arXiv subjects

Katherin Indriawati

Publications and source records attributed to Katherin Indriawati.

6 recordsLinked to original sources

arXiv2026

From Winding-Fault Geometry to Reliability: Estimation and Prognosis of Stator Inter-Turn Faults

This paper develops an integrated framework for estimation, prognosis, and reliability assessment of stator inter-turn faults in induction motors. The fault is characterized by its severity, defined as the fraction of short-circuited turns, and its spatial orientation. A geometric fault model shows that the resulting output signature is affine in the fault severity and exhibits a fundamental spatial periodicity. Exploiting this structure, an augmented-state particle filter jointly estimates the nonlinear electromechanical state and the unknown fault severity while quantifying posterior uncertainty. The estimated degradation is then propagated using four prognostic models: linear trend, Holt exponential smoothing, Bayesian degradation, and particle-based forecasting. Their predictions are connected to threshold-crossing remaining useful life (RUL), first-passage reliability, degradation-dependent hazard reliability, and a Weibull lifetime benchmark, thereby providing both deterministic and probabilistic health assessments. Numerical results demonstrate accurate online fault estimation and output reconstruction, characterize the effects of degradation pattern and prediction horizon on prognosis, and show consistent reliability and threshold-crossing predictions. Moreover, estimation accuracy remains comparable across the distinct fault orientations induced by the spatial periodicity. The resulting framework provides a unified connection from physics-based inter-turn fault modeling to online diagnosis, degradation prognosis, and reliability assessment.

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arXiv2026

Adaptive Covariance Kalman Filtering and Nonlinear Decoupling Control via Feedback Linearization for a Three-Tank Process

Hydraulic three-tank systems are widely used in water treatment and liquid storage applications, where accurate level regulation is essential for safe and efficient operation. This paper investigates linear and nonlinear control strategies for reference tracking in a three-tank process. A linear state-feedback controller with integral action is first designed based on a linearized model, followed by a nonlinear decoupling controller using feedback linearization. In addition, an adaptive covariance Kalman filter (AKF) is employed for state estimation by dynamically updating the process-noise covariance matrix. Numerical simulations demonstrate that both control approaches achieve satisfactory reference tracking, while the proposed AKF provides accurate state estimation and effectively captures the nonlinear system behavior. The results highlight the effectiveness of combining nonlinear control and adaptive state estimation for hydraulic process systems.

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arXiv2026

Cooperative $\mathcal{H}_\infty$ Fault-Tolerant Tracking with ISS Guarantees for Networked Systems with Sensor Faults

This paper develops a cooperative fault-tolerant tracking framework for heterogeneous networked linear systems subject to sensor faults and external disturbances. Each unit employs an augmented $\mathcal{H}_\infty$ observer that jointly reconstructs the system state and unknown sensor fault, providing disturbance-attenuated estimation guarantees. An inner state-feedback gain is synthesized through convex $\mathcal{H}_\infty$ Linear Matrix Inequalities (LMIs) to ensure robust closed-loop stabilization and disturbance rejection, while an outer distributed integral action eliminates steady-state tracking offsets and enables cooperative tracking of a setpoint source. The resulting cooperative error dynamics are shown to satisfy an Input-to-State Stability (ISS) property with respect to disturbances and residual estimation uncertainty, and converge exponentially to zero in the disturbance-free case. Furthermore, vanishing cooperative error guarantees network-wide consensus tracking of the desired setpoint. Numerical studies on heterogeneous DC-motor networks with star, cyclic, and path communication topologies demonstrate accurate state and fault estimation, robust cooperative tracking, and resilience against disturbances and time-varying sensor faults. The proposed framework provides a scalable and robust coordination strategy for interconnected systems operating under sensing imperfections and uncertain environments.

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arXiv2025

Model Reference Adaptive Control of Networked Systems with State and Input Delays

Adaptive control strategies have progressively advanced to accommodate increasingly uncertain, delayed, and interconnected systems. This paper addresses the model reference adaptive control (MRAC) of networked, heterogeneous, and unknown dynamical agents subject to both state and input delays. The objective is to ensure that all follower agents asymptotically track the trajectory of a stable leader system, despite system uncertainties and communication constraints. Two communication topologies are considered, full connectivity between each agent and the leader, and partial connectivity wherein agents rely on both neighboring peers and the leader. The agent-to-agent and agent-to-leader interactions are encoded using a Laplacian-like matrix and a diagonal model-weighting matrix, respectively. To compensate for the delays, a predictor-based control structure and an auxiliary dynamic system are proposed. The control framework includes distributed adaptive parameter laws derived via Lyapunov-based analysis, ensuring convergence of the augmented tracking error. Stability conditions are established through a carefully constructed Lyapunov Krasovskii functional, under minimal assumptions on connectivity and excitation. Numerical simulations of both network structures validate the proposed method, demonstrating that exact leader tracking is achieved under appropriately designed learning rates and initializations. This work lays a foundation for future studies on fault-resilient distributed adaptive control incorporating data-driven or reinforcement learning techniques.

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arXiv2023

Fault-Tolerant Control Design in Scrubber Plant with Fault on Sensor Sensitivity

The concept of fault-tolerant control has extensively been explored with various mapping of development. It starts from the system characteristic, the robustness of the controller, estimation methods and optimization, to the combination of the faults such that it can touch the true observed system. The mathematical concepts of the scrubber plant taking into account the pressure parameter along with sensing element and actuator are proposed. The data to construct the designs derive from the true values in one of Indonesian company. The performances coming from the simulations depict that the open- and closed-loop system could be the same as those of the real results. Furthermore, the observer is proposed to give the estimates of the states of $(\hat{x})$ and $(\hat{f}_s)$ showing the positive trace on the set-point of the residual fault followed by designing the fault-tolerant control with sensor fault on sensitivity. The scenarios are to give the lack of reading in sensor with $70\%$ and $85\%$ sensitivity and those are contrasted to the system without FTC (only PI controller). The yields portray that the system with FTC could deal with those sensor fault scenarios while its counterpart cannot drawing the faulty performance instead of tracking the set-point. The next project associated with this paper is also mentioned in the last section.

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arXiv2023

Non-Linear Estimation using the Weighted Average Consensus-Based Unscented Filtering for Various Vehicles Dynamics towards Autonomous Sensorless Design

The concerns to autonomous vehicles have been becoming more intriguing in coping with the more environmentally dynamics non-linear systems under some constraints and disturbances. These vehicles connect not only to the self-instruments yet to the neighborhoods components, making the diverse interconnected communications which should be handled locally to ease the computation and to fasten the decision. To deal with those interconnected networks, the distributed estimation to reach the untouched states, pursuing sensorless design, is approached, initiated by the construction of the modified pseudo measurement which, due to approximation, led to the weighted average consensus calculation within unscented filtering along with the bounded estimation errors. Moreover, the tested vehicles are also associated to certain robust control scenarios subject to noise and disturbance with some stability analysis to ensure the usage of the proposed estimation algorithm. The numerical instances are presented along with the performances of the control and estimation method. The results affirms the effectiveness of the method with limited error deviation compared to the other centralized and distributed filtering. Beyond these, the further research would be the directed sensorless design and fault-tolerant learning control subject to faults to negate the failures.

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