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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 1
1. Useful approximation of discrete transcendent transfer function
2. FPGA Neural Network implementation for real time control
3. Computation of positive realization of MIMO linear systems
4. Iterative learning control for robot manipulators
5. Procedure application in assembler encoding
6. Direct torque control for induction machines using neural networks
7. An algebraic approach to linear-quadratic optimization of
second order dynamical systems

Useful approximation of discrete transcendent transfer functionDownload full PDF article
Jaromir Kukal, Oskar Schmidt
(ICT Prague, Department of Computing and Control Engineering, Czech)

Linear systems with distributed parameters are described via linear partial differential equations. The application of Laplace transform comes to discrete transcendent transfer functions. When the response to unit step is aperiodic then the transfer function can be approximated by the system of second order with time delay. The role of sampling period is studied on four examples of heat and mass transfer systems. The methodology is based on inverse Laplace transform, sampling, Z transform and properties of power series. A new useful lemma was developed to help with error approximation. All the calculations were performed in the Matlab environment.

keywords: power series, transcendent transfer function, sampling, approximation, Matlab


FPGA Neural Network implementation for real time controlDownload full PDF article
C. Benbouchama, S. Sakhi
(Department of Automatics, EMP, Algiers, Algeria)
M. Tadjine
(Department of Electric Eng, LCP, ENP, Algiers, Algeria)
A. Bouridane
(School of Electronics, Electrical Eng and Computer Science, Queen's University, Belfast, Northern Ireland)

This paper describes an efficient implementation of neural multi-layer networks on FPGA fabric (Field Programmable Gate Array). A back-propagation algorithm was used for the training task while implementation and synthesis tools are centered on the ISE 6.3 of Xilinx with the targeted components being VirtexII and VirtexIIPro. A fixed point and a floating point number representation were used for encoding real numbers and for data processing, respectively. The realization of the activation function was carried out according to three methods, for which the results of simulation and synthesis are also presented. The implementation performances were tested using an approximation of some linear and non-linear functions. Of particular importance, two experimental evaluations involving the speed and the position control of a DC motor are given to demonstrate the features of the adopted methodology.

keywords: back propagation, control, FPGA, implementation, Neural Networks


Computation of positive realization of MIMO linear systemsDownload full PDF article
Tadeusz Kaczorek, Lukasz Sajewski
(Faculty of Electrical Engineering, Bialystok Technical University, Poland)

The realization problem for 2D positive multi-inputs and multi-outputs (MIMO) linear hybrid systems is formulated and a method based on the state variable diagram for finding a positive realization of a given proper transfer matrix is proposed. Sufficient conditions for the existence of a positive realization of a given proper transfer matrix are established. A procedure for computation of a positive realization is proposed and illustrated by a numerical example.

keywords: hybrid, 2D system, positive, realization, existence, computation


Iterative learning control for robot manipulatorsDownload full PDF article
F. Bouakrif, D. Boukhetala, F. Boudjema
(Laboratoire de Commande des Processus, Ecole Nationale Polytechinque, Elharrach, Algiers, Algeria)

In this paper, we present a time-domain iterative learning control scheme for the trajectory tracking problem of rigid robot manipulators that perform repeated tasks. The proposed control scheme comprises a computed torque control designed exploiting the approximated linear model of a manipulator and a learning law to compensate effects of nonlinear terms, that are ignored in obtaining the linear model, and the external disturbance. We show that the iterative learning controller is capable of effectively canceling the disturbances caused by nonlinear terms and other disturbance. The asymptotic stability of the closed-loop system is guaranteed, and the conditions of this stability are given. Simulation results on PUMA 560 robot show clearly efficiency of the proposed scheme.

keywords: computed torque control, iterative learning control, nonlinear terms, robot manipulator


Procedure application in assembler encodingDownload full PDF article
Tomasz Praczyk
(Naval University, Gdynia, Poland)

In order to use evolutionary techniques to search for optimal neural networks it is necessary to encode the latter in the form of chromosome or a set of chromosomes. In the paper a new neural network encoding method is presented - assembler encoding (AE). It assumes neural network encoded in the form of linearly organized structure similar to assembler program with code part and with data part. The task of assembler code is to create connectivity matrix which in turn can be transferred into neural network with any architecture. In the article the variant of AE in which we deal with application of procedures is discussed. Assembler encoding programs consisting of many procedures are used to solve optimization problem. Results of tests conducted are included in the paper.

keywords: neural networks, genetic algorithms, optimization


Direct torque control for induction machines using neural networksDownload full PDF article
Iqbal Messaif, Nadia Saadia
(Universite des Sciences et de la Technomogie Houari Boumediene, Algiers, Algeria)
El-Madjid Berkouk
(Ecole Nationale Polytechnique, Algiers, Algeria)

In this work, a novel switching vector selector in Direct Torque Control of an induction machine using Artificial Neural Network is studied.

In the first part, we describe design of a speed sensor-less Direct Torque Control (DTC) strategy of an induction motor supplied by a two-level voltage source. For this, a conventional look up table is applied which improves the performances. Due to the high computation load, this technique is not convenient for an one-line and real-time control.

Thus, a simplified method of choosing the output vector for two-level voltage source inverter-fed induction machine is proposed in the second part, and a novel switching vector selector using Artificial Neural Network (ANN) is trained under the tutor of the method mentioned above. The ANN receives attention as controllers for many industrial applications. Although these networks eliminate the need for mathematical models, they require a lot of training to understand the model of plant or process. In fact, when the stator flux and electromagnetic torque are different from theirs respective references, the output vector can be expediently acquired.

Simulation results showed that the ANN structure can replace successfully the conventional look up table of the DTC.

keywords: direct torque control, switching table, inverter, induction machine, neural network structure and training, Levenberg-Marquardt algorithm


An algebraic approach to linear-quadratic optimization of
second order dynamical systems
Download full PDF article
Jerzy Respondek
(Silesian University of Technology, Gliwice, Poland)

The article is devoted to finding the optimal control for the dynamical system governed by the linear second order stationary differential equation with damping terms with quadratic performance index by the algebraic approach. The infinite control time horizon is assumed. The main part of the article is devoted to the finding of the least solution of the Riccati equation. Following this aim the methods of the linear algebra and the matrix spectral theory were involved.

keywords: linear-quadratic optimization, spectral theory, Riccati equation


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