MRAS Based Online Parameter Identification for PMSM Considering VSI Nonlinearity

Pei, Genji; Liu, Jiaxi; Li, Liyi; Du, Peng; Pei, Le; Hu, Yusheng

doi:10.1109/peac.2018.8590394

Cited by 13 publications

(6 citation statements)

References 23 publications

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“…Several online methods have been previously implemented to solve the parameter estimation problems [14]-affine projection algorithm [15], extended Kalman filtering (EKF), Kalman like adaptive observers [16] and model reference adaptive system [17], which suffer from high computational load and complex designs. The Model Predictive Current Control (MPCC) for PMSM drives has also been proposed in the past [18] but mostly face torque and current ripples.…”

Section: Related Workmentioning

confidence: 99%

Virtual Sensor Using a Super Twisting Algorithm Based Uniform Robust Exact Differentiator for Electric Vehicles

et al. 2022

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The highly efficient Interior Permanent Magnet Synchronous Motor (IPMSM) is ubiquitous choice in Electric Vehicles (EVs) for today’s automotive industry. IPMSM control requires accurate knowledge of an immeasurable critical Permanent Magnet (PM) flux linkage parameter. The PM flux linkage is highly influenced by operating temperature which results in torque derating and hence power loss, unable to meet road loads and reduced life span of electrified powertrain in EVs. In this paper, novel virtual sensing scheme for estimating PM flux linkage through measured stator currents is designed for an IPMSM centric electrified powertrain. The proposed design is based on a Uniform Robust Exact Differentiator (URED) centric Super Twisting Algorithm (STA), which ensures robustness and finite-time convergence of the time derivative of the quadrature axis stator current of IPMSM. Moreover, URED is able to eliminate chattering without sacrificing robustness and precision. The proposed design detects variation in PM flux linkage due to change in operating temperature and hence is also able to establish characteristics of fault detection. The effectiveness and accuracy in different operating environments of the proposed scheme for nonlinear mathematical IPMSM model with complex EV dynamics are verified thorough extensive simulation experiments using MATLAB/Simulink.

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Section: Related Workmentioning

confidence: 99%

Virtual Sensor Using a Super Twisting Algorithm Based Uniform Robust Exact Differentiator for Electric Vehicles

et al. 2022

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“…Hence, the NDC (25) and ( 26) is feasible simply by the static feedback of the available system variables without requiring any high-order filters, estimators, or observers. Compared to other compensation methods based on model predictive control [22]- [24], multiple RLC adaptive filters [25], [26], LSM [27], MRAC [28], the proposed NDC show significant practical advantages in that it can be implemented without requiring system order increases, heavy computational load, or large memory size, and the compensation signal can be calculated within one sampling period without requiring longer multiple calculation cycles. The NDC input-output structure is illustrated in Fig.…”

Section: Design Of Nonlinear Damping Compensatormentioning

confidence: 99%

“…In order to fundamentally solve this problem, both the parameter values and the dead-time disturbance voltage are estimated based on recursive least squares (RLS) adaptive filters in [25], [26] and least square method (LSM) in [27]. In [28], a model reference adaptive control (MRAC) algorithm was proposed for updating the parameter estimates in such a way as to minimize αβ-axes current errors while considering VSI nonlinearity. However, these approaches suffer from the disadvantages of system order increases; complicated calculations for the RLS adaptive filters, LSM, and update laws in MRAC; and requirements of large memory size.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Damping Compensator for Dead-Time Disturbance Voltage Considering Parameter Uncertainty in SPMSM Drives

et al. 2021

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A dynamic model of surface-mounted permanent magnet synchronous motors (SPMSMs) is constructed to include the dead-time disturbance voltage of the voltage-source inverter and SPMSM parameter uncertainty. The constructed model reveals that both the disturbance voltage and the parameter uncertainty degrade the SPMSM stator current control performance. Based on this notion, a nonlinear damping compensator is designed to simultaneously compensate for the dead-time disturbance voltage and parameter uncertainty in a unified structure. The designed compensator is advantageous because it is implemented with only the static feedback of available system variables with a low computational load, which enables the compensation signal to be calculated within one sampling period without requiring a specific calculation cycle. A rigorous analysis using a Lyapunov function shows that the designed compensator achieves the global exponential ultimate boundedness of the stator current errors with arbitrarily small bounds and fast decay rates in the presence of both the dead-time disturbance voltage and parameter uncertainty. The designed compensator performance is demonstrated by performing experiments using a 750-W laboratory SPMSM drive and comparing the experimental results with those of a conventional proportional-integral controller with a feedforward compensator.

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“…Currently, these research efforts can be divided into two categories: traditional identification methods and identification methods based on artificial intelligence optimization algorithms. Traditional identification methods include Recursive Least Squares (RLS) [11]- [13], Model Reference Adaptive Systems (MRAS) [14]- [17], and Extended Kalman Filters (EKF) [18]- [20]. The RLS method linearizes the mathematical model of the motor, which is easy to implement but requires a large amount of computation and data storage space.…”

Section: Introductionmentioning

confidence: 99%

Parameter Identification of Permanent Magnet Synchronous Motor Based on CGCRAO Algorithm

Li,

Jian

2023

IEEE Access

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In response to the challenge of easily falling into local optima and slow identification speed in the parameter identification of permanent magnet synchronous motors (PMSMs), this paper proposes a CGCRAO algorithm based on chaotic initialization and a hybrid variation strategy. The algorithm uses Tent chaotic mapping for population initialization to improve population diversity. At the same time, by combining the Gaussian and Cauchy distribution characteristics and the three-stage operation idea, the optimal individual variation strategy is autonomously selected in real time to improve the RAO-1 algorithm. This paper validates the effectiveness of the algorithm improvement and the correctness of the threestage operation idea using eight benchmark test functions. Furthermore, This paper conducted comparative experiments on parameter identification of five algorithms under different operating conditions through simulation and experiments. The results indicate that the proposed CGCRAO algorithm enables fast and accurate identification of PMSM parameters.INDEX TERMS CGCRAO algorithm, permanent magnet synchronous motors (PMSMs), three-stage running idea, Chaos initialization, Gaussian-Cauchy hybrid variation.

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MRAS Based Online Parameter Identification for PMSM Considering VSI Nonlinearity

Cited by 13 publications

References 23 publications

Virtual Sensor Using a Super Twisting Algorithm Based Uniform Robust Exact Differentiator for Electric Vehicles

Virtual Sensor Using a Super Twisting Algorithm Based Uniform Robust Exact Differentiator for Electric Vehicles

Nonlinear Damping Compensator for Dead-Time Disturbance Voltage Considering Parameter Uncertainty in SPMSM Drives

Parameter Identification of Permanent Magnet Synchronous Motor Based on CGCRAO Algorithm

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