Control system of robot movement

Abstract

Development of modern technologies is related to an increasing complexity of the objects controled and hence the systems controlling them. In the most cases, automatic control systems consist of different nonlinear elements that significantly limit the capabilities of classical control theory in designing controllers. In recent decades, the methodology of neural networks has been increasingly used lately. These networks may provide easier solutions to various complex control problems. Moreover, their basic elements (i.e., neurons) are nonlinear, and that is why neural networks are essentially nonlinear systems which can perform various control tasks. In this paper are investigated some new proposed structures, i.e., with so-called two separate inputs which allow to improve the dynamic characteristics of control system as well.

Neurocontrolled Car Speed System

“…These possibilities come up during the design, simulation, and testing processes [13][14][15][16] as well as in the ordinary exploitation period of a concrete product [17][18][19][20][21][22][23]. Controllers that use neural network systems achieve an effective speed control in both electric and traditional cars [24][25][26][27][28][29][30].…”