A novel concurrent learning-based sliding-mode observer for second-order multivariable systems with a time-varying coefficient: An application to machine vision

Keshavan, J (2023) A novel concurrent learning-based sliding-mode observer for second-order multivariable systems with a time-varying coefficient: An application to machine vision. In: International Journal of Robust and Nonlinear Control .

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Abstract

The problem of finite-time state recovery for multivariable second-order systems with a time-varying coefficient is considered in this study. The key challenge lies in the time-varying nature of the regressor coefficient, which possibly results in the lack of a well-defined relative degree, and renders the synthesis of a finite-time state observer difficult for such systems. In order to overcome this challenge, a novel multivariable sliding-mode observer is developed that relies on an information-rich term based on concurrent learning to ensure observer convergence. In particular, the concurrent learning-based augmentation term leverages information contained in prior data, which is recorded over a sliding time window in the recent past, so that the resulting observer structure need only satisfy a relaxed observability condition for ensuring finite-time convergence. A Lyapunov-based stability analysis is undertaken to demonstrate finite-time convergence of the observer estimates to a small uniform ultimate bound around the ground truth for a sufficiently large choice of observer gains. The observer is then applied to accomplish the task of structure and motion recovery from machine vision that involves tracking of a single stationary object feature by a moving camera across the image sequence. Numerical results are used to validate accurate observer performance in the presence of model uncertainty and measurement noise for weakly persistently exciting systems. Furthermore, a detailed comparison study with leading alternative designs is also included that demonstrates the superior performance of the proposed scheme. As the current approach precludes any reliance on a restrictive persistency of excitation condition that is difficult to satisfy apriori, an important advantage of the proposed scheme is its suitability to practical applications such as visual servo control. © 2023 John Wiley & Sons Ltd.

Item Type:	Journal Article
Publication:	International Journal of Robust and Nonlinear Control
Publisher:	John Wiley and Sons Ltd
Additional Information:	The copyright for this article belongs to John Wiley and Sons Ltd.
Keywords:	Computer vision; Learning systems; Multivariable systems; Sliding mode control; Uncertainty analysis; Visual servoing, Concurrent learning; Finite-time; Finite-time convergence; Lyapunov analysis; Machine-vision; Non-linear observer; Perspective dynamical system; Second orders; Sliding-mode observer; Time-varying coefficient, Dynamical systems
Department/Centre:	Division of Mechanical Sciences > Mechanical Engineering
Date Deposited:	17 Mar 2023 09:48
Last Modified:	17 Mar 2023 09:48
URI:	https://eprints.iisc.ac.in/id/eprint/81024

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