State and Parametric Fault Estimation Using Extended Kitanidis Kalman Filter for Chaotic Rössler System

Maleki, Shirin; Shahmansoorian, Aref

doi:10.3103/s0146411621090054

Cited by 1 publication

(1 citation statement)

References 32 publications

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“…The Kalman–Bucy filter is an algorithm (Kalman, 1960; Kalman and Bucy, 1961) used to estimate system states or parameters by considering and processing sensor measurements with white noise. If the Kalman–Bucy filter is used for estimating parameters and nonlinear system states, it is called an extended Kalman–Bucy filter (Herrera et al, 2022; Maleki and Shahmansoorian, 2021). When PID controllers are part of the whole control system, an extended Kalman–Bucy filter can estimate the PID parameters.…”

Section: Introductionmentioning

confidence: 99%

Control & synchronization of a unified chaotic system using an adaptive controller with an extended Kalman–Bucy-filter based Auto-Tuner

Kızmaz

2023

Transactions of the Institute of Measurement and Control

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A current challenge with adaptive controllers is to define efficient tuning methods of the controller parameters. Unlike linear systems, nonlinear systems may need parameters that are continuously tuned at different operating points to provide stability and desired behaviours. This study aims to develop a solution for tuning proportional–integral–derivative (PID) controller parameters as opposed to changing the operating points of a nonlinear system. Most tuning methods calculate parameters according to the system’s step or frequency response. However, adaptive controllers have self-tuneable parameters or control rules. The proposed algorithm in this paper contains a controller, an estimator, and a reference model, and uses the system model. Unlike the model reference adaptive control method, the proposed controller has tuneable controller parameters estimated by the extended Kalman–Bucy filter. The filter estimates the controller parameters to make the system perform like the auxiliary ideal reference model to ensure minimum-time consumption. Hence, this study aims to develop an algorithm that will automatically calculate controller parameters for each operating point of the controlled chaotic or nonlinear system to minimize settling time at each operating point. The proposed algorithm is implemented in a unified chaotic system in which the estimator and controller of the system run together. Simulation results confirm the performance of the proposed algorithm. In addition, the simulation results provide strong evidence that the proposed algorithm can be an effective tool for controlling nonlinear or chaotic systems.

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Section: Introductionmentioning

confidence: 99%

Control & synchronization of a unified chaotic system using an adaptive controller with an extended Kalman–Bucy-filter based Auto-Tuner

Kızmaz

2023

Transactions of the Institute of Measurement and Control

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State and Parametric Fault Estimation Using Extended Kitanidis Kalman Filter for Chaotic Rössler System

Cited by 1 publication

References 32 publications

Control & synchronization of a unified chaotic system using an adaptive controller with an extended Kalman–Bucy-filter based Auto-Tuner

Control & synchronization of a unified chaotic system using an adaptive controller with an extended Kalman–Bucy-filter based Auto-Tuner

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