2018 (vol. 28) - Number 1

*I. Duleba, I. Karcz-Duleba:*

A comparison of methods solving repeatable inverse kinematics for robot manipulators

*A. Niederliński:*

A new approach for modelling uncertainty in expert systems knowledge bases

*T.T. Tuan, L.D. Tufa, M.I.A. Mutalib, N.M. Ramli :*

Optimal operation of a process by integrating dynamic economic optimization and model predictive control formulated with empirical model

*M. Kaleta:*

Network winner determination problem

*A. Kowalewski:*

Extremal problems for parabolic systems with time-varying lags

*T. Rybus, K. Seweryn, J.Z. S±siadek:*

Application of predictive control for manipulator mounted on a satellite

*T. Kaczorek:*

Reachability and observability of positive discrete-time linear systems with integer positive and negative powers of the state Frobenius matrices

*G. Grassi, A. Ouannas, V-T. Pham:*

A general unified approach to chaos synchronization in~continuous-time systems (with or without equilibrium points) as well as in discrete-time systems

*D. Pazderski:*

A robust smooth controller for a unicycle-like robot

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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