Robust H-Infinity Active Fault Tolerant Control for Incomplete Information Problem

Cai

J. Electr. Eng. Technol.

et al. 2020

The state of charge (SOC) of lithium-ion batteries reflects their remaining capacity. Accurate estimation of SOC helps battery safety and is beneficial to the efficient management of batteries. The charging and discharging processes of lithium-Ion batteries are very complicated, and it is difficult to obtain accurate SOC estimation results. Therefore, it is important to study improved algorithms for SOC estimation for this nonlinear non-Gaussian battery system. In this paper, we propose an unscented H-infinity filter (UHF) based SOC estimation method, which combines the advantages of both the unscented Kalman filter (UKF) and the H-infinity filter (HF). The UKF propagates the sigma points through the nonlinear system and does not need the first-order linear approximation of the system equation, while the HF can suppress the non-Gaussian noise in the system to the greatest extent. The proposed UHF based SOC estimation algorithm is verified and evaluated in the battery management system, and further optimized in practical problems. Experimental results show that the proposed UHF based algorithm can perform accurate SOC estimation for lithium-ion batteries, and is superior to the UKF based SOC estimation. Keywords Index terms-unscented kalman filterer • H-infinity filter • State of charge* Zhiwei He

Section: The H-infinity Filtermentioning

confidence: 99%

Section: A Dynamic Models Of Li-ion Batteries For Soc Estimationmentioning

confidence: 99%

State of Charge Estimation for Li-Ion Batteries Based on an Unscented H-Infinity Filter

Cai

J. Electr. Eng. Technol.

et al. 2020

“…In the PFTCs method, no online error information is required for the controller but has an error limit to overcome. While the AFTCS method, the parameters of the controller are reconfigured according to the error information obtained online to improve the stability and overall performance of the system when an error occurs on the component [2].…”

Section: Introductionmentioning

confidence: 99%

Active Fault Tolerance Control For Sensor Fault Problem in Wind Turbine Using SMO with LMI Approach

Mardiyah¹,

Setyawan²,

Retno³

et al. 2018

EECSI

In this paper, we start to investigate the sensor fault problem in a Wind Turbine model with Fault Tolerant Control (FTC). FTC is used to allow the parameters of the controller to be reconfigured in accordance error information obtained online from sensors to improve the stability and overall performance of the system when an error occurs. The design is divided into two parts. The first part is designed Sliding Mode Observer (SMO) based Fault Detection Filter (FDF) to generate a residual signal to estimate fault. FDF is designed to maximize sensitivity fault. The second is a design output feedback control and Fault Compensation to guarantee the stability and performance system from disturbance by ignoring faults. Moreover, the function of fault compensation is to minimize effect fault of the system. The main contribution of this research is FTC proved to solve the sensor fault problem in a Wind Turbine model. The simulation showed the effectiveness of this method to estimate the fault and stabilized the system faster to a steady condition.

Data-Driven Fault Detection and Isolation for Multirotor System Using Koopman Operator

Lee,

Im,

Kim

et al. 2024

J Intell Robot Syst

This paper presents a data-driven fault detection and isolation (FDI) for a multirotor system using Koopman operator and Luenberger observer. Koopman operator is an infinite-dimensional linear operator that can transform nonlinear dynamical systems into linear ones. Using this transformation, our aim is to apply the linear fault detection method to the nonlinear system. Initially, a Koopman operator-based linear model is derived to represent the multirotor system, considering factors like non-diagonal inertial tensor, center of gravity variations, aerodynamic effects, and actuator dynamics. Various candidate lifting functions are evaluated for prediction performance and compared using the root mean square error to identify the most suitable one. Subsequently, a Koopman operator-based Luenberger observer is proposed using the lifted linear model to generate residuals for identifying faulty actuators. Simulation and experimental results demonstrate the effectiveness of the proposed observer in detecting actuator faults such as bias and loss of effectiveness, without the need for an explicitly defined fault dataset.