Design ofasoft sensing technique for measuring pitch and yaw angular positions foraTwinRotor MIMOSystem.

Sneha Nayak,Meghana Shankar,Santhosh Krishnan Venkata,Sravani Vemulapalli

doi:10.12688/f1000research.51894.1

Sneha Nayak, Meghana Shankar + Show 2 more

Open Access

https://doi.org/10.12688/f1000research.51894.1

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Journal: F1000Research	Publication Date: May 5, 2021
License type: CC BY 4.0

Affiliation: Manipal Academy of Higher Education

Abstract

This paper presentsa soft sensor design technique for estimation of pitch and yaw angular positions of a Twin Rotor MIMO System (TRMS).The objective of the proposed workwas to calculate the value of pitch and yaw angular positions using a stochastic estimation technique. Methods:Measurements from optical sensorswereused to measure fan blade rotationsper minute(RPM). The Kalman filter,which is a stochastic estimator,was used in the proposed system and its resultswere compared with those oftheLuenbergerobserver and neural network. The TwinRotor MIMOSystem is a nonlinear system with significant cross coupling between its rotors. Results:The estimatorswere designed for the decoupled system andwere applied in real life to the coupled TRMS. The convergence of estimation to the actual valueswas checked on a practical setup. The Kalman filter estimatorswere evaluated for various inputs and disturbances, and the resultswere corroborated in real time. Conclusion:From the proposed work itwas seen thattheKalman filterhadatleastIntegral Absolute Error(IAE),Integral Square Error(ISE),Integral Time Absolute Error(ITAE)as compared totheneural network andtheLuenbergerbased observer.

Highlights

Study of aerospace systems has always been a subject of interest by many researchers, engineers, and technical students
From the responses it was found that the proposed sensing technique was able to track the pitch and yaw positions accurately in a practical system
The performance measures Integral Absolute Error (IAE), Integral Square Error (ISE), and Integral Time Absolute Error (ITAE) were used to quantitatively compare the outputs obtained from the Luenberger observer, Kalman filter, and neural network estimator

Summary

Introduction

Study of aerospace systems has always been a subject of interest by many researchers, engineers, and technical students. Excitation to these motors is given by a controller based on a set point given by user. Netto et al.,[11] reported an Adaptive PID controller to cancel the effect of cross coupling between the tail rotor and main rotor when operating simultaneously in a TRMS. Design of a differential evolution based neural network model to control the TRMS with data of angular

Methods

Results

Discussion and Conclusions

Rohith G

29. Simon Haykin S

31. Orlowska-Kowalska T

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