2021 (vol. 31) - Number 3

*Vanya R. Barseghyan:*

The problem of control of rod heating process with nonseparated conditions at intermediate moments of time

*Khadidja Bentata , Ahmed Mohammedi, Tarak Benslimane:*

Development of rapid and reliable cuckoo search algorithm for global maximum power point tracking of solar PV systems in partial shading condition

*Jakub Musial, Krzysztof Stebel and Jacek Czeczot:*

Self-improving Q-learning based controller for a class of dynamical processes

*Ramesh Devarapalli and Vikash Kumar:*

Power system oscillation damping controller design: a novel approach of integrated HHO-PSO algorithm

*T. Kaczorek:*

Poles and zeros assignment by state feedbacks in positive linear systems

*Saule Sh. Kazhikenova and Sagyndyk N. Shaltakov, Bekbolat R. Nussupbekov:*

Difference melt model

*R. Almeida and N. Martins, E. Girejko and A.B. Malinowska, L. Machado:*

Evacuation by leader-follower model with bounded confidence and predictive mechanisms

*B. Zhao and R. Zhang, Y. Xing:*

Evaluation of medical service quality based on a novel multi-criteria decision-making method with unknown weighted information

*Stefan Mititelu, Savin Treanta:*

Efficiency in vector ratio variational control problems involving geodesic quasiinvex multiple integral functionals

*D.K. Dash and P.K. Sadhu, B. Subudhi:*

Spider monkey optimization (SMO) – lattice Levenberg–Marquardt recursive least squares based grid synchronization control scheme for a three-phase PV system

*Suresh Rasappan and K.A. Niranjan Kumar:*

Dynamics, control, stability, diffusion and synchronization of modified chaotic colpitts oscillator

ACS Abstract:

**2007 (Volume 17)**

Number 1

**Useful approximation of discrete transcendent transfer function**

Jaromir Kukal, Oskar Schmidt(ICT Prague, Department of Computing and Control Engineering, Czech) |

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

**FPGA Neural Network implementation for real time control**

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) |

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

**Computation of positive realization of MIMO linear systems**

Tadeusz Kaczorek, Lukasz Sajewski(Faculty of Electrical Engineering, Bialystok Technical University, Poland) |

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

**Iterative learning control for robot manipulators**

F. Bouakrif, D. Boukhetala, F. Boudjema(Laboratoire de Commande des Processus, Ecole Nationale Polytechinque, Elharrach, Algiers, Algeria) |

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

**Procedure application in assembler encoding**

Tomasz Praczyk(Naval University, Gdynia, Poland) |

**keywords:** neural networks, genetic algorithms, optimization

**Direct torque control for induction machines using neural networks**

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

second order dynamical systems

Jerzy Respondek(Silesian University of Technology, Gliwice, Poland) |

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

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