Application of predictive control methods for Radio telescope disk rotation control
Radio telescope (RT) installations are highly valuable assets and during the period of their service life they need regular repair and maintenance to be carried out for delivering satisfactory performance and minimizing downtime. Same down time can be expected during machinery usage. Constant control of telescope rotation angle is done manually using visual inspection of hardware. The accuracy of this procedure is very low, therefore, automation and computer control systems are required. With the growing automation technologies, predictive control can prove to be a better approach than the traditionally applied visual inspection policy and linear control models. In this paper, Irbene Radio telescope RT-16 disk rotation control motors are analysed using control voltage from the converters. Retrieved data from the small DC motor is used for the predictive control approach using two different methods: a neural network trained with Basic Levenberg-Marquardt method and a linear model. A multilayer perceptron network approach is used for prediction of the indicator voltage output which affects the monitoring of the disk rotating angle. Finally, an experimental control system was proposed and installed using National Instruments equipment.
predictive control neural network multi-layer perceptron data processing