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Author: search.filters.author.Italo Aranda-CetraroStart date: 2021

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    Highly maneuverable target tracking under glint noise via uniform robust exact filtering differentiator with intrapulse median filter
    (Institute of Electrical and Electronics Engineers Inc., 2021-12-28)
    Highly maneuverable target tracking under glint noise and nonlinear uncertainties during course changes and terminal maneuvers has been solved suboptimally for many years via the interacting multiple model algorithm with the use of the Kalman filter (KF), unscented Kalman filter, and the extended Kalman filter (EKF). Also, nonlinear KFs such as the cubature Kalman filter have been proposed to improve nonlinear tracking without being able to filter out glint noise. To this end, the particle filter and some KF based on variational Bayesian approach have been proposed with very good results in filtering out glint noise. Nonetheless, it is difficult for state-of-the-art methods to achieve efficient filtering of glint noise and nonlinear tracking at the same time. On the other hand, robust exact differentiators, based on the super-twisting algorithm, have been used for many years in output-feedback control and state observation in order to obtain the derivatives of an input signal with theoretical finite-time exactness. However, their potential for target tracking applications has not been explored sufficiently. In this article, a uniform robust exact filtering differentiator with intrapulse median filtering is proposed to filter out glint noise at the sliding manifold, while offering nonlinear tracking robustness via high-degree super-twisting terms outside the sliding manifold. Numerical simulations comparing the proposed solution to other state-of-the-art methods were conducted, showing promising results.
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    Nonlinear Robust Control by a Modulating-Function-Based Backstepping Super-Twisting Controller for a Quadruple Tank System
    (MDPI, 2023-06-01)
    In this paper, a robust nonlinear approach for control of liquid levels in a quadruple tank system (QTS) is developed based on the design of an integrator backstepping super-twisting controller, which implements a multivariable sliding surface, where the error trajectories converge to the origin at any operating point of the system. Since the backstepping algorithm is dependent on the derivatives of the state variables, and it is sensitive to measurement noise, integral transformations of the backstepping virtual controls are performed via the modulating functions technique, rendering the algorithm derivative-free and immune to noise. The simulations based on the dynamics of the QTS located at the Advanced Control Systems Laboratory of the Pontificia Universidad Católica del Perú (PUCP) showed a good performance of the designed controller and therefore the robustness of the proposed approach.
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    Adaptive Residual-Based Modulating Function Regressor for Decoupled Estimation of Leak Size and Localization in Uncertain Water Network Systems
    (Institute of Electrical and Electronics Engineers Inc., 2025-01-01)
    Most solutions for detecting, estimating, and localizing leaks in water networks rely on complex banks of Kalman filters (BKFs) or advanced stand-alone Kalman filter (KF) algorithms to account for the network's model uncertainty, requiring extra hardware, extensive calibration, and maintenance. This study proposes a modulating function (MF) regressor based on a lumped model with pressure-flow boundary conditions to detect and localize a single leak in a water network. The uncertainty of the lumped model is reduced by adapting the MF regressor via a Lyapunov-based adaptive law. A real water network system (WNS) test bench was employed to validate the effectiveness of the proposed regressor. Initially, an experimental phase was conducted to identify and analyze the primary sources of uncertainty of the plant models concerning the test setup. Subsequently, the proposed leak detection, estimation, and localization algorithm was tested and compared with the robust adaptive unscented Kalman Filter (RAUKF), showing promising results.
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