Performance of any system is identified through the observation of significant system parameters. Required parameters have to be measured using suitable sensors. But in some scenarios, it is difficult to measure some of the parameters due to issues in the placement of sensors. In such cases, estimators are developed to measure the parameters indirectly. In this paper, an attempt is made to develop an estimator to monitor the value of pitch and yaw of a twin-rotor multi input multi output system. The observer is developed using two methods one using Luenberger’s equations and the other using an Artificial Neural Network (ANN). For training the neural network model, the backpropagation algorithm is used. Tests have been conducted to analyze and compare the behavior of both observers. From the results, it is evident that a Luenberger observer performs better when sufficient system information is available and ANN observer performs better when inadequate system information is available.
All Science Journal Classification (ASJC) codes
- Physics and Astronomy(all)
- Electrical and Electronic Engineering