Archives of Control Sciences

Last issue
2017 (vol. 27) - Number 2

W. Bozejko, M. Wodecki:

Discrete Systems: Theory and Applications. Special issue.

G. Bocewicz, Z. Banaszak, I. Nielsen:

Delivery-flow routing and scheduling subject to constraints imposed by vehicle flows in fractal-like networks

W. Bozejko, A. Gnatowski, R. Idzikowski, M. Wodecki:

Cyclic flow shop scheduling problem with two-machine cells

W. Bozejko, M. Uchronski,, Z. Chaczko, M. Wodecki:

Parallel patterns determination in solving cyclic flow shop problem with setups

J. Brodny, S. Alszer, J. Krystek, M. Tutak:

Availability analysis of selected mining machinery

K. Chmielewska, D. Formanowicz, P. Formanowicz:

The effect of cigarette smoking on endothelial damage and atherosclerosis development - modeled and analyzed using Petri nets

A. Galuszka, J. Krystek, A. Swierniak, T. Grzejszczak, C. Lungoci:

Information management in passenger traffic supporting system design as a multi-criteria discrete optimization task

M. Kardynska, J. Smieja:

Sensitivity analysis of signaling pathway models based on discrete-time measurements

J. Kasprzyk, P. Krauze, S. Budzan, J. Rzepecki:

Vibration control in semi-active suspension of the experimental off-road vehicle using information about suspension deflection

M. Koryl, D. Mazur:

Towards emergence phenomenon in business process management

M. Koryl:

Active resources concept of computation for enterprise software

H. Krawczyk, M. Nykiel:

Mobile devices and computing cloud resources allocation for interactive applications

W. Mitkowski, W. Bauer, M. Zagórowska:

Discrete-time feedback stabilization

J. Pempera:

An exact block algorithm for no-idle RPQ problem

K. Rzosinska, D. Formanowicz, P. Formanowicz:

The study of the influence of micro-environmental signals on macrophage differentiation using a quantitative Petri net based model

K. Skrzypczyk , M. Mellado:

Vehicle navigation in populated areas using predictive control with environmental uncertainty handling

W. Bozejko, J. Pempera, M. Wodecki:

A fine-grained parallel algorithm for the cyclic flexible job shop problem

ACS Abstract:

2007 (Volume 17)
Number 2

1.	New necessary conditions for optimal control of linear stationary systems
2.	Affine invariant 2D recognition in grain classification
3.	Combination and genetic algorithms for the locations and tuning of robust power system stabilizers
4.	Advanced predictive control of a distillation column with neural models
5.	On-line model identification for active noise control using integrated bispectrum analysis
6.	New approach to linguistic fuzzy modeling of induction motor fed by PWM voltage source inverter

New necessary conditions for optimal control of linear stationary systems Download full PDF article

Stanislaw Bialas
(Faculty of Applied Mathematics, AGH University of Science and Technology, Cracow, Poland)

Henryk Gorecki
(College of Computer Science, Lodz, Poland )

The linear-quadratic problem in the system with proportional controllers is considered. New necessary conditions for establishing optimal values of gains Ki are determined which are very simple and convenient for calculations. New properties of the Hurwitz matrix are also discovered. The proposed method is alternative to the well known algebraic Riccati equations.

keywords: linear stationary systems, optimal control, Hurwitz matrix

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Affine invariant 2D recognition in grain classification Download full PDF article

Katerina Novakova
(Czech Technical University in Prague, Faculty of Nuclear Sciences and Physical Engineering, Department of Software Engineering in Economy, Prague, Czech Republic)

Jaromir Kukal
(Institute of Chemical Technology, Faculty of Chemical Engineering, Department of Computing and Control Engineering, Prague, Czech Republic)

Invariant recognition of 2D binary image often arises from image moments. They enable the construction of affine transform, which ensures the invariance to translation, scaling, first rotation and stretching of the image. It is a problem to ensure the invariance to the second rotation. The paper deals with two methods how to realize the affine invariant recognition system with the numerical stable elimination of the second rotation. Modified images are obtained via polar or Radon's transform. Mentioned two approaches enable affine invariant systems construction and they were used for analysis of particles in granular mixtures. The affine invariant system is applied to detail analysis of fertilizer grains.

