Open Access

ARTICLE

Kalman Filter and H_∞ Filter Based Linear Quadratic Regulator for Furuta Pendulum

by N. Arulmozhi^1,*, T. Aruldoss Albert Victorie²

1 Department of Electronics and Instrumentation Engineering, Government College of Technology, Coimbatore, Tamil Nadu, India
2 Department of Electrical and Electronics Engineering, Anna University, Regional Campus, Coimbatore, Tamil Nadu, India

* Corresponding Author: N. Arulmozhi. Email:

Computer Systems Science and Engineering 2022, 43(2), 605-623. https://doi.org/10.32604/csse.2022.023376

Received 06 September 2021; Accepted 16 November 2021; Issue published 20 April 2022

Abstract

This paper deals with Furuta Pendulum (FP) or Rotary Inverted Pendulum (RIP), which is an under-actuated non-minimum unstable non-linear process. The process considered along with uncertainties which are unmodelled and analyses the performance of Linear Quadratic Regulator (LQR) with Kalman filter and H_∞ filter as two filter configurations. The LQR is a technique for developing practical feedback, in addition the desired x shows the vector of desirable states and is used as the external input to the closed-loop system. The effectiveness of the two filters in FP or RIP are measured and contrasted with rise time, peak time, settling time and maximum peak overshoot for time domain performance. The filters are also tested with gain margin, phase margin, disk stability margins for frequency domain performance and worst case stability margins for performance due to uncertainties. The H-infinity filter reduces the estimate error to a minimum, making it resilient in the worst case than the standard Kalman filter. Further, when the β restriction value lowers, the H_∞ filter becomes more robust. The worst case gain performance is also focused for the two filter configurations and tested where H_∞ filter is found to outperform towards robust stability and performance. Also the switchover between the two filters is dependent upon a user-specified co-efficient that gives the flexibility in the design of non-linear systems. The non-linear process is tested for set point tracking, disturbance rejection, un-modelled noise dynamics and uncertainties, which records robust performance towards stability.

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Keywords

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