Sensorless Control of Surface-Mounted Permanent Magnet Synchronous Motor Using Adaptive Robust UKF

Ding, Hongchang; Qin, Xiaojun; Wei, Linchao

doi:10.1007/s42835-022-01061-y

Cited by 7 publications

(3 citation statements)

References 28 publications

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“…Step 4: Using Equation ( 23) and the weights of Equation ( 19), update the predicted value of the system state x− k−1,l and the state prediction variance matrix P xx at time t k−1,l [26].…”

Section: Principle Of the Ukf Algorithmmentioning

confidence: 99%

An Improved Multi-Timescale AEKF–AUKF Joint Algorithm for State-of-Charge Estimation of Lithium-Ion Batteries

Wu,

Zhou,

Mao

et al. 2023

Energies

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State-of-charge (SoC) estimation is one of the core functions of battery energy management systems. An accurate SoC estimation can guarantee the safe and reliable operation of the batteries system. In order to overcome the practical problems of low accuracy, noise uncertainty, poor robustness, and adaptability in parameter identification and SoC estimation of lithium-ion batteries, this paper proposes a joint estimation method based on the adaptive extended Kalman filter (AEKF) algorithm and the adaptive unscented Kalman filter (AUKF) algorithm in multiple time scales for 18,650 ternary lithium-ion batteries. Based on the slowly varying characteristics of lithium-ion batteries’ parameters and the quickly varying characteristics of the SoC parameter, firstly, the AEKF algorithm was used to online identify the parameters of the model of batteries with a macroscopic time scale. Secondly, the identified parameters were applied to the AUKF algorithm for SoC estimation of lithium-ion batteries with a microscopic time scale. Finally, the comparative simulation experiments were implemented, and the experimental results show the proposed joint algorithm has higher accuracy, adaptivity, robustness, and self-correction capability compared with the conventional algorithm.

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Section: Principle Of the Ukf Algorithmmentioning

confidence: 99%

An Improved Multi-Timescale AEKF–AUKF Joint Algorithm for State-of-Charge Estimation of Lithium-Ion Batteries

Wu,

Zhou,

Mao

et al. 2023

Energies

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show abstract

“…If the noise covariance is not selected properly, an EKF system may converge too slowly, jitter too much, or even completely diverge. Therefore, there have been many attempts to select optimal EKF noise covariance matrices [13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…After optimization, the system could suppress noise well and shorten the covariance matrix selection time. Some efforts [16,17] applied the unscented Kalman filter (UKF) to improve nonlinear calculation accuracy. However, the UKF is easily affected by system noise and measurement noise error, which restricts its application.…”

Section: Introductionmentioning

confidence: 99%

Sensorless HSPMSM Control of an Improved SMC and EKF Based on Immune PSO

Meng,

Liu,

Zhang

et al. 2023

Applied Sciences

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Here, for controlling a high-speed flywheel permanent magnet synchronous motor (HSPMSM), a position sensorless control method for estimation of motor rotor position and speed is proposed to address the problems faced by mechanical position sensors of high cost, large size, and poor interference immunity. The extended Kalman filter (EKF) has difficulty obtaining the optimal covariance matrix when performing state estimation. Therefore, the particle swarm algorithm (PSO) with an immune mechanism is used to optimize the covariance matrix of the EKF. However, the EKF algorithm makes the system less robust due to its delay effect. Based on the traditional sliding mode control rate, the exponential convergence law is improved, and the continuous function sat(s) is used instead of the symbolic function sgn(s). This improves the convergence law and proves the asymptotic stability of the designed sliding mode variable structure controller based on Lyapunov’s stability theorem. Then, the novel control law is applied to the sliding mode surface (SMS). An ordinary sliding mode controller (OSMC) using a linear sliding mode controller (LSMC), a global sliding mode controller (GSMC) using a global sliding mode surface (GSMS), and an integral sliding mode controller (ISMC) using an integral sliding mode surface (ISMS) are designed for improving control. Joint simulation in MATLAB and Simulink verifies that the optimized EKF based on the immune PSO can improve precision and accuracy for controlling the electronic rotor position and speed. Comparing the new sliding mode controller with a traditional PI controller reveals that the proposed system has stronger resistance to load disturbance and better robustness.

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Robust UKF orbit determination method with time-varying forgetting factor for angle/range-based integrated navigation system

QIANG,

LIN,

LIU

et al. 2024

Chinese Journal of Aeronautics

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Sensorless Control of Surface-Mounted Permanent Magnet Synchronous Motor Using Adaptive Robust UKF

Cited by 7 publications

References 28 publications

An Improved Multi-Timescale AEKF–AUKF Joint Algorithm for State-of-Charge Estimation of Lithium-Ion Batteries

An Improved Multi-Timescale AEKF–AUKF Joint Algorithm for State-of-Charge Estimation of Lithium-Ion Batteries

Sensorless HSPMSM Control of an Improved SMC and EKF Based on Immune PSO

Robust UKF orbit determination method with time-varying forgetting factor for angle/range-based integrated navigation system

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