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.

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.

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.