Laser pointing stabilization and control in the submicroradian regime with neural networks

The possibility of controling the pointing stability of a slowly pulsed Ti:Sapphire laser system by lowpass filters and artificial neural networks (NN) is investigated by performing time series analysis and computer simulations on experimentally measured datasets. The simulations show that at pulse repetition rates of 20 Hz it is possible to use a feedforward algorithm to reduce the angular standard deviation from 0.7 to 0.3 mu rad. The properties and advantages of NN methods such as automatic adaptation characteristics of a time series are discussed. (C) 2001 American Institute of Physics.