The paper addresses the problem of robust decentralized control design which guarantee exponential stability of uncertain linear complex systems with output feedback. No matching conditions are demanded for modelling ...
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The paper addresses the problem of robust decentralized control design which guarantee exponential stability of uncertain linear complex systems with output feedback. No matching conditions are demanded for modelling the uncertain system, where the uncertainties, which are possibly nonlinear, may appear in the subsystems as well as in the interconnections.
This paper studies the problem of an H/sub /spl infin//-norm and variance-constrained state estimator design for uncertain linear discrete-time systems. The system under consideration is subjected to time-invariant no...
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This paper studies the problem of an H/sub /spl infin//-norm and variance-constrained state estimator design for uncertain linear discrete-time systems. The system under consideration is subjected to time-invariant norm-bounded parameter uncertainties in both the state and measurement matrices. The problem addressed is the design of a gain-scheduled linear state estimator such that, for all admissible measurable uncertainties, the variance of the estimation error of each state is not more than the individual prespecified value, and the transfer function from disturbances to error state outputs satisfies the prespecified H/sub /spl infin//-norm upper bound constraint, simultaneously. The conditions for the existence of desired estimators are obtained in terms of matrix inequalities, and the explicit expression of these estimators is also derived. A numerical example is provided to demonstrate various aspects of theoretical results.
An adaptive pole placement controller with dual modification is applied to a dynamic plant which is described by an unknown nonlinear model. Stability of the suggested dual controller applied together with a robust ad...
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An adaptive pole placement controller with dual modification is applied to a dynamic plant which is described by an unknown nonlinear model. Stability of the suggested dual controller applied together with a robust adaptation scheme is proved for this system. It is demonstrated that after the insertion of the dual controller the robust adaptive system remains stable. However, some known assumptions about the unmodeled residuals of the nonlinear plant model should be modified. It is also demonstrated that in the case of no residuals in the plant model the closed-loop system converges to the model defined by the desired pole positioning.
The paper presents an application-oriented robust decentralized control design technique based upon the sufficient condition for robust stability (SCRS) of systems under decentralized control (DC) which is fulfilled u...
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The paper presents an application-oriented robust decentralized control design technique based upon the sufficient condition for robust stability (SCRS) of systems under decentralized control (DC) which is fulfilled using parameter tuning of fixed-structure local controllers. Graphical interpretation of the SCRS provides two useful stability criteria used to test stability of the overall system as well as stability of subsystems. Theoretical results are illustrated in a case study.
In this paper is presented a new approach to robust non-linear control design, which can guarantee a prescribed decay rate of exponential stability for known system uncertainties. The proposed approach doesn’t employ...
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In this paper is presented a new approach to robust non-linear control design, which can guarantee a prescribed decay rate of exponential stability for known system uncertainties. The proposed approach doesn’t employ matching conditions. The non-linear power system with deterministic uncertainties is chosen as demonstration example.
As an alternative to the defuzzification a new computation method of fuzzy logic controller output is presented. In connection with this method an other procedure is proposed, which allowes to substitute also for fuzz...
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As an alternative to the defuzzification a new computation method of fuzzy logic controller output is presented. In connection with this method an other procedure is proposed, which allowes to substitute also for fuzzification. It uses the same knowledge base in the form of a rule-table like the fuzzy logic controller, but without using the fuzzy logic. The proposed algorithm is simple and the results which where verified in simulation and in the real-time control as well are the same like with fuzzy logic controllers.
The aim of the work is to present the state-of-the-art in this filed of adaptive control. Some development trends based especially on the general stability theory are analyzed. The characteristics of basic model refer...
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The aim of the work is to present the state-of-the-art in this filed of adaptive control. Some development trends based especially on the general stability theory are analyzed. The characteristics of basic model reference adaptive control (MRAC) structures and algorithms are given in order to provide an insight into the development of the theory and to indicate the possibilities of their practical application. As the paper makes only a survey of the subject area, the stability proofs of the presented algorithms are omitted. The plant to be controlled is assumed to be described by a linear model.
In this paper an application of the H ∞ -optimal position control of a hydraulic drive is presented. The treatment of model uncertainties in the field of robust control is demonstrated and an analysis for the two mos...
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In this paper an application of the H ∞ -optimal position control of a hydraulic drive is presented. The treatment of model uncertainties in the field of robust control is demonstrated and an analysis for the two most important kinds of uncertainties is presented. Main goal is to portray the relation between the weighting functions and the performance and robustness of the controlled system. On the basis of this detailed analysis, a set of simple tuning rules for the appropriate selection of the weighting functions is derived. A software package for the analysis of the plant and the selection of the weighting functions has been developed and successfully applied to a hydraulic drive.
In this paper the problem of modeling the Laguerre ladder network in the discrete-time domain is addressed. New algorithm of computing the discrete state-space model of the Laguerre ladder network, which reduces requi...
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In this paper the problem of modeling the Laguerre ladder network in the discrete-time domain is addressed. New algorithm of computing the discrete state-space model of the Laguerre ladder network, which reduces requirements on computer memory and provides a simple applicability to computer systems, is proposed. A receding horizon LQ regulator based on Laguerre function model is also presented.
Incipient faults and changes in the structure of any industrial process may be detected and known by their effects: vibration and/or acoustic signals. We consider some methods for pre-processing the acoustic signal, g...
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Incipient faults and changes in the structure of any industrial process may be detected and known by their effects: vibration and/or acoustic signals. We consider some methods for pre-processing the acoustic signal, generated by a rolling mill process, for fault detection and structural classification. The pre-processing methods are based on artificial neural networks. The methods refer to: signal decomposition algorithms, a distances measure for spectral amplitude classification and neural network structures for spectrum compression. For the signal decomposition problem an adaptive neural network algorithm is proposed in which the number of inputs is adapted to the imposed error. When the training error for two successive steps is very little, then the number of inputs in network is increased. If the spectral components are zero for sufficient time, then the number of inputs is decreased. The Hausdorff distance is proposed for spectrum classification as the distance measure for the frequency domain in a pattern recognition context. It shown that the Hausdorff distance has a monotone relationship with the signal-to-noise-ratio. Finally, the possibility of decreasing the number of spectrum components as patterns is presented, by compression with neural networks. Spectral representations of the acoustic source show that signatures collected at rolling mill sensor locations can be successfully used to identify process and structural changes in the rolling mill monitoring system. The results obtained by simulation is encouraging for real-time implementation.
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