Analysis of complex systems such as hierarchically structured network systems that are employed in modelling and analysis various aspects and issues of control and management in telecommunications is investigated. The...
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The paper addresses the design of a robust controller with output feedback for uncertain linear systems in the time domain. The necessary and sufficient conditions for static output feedback stabilizability of linear ...
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The paper addresses the design of a robust controller with output feedback for uncertain linear systems in the time domain. The necessary and sufficient conditions for static output feedback stabilizability of linear continuous and discrete time systems are the basis for the proposed robust controller design procedure. The proposed approach does not employ matching conditions.
The paper addresses the design of a robust controller with output feedback for parametric uncertain linear systems in the time domain. The necessary and sufficient conditions for static output feedback stabilizability...
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The paper addresses the design of a robust controller with output feedback for parametric uncertain linear systems in the time domain. The necessary and sufficient conditions for static output feedback stabilizability of linear continuous and discrete time systems are the basis for the proposed robust controller design procedure. The proposed approach does not employ matching conditions.
This paper presents an attractive artificial neural network architecture with orthonormal type of activation functions (e.g. harmonic, Legendre, Laguerre and Chebychev) for modeling and self-tuning control of nonlinea...
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This paper presents an attractive artificial neural network architecture with orthonormal type of activation functions (e.g. harmonic, Legendre, Laguerre and Chebychev) for modeling and self-tuning control of nonlinear processes. An algorithm for modeling and self-tuning control of uncertain nonlinear complex processes is proposed. Several types of orthonormal functions for nonlinear process modeling and control are analyzed and compared. Simulation results are provided to illustrate high performance of artificial neural networks with the orthonormal activation functions in practical industrial problems. The obtained results are compared and potential shortcomings of the proposed methods for real time industry control are discussed and analyzed.
Behaviour of a group of robots controlled by agent oriented programming. Group knowledge, one and multi-generational goals, individual and group experience, individual goals versus group mission, organization of exper...
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Behaviour of a group of robots controlled by agent oriented programming. Group knowledge, one and multi-generational goals, individual and group experience, individual goals versus group mission, organization of experience transmittion, multi-level model of the agents’ environment.
Necessary and sufficient conditions for a discrete-time system to be stabilizable via output feedback are established. The obtained conditions include a Riccati equation. An iterative algorithm for the computation of ...
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Necessary and sufficient conditions for a discrete-time system to be stabilizable via output feedback are established. The obtained conditions include a Riccati equation. An iterative algorithm for the computation of output stabilizing feedback gains is proposed in two alternatives. The results provide the discrete-time counterpart to the results by Kučera and deSouza, 1995.
This paper considers the problem of modeling dynamical system in the discrete-time domain using orthonormal series representation. It is shown that this modeling approach can bring some advantages to the robust identi...
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This paper considers the problem of modeling dynamical system in the discrete-time domain using orthonormal series representation. It is shown that this modeling approach can bring some advantages to the robust identification and to the control schemes. In requires a minimum a priori information and reduces requirements on computer algorithm design. The experimental results for the robust predictive control method based on Laguerre orthonormal functions for a synchronous motor is included.
This paper presents a novel methodology for modeling and control of nonlinear processes using artificial neural network with orthonormal activation functions (e.g. harmonic, Legendre, Laguerre and Chebychev). An algor...
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This paper presents a novel methodology for modeling and control of nonlinear processes using artificial neural network with orthonormal activation functions (e.g. harmonic, Legendre, Laguerre and Chebychev). An algorithm for modeling and self-tuning control of uncertain nonlinear complex processes is proposed. Several types of orthonormal functions for nonlinear process modeling and control are analyzed and compared. Simulation results are provided to illustrate high perfonnance of artificial neural networks with the orthonormal activation functions in practical industrial problems. The obtained results are reviewed and potential shortcomings of the analyzed methods are discussed.
In this paper the concept and implementation ofan Intelligent Motion controller for a Brushless D.C. Motor are presented. Fuzzy logic control and fuzzy reasoning tuning procedures are used for speed and current contro...
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In this paper the concept and implementation ofan Intelligent Motion controller for a Brushless D.C. Motor are presented. Fuzzy logic control and fuzzy reasoning tuning procedures are used for speed and current controllers respectively. A digital signal processor (DSP) based hardware set-up TMS320F240 and a specialized software environment are used to laboratory experiments. Actual experimental results proved the effectiveness of the proposed intelligent motion controller.
The problems of nonlinear complex system control design are still in the centre of attention and there exists no universal solution by now. The aim of this paper is to propose the control algorithm for nonlinear syste...
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The problems of nonlinear complex system control design are still in the centre of attention and there exists no universal solution by now. The aim of this paper is to propose the control algorithm for nonlinear systems consisting of several subsystems. The subsystem nonlinearities are assumed to be known and the state variables are accessible only locally. The interactions between subsystems can be unknown, but bounded. The proposed control algorithm for such class of complex systems is based on the feedback linearization technique with automatic adjusting of the subsystem decay rate. The control performances are illustrated by an example of nonlinear system consisting of two interconnected subsystems.
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