Identification Random Disturbances of Optomechanical Control Systems Based Neural Observers

This paper deals with the application of neural observer to estimate external random disturbance in the form of wind gust dynamic acting on the opto-mechanical control systems. First, we introduce the mathematical model of wind gust disturbances, which are considered as random processes with zero mean. The next section investigates architecture of neural observer based on dynamic recurrent neural networks (DRNN). The learning process of DRNN is described and the performances mean square error (MSE) of different learning algorithms are compared with the aim of studying quality identification and selection of the best learning algorithm for synthesis neural observer. In this work, designing, training, testing of DRNN is carried out in Matlab/ SIMULINK environment. © 2020 IEEE.