QFT Based Control for Twin Rotor MIMO System

Abstract

This paper introduces a methodology to design quantitative feedback theory (QFT) robust controller for twin rotor multiple input-multiple output (MIMO) system. This approach has been considered in order to assure high control performance of the system. The QFT controller is used to design a simple, low order and robust position control for twin rotor MIMO system. Further the QFT controller is designed to give a robust stability and performance (disturbance rejection, reference tracking) with the presence of system parametric and non-parametric uncertainty. The robust stability and performance analysis has been done with unstructured model.

Optimal feedback control of twin rotor MIMO system with a prescribed degree of stability

“…Its each rotor affects both the position angles (corresponding to the vertical and horizontal positions of the beam) ψ and ϕ . The significant cross-coupling can be also observed between the action of the main and tail rotors (Meena and Janardhanan, 2011; Huang, 2011; Mondal and Mahanta, 2012). The TRMS model contains open-loop unstable dynamics and faces uncertainty due to external disturbances and coupling between the main and tail rotors.…”

“…Hence, controlling the TRMS for either posture stabilization or trajectory tracking is a challenging task and has gained a lot of research interest (Pratap and Purwar, 2012; Wen and Li, 2011; Tao et al , 2010a). The TRMS developed by Feedback Instruments Limited (see Figure 1) has been a popular experimental platform for the development of controller algorithms (Feedback, 2010; Meena and Janardhanan, 2011; Huang, 2011; Mondal and Mahanta, 2012). It consists of a beam pivoted on its base in such a way that it can rotate freely both in its horizontal and vertical planes.…”

“…Several research works have been explored to develop control synthesis frameworks for TRMS. Some of them can already be found in the literature (see Feedback, 2010; Pratap and Purwar, 2012; Wen and Li, 2011; Tao et al , 2010a, b; Ramalakshmi and Manoharan, 2012; Chalupa et al , 2015; Rahideh et al , 2008; Shaik et al , 2011; Pratap et al , 2012; Gadewadikar et al , 2009; Usman et al , 2014; Juang et al , 2008a, b, 2011; Meena and Janardhanan, 2011; Huang, 2011; Mondal and Mahanta, 2012; Butt and Aschemann, 2015; Rahideh et al , 2012; Yang et al , 2016; Butt et al , 2015; Wen and Lu, 2008; Pradhan and Ghosh, 2013; Nasir and Tokhi, 2015). A linear-quadratic regulation (LQR) optimal controllers were also designed in Tao et al (2010b) and Pratap et al (2012) but the prescribed degree of stability concept was not discussed in any of the paper.…”