Can I read Ground-based Telescope Pointing and Tracking Optimization Using a Neural Controller on EtoBox?

Ground-based Telescope Pointing and Tracking Optimization Using a Neural Controller by D Mancini; M Brescia; P Schipani is a Computer Science article available to read on EtoBox.

What is Ground-based Telescope Pointing and Tracking Optimization Using a Neural Controller about?

Neural network models (NN) have emerged as important components for applications of adaptive control theories. Their basic generalization capability, based on acquired knowledge, together with execution rapidity and correlation ability between input stimula, are basic attributes to consider NN as an extremely powerful tool for on-line control of complex systems. By a control system point of view, not only accuracy and speed, but also, in some cases, a high level of adaptation capability is required in order to match all working phases of the whole system during its lifetime. This is particularly remarkable for a new generation ground-based telescope control system. Infact, strong changes in terms of system speed and instantaneous position error tolerance are necessary, especially in case of trajectory disturb induced by wind shake. The classical control scheme adopted in such a system is based on the proportional integral (PI) filter, already applied and implemented on a large amount of new generation telescopes, considered as a standard in this technological environment. In this paper we introduce the concept of a new approach, the neural variable structure proportional integral,

Who reads Ground-based Telescope Pointing and Tracking Optimization Using a Neural Controller?

Author

D Mancini; M Brescia; P Schipani

Publisher

Elsevier Science; Elsevier ; Elsevier Ltd.; Elsevier BV (ISSN 0893-6080)

Published

2003

Language

EN

Field

Computer Science (Physical Sciences)