Fast real-time SDRE controllers using neural networks.

This paper describes the implementation of fast state-dependent Riccati equation (SDRE) control algorithms through the use of shallow and deep artificial neural networks (ANN). Several ANNs are trained to replicate an SDRE controller developed for a satellite attitude dynamics simulator (SADS) to display the technique's efficacy. The neural controllers have reduced computational complexity compared with the original SDRE controller, allowing its execution at a significantly higher rate. One of the neural controllers was validated using the SADS in a practical experiment. The experimental results indicate that the training error is sufficiently small for the neural controller to perform equivalently to the original SDRE controller. • A real-time SDRE controller is designed using shallow and deep neural networks. • A significant reduction in computation is achieved using the neural controllers. • Deep denoising autoencoders were trained as deep neural controllers. • The neural controller is validated through simulations and a practical experiment. • Results show that the neural controller retains its high performance. [ABSTRACT FROM AUTHOR]