Dogus Author) -- 16th Triennial World Congress of International Federation of Automatic Control, IFAC 2005; Prague; Czech Republic; 3 July 2005 through 8 July 2005; Code 85501This paper deals with the robust H∞ control problem for a class of multi-input non-minimum-phase nonlinear systems with parameter uncertainty. A system of this class is assumed to be in a special interlaced form, which includes a strict triangular form as a special case. By using an extension of backstepping, nonlinear static-state feedback controllers are designed such that the closed-loop system is input-to-state stable with respect to the disturbance input and has the prescribed L2-gain from the disturbance input to the controlled output for all admissible parameter uncertainties.This work was supported by the NSF of China under Grant 60274009, by SRFDP under Grant 20020145007 and by NSF of Liaoning Province under Grant 20032020