keywords: 2D recognition, affine invariance, classification, grain analysis, vision

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Combination and genetic algorithms for the locations and tuning of robust power system stabilizers Download full PDF article

Karim Sebaa
(Department of Electrical Engineering, University Center of Yahia Fares, Medea, Algeria)

Mohamed Boudour
(Department of Electrical Engineering, University of Sciences and Technology Houari Boumediene, Algiers, Algeria)

Combinations and Genetic Algorithms for the Locations and tuning of Robust Power System Stabilizers (PSS's) is presented in this paper. The PSS parameters and locations are computed to assure maximum damping performance under different operating conditions. The efficacy of this technique in damping local and inter-area modes of oscillations in multimachine power systems is confirmed through nonlinear simulation results and eigenvalues analysis.

keywords: optimal location, PSS design, genetic algorithm, transient stability, dynamic performances

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Advanced predictive control of a distillation column with neural models Download full PDF article

Maciej Lawrynczuk
(Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, Warszawa, Poland)

This paper describes application of linear and nonlinear Model Predictive Control (MPC) algorithms to a cyclohexane-heptane distillation column. Two nonlinear MPC techniques are compared in terms of control accuracy and computational complexity: MPC with Nonlinear Optimization (MPC-NO) and MPC with Nonlinear Prediction and Linearization (MPC-NPL). In nonlinear MPC a feedforward neural model is used rather than significantly complicated and causing numerical problems fundamental model of the process.

keywords: model predictive control, neural networks, optimization, linearization, quadratic programming

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On-line model identification for active noise control using integrated bispectrum analysis Download full PDF article

Teresa Glowka
(Institute of Automatic Control, Silesian University of Technology, Gliwice, Poland)

In the paper a problem of the on-line model identification of secondary and feedback paths in the feedforward ANC system is considered. The system is closed-loop, with low signal-to-noise ratio and with the disturbance affecting the input and output of the identified paths. To overcome the mentioned difficulties a new approach to the identification based on the higher-order spectra is presented. The integrated bispectrum-based identification method is proposed and the results of its applying are provided and compared with the results derived from the classical methods. The estimates are computed on the basis of data acquired in the laboratory (real-world) experiment as well as in the computer simulations.

keywords: active noise control, closed-loop systems, higher-order statistics, non-gaussian processes, spectral analysis, system identification

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New approach to linguistic fuzzy modeling of induction motor fed by PWM voltage source inverter Download full PDF article

L. Barazane
(University of Science and Technology Houari, Boumediene (USTHB) Faculty of Electronic and Computing, Alger, Algerie)

M.M. Krishan
(Al-Balq'a Applied University, Engineering of Information and Measurement Systems, Jordan)

A. Khwaldeh
(Department of Computer Science Faculty of Engineering, Philadelphia University, Jordan)

P. Sicard
(Groupe de Recherche en Electronique Industrielle Universite du Quebec a Trois-Rivieres Trois-Rivieres (Quebec) Canada)

In this paper, a new approach of linguistic fuzzy modeling proposed by Ben-Ghalia et al. is applied to induction motor. Thus, a classical model is first given together with the most used asynchronous motor control strategy, which is the pole assignment. After more, in order to minimize the dependence of the system to parameter variations and external perturbations, and reduce the number of variable states, a new model of the original system is proposed. This model is too simple and is suitable to cognitive approach such as fuzzy modeling. Then, fuzzy modeling of an uncertain system using the approach of Ben-Ghalia et al. to reduce the effects of imperfections of the classical modeling are introduced. In addition, their applications in the fuzzy sliding mode control of the asynchronous motor are presented. Finally, Simulation results reveal some very interesting features and show that the proposed fuzzy model has great potential for use as an alternative to the approximation of the conventional model of induction motor taking into account the effect of system model uncertainties and gives a good performances in sliding mode control.

keywords: feedback linearization, nonlinear feedback control, sliding mode control, induction motor, fuzzy model based control, fuzzy sliding mode control

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