Design of Neural Network based Estimator for Pitch and Yaw in a TRMS

Santhosh, K V and Meghana, Shankar (2016) Design of Neural Network based Estimator for Pitch and Yaw in a TRMS. In: Design of Neural Network based Estimator for Pitch and Yaw in a TRMS, 25/02/2016, Coimbatore.

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Abstract

This paper presents a technique for measurement of pitch and yaw in a Twin Rotor Multi input multi output System (TRMS). The objective of the proposed work is to estimate the pitch and yaw of rotor system using the data derived from optical sensors. An estimator is designed to predict the output pitch and yaw using the observer model. Estimator in the proposed work is designed using Luenbergerneural network logic. Feed forward type of neural network architecture using levenburg-Marquardt algorithm is used in the proposed technique. Once the estimator is designed it is subjected to test and is validated by comparing it with actual yaw and pitch values. Results obtained show successful implementation of proposed objectives.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: TRMS, Estimator, Artificial Neural Network, Observer
Subjects: Engineering > MIT Manipal > Instrumentation and Control
Depositing User: MIT Library
Date Deposited: 05 Jul 2016 10:17
Last Modified: 05 Jul 2016 10:17
URI: http://eprints.manipal.edu/id/eprint/146492

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