An Algorithm for Online Inertia Identification and Load Torque Observation via Adaptive Kalman Observer-Recursive Least Squares

Yang, Ming; Liu, Zirui; Long, Jinlin; Qu, Wanying; Xü, Dianguo

doi:10.3390/en11040778

Cited by 14 publications

(8 citation statements)

References 27 publications

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“…where ε N is the estimation error, y is the output matrix, ϕ is the feedback matrix, η is the estimated parameter vector, λ is the forgetting factor, I is the identity matrix, K and P are the update gain matrices. The value of the forgetting factor is usually chosen between 0.9 and 1 [21]. In this work, λ is set as 0.95.…”

Section: Multi-parameter Identificationmentioning

confidence: 99%

Voltage Vector Directional Control for IPMSM Based on MTPA Strategy

Song

Zhao

et al. 2020

IEEE Access

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Space vector pulse width modulation (SVPWM) is used in motor control because of its high dynamic response and flexible control. Maximum torque per ampere (MTPA) strategy has become the most commonly used choice for internal permanent magnet synchronous motor (IPMSM). However, complicated coordinate transformations are required in the usual SVPWM algorithm and the performance of the MTPA algorithm can be affected by the parameters variation. This paper proposes a voltage vector directional control (VVDC) method based on the MTPA strategy. The inverse Park transformation is avoided in the proposed control scheme. And the proposed algorithm not only is briefer and clearer in the sector judgment process but also has lesser calculation during the duty cycle generation. In order to realize the MTPA strategy, two voltage feedback closed loops are designed to compensate for the d-axis and q-axis currents. A simple and effective multi-parameter identification method is also presented to solve the parameters variation problem. The effectiveness of the proposed method is verified by simulation and experiments.INDEX TERMS IPMSM, MTPA, motor parameter identification, voltage vector control.

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Section: Multi-parameter Identificationmentioning

confidence: 99%

Voltage Vector Directional Control for IPMSM Based on MTPA Strategy

Song

Zhao

et al. 2020

IEEE Access

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“…Observation of motor inertia can be expressed as the following equation (Li and Liu, 2009; Yang M et al, 2018)…”

Section: Model-based Mpc Control Schemementioning

confidence: 99%

Model-based model predictive control for a direct-driven permanent magnet synchronous generator with internal and external disturbances

Yongwei

et al. 2019

Transactions of the Institute of Measurement and Control

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This paper deals with the critical issue in a direct-driven permanent magnet synchronous generator (PMSG)-based wind energy conversion system (WECS): the rejection of internal and external disturbances, including the uncertainties of external environment, rapid wind speed changes in the original parameters of the generator caused by mutative operating conditions. To track the maximum power, a maximum power point tracking strategy based on model predictive controller (MPC) is proposed with extended state observer (ESO) to attenuate the disturbances and uncertainties. In real application, system inertia and the system parameters vary in a wide range with variations of wind speeds and disturbances, which substantially degrade the maximum power tracking performance of wind turbine. The MPC design should incorporate the available model information into the ESO to improve the control efficiency. Based on this principle, a model-based MPC with ESO control structure is proposed in this paper. Simulation study is conducted to evaluate the performance of the proposed control strategy. It is shown that the effect of internal and external disturbances is compensated in a more effective way compared with the ESO-based MPC approach and traditional proportional integral differential (PID) control method.

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“…A PMSM is a typical nonlinear and strongly coupled system, with unpredictable external disturbances, as well as internal parameter variations [2]. In recent years, various nonlinear control methods [3][4][5][6][7][8][9][10][11], such as fuzzy logic control, sliding mode control, neural network control, nonlinear optimal control, internal model control, adaptive control, have been used to meet the requirements of high reliability and performance in PMSM control [7][8][9][10]. The fuzzy logic control is successfully applied in the speed control of PMSMs [12,13].…”

Section: Introductionmentioning

confidence: 99%

A Novel Adaptive Neuro-Control Approach for Permanent Magnet Synchronous Motor Speed Control

Wang

et al. 2018

Energies

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A speed controller for permanent magnet synchronous motors (PMSMs) under the field oriented control (FOC) method is discussed in this paper. First, a novel adaptive neuro-control approach, single artificial neuron goal representation heuristic dynamic programming (SAN-GrHDP) for speed regulation of PMSMs, is presented. For both current loops, PI controllers are adopted, respectively. Compared with the conventional single artificial neuron (SAN) control strategy, the proposed approach assumes an unknown mathematic model of the PMSM and guides the selection value of parameter K online. Besides, the proposed design can develop an internal reinforcement learning signal to guide the dynamic optimal control of the PMSM in the process. Finally, nonlinear optimal control simulations and experiments on the speed regulation of a PMSM are implemented in Matlab2016a and TMS320F28335, a 32-bit floating-point digital signal processor (DSP), respectively. To achieve a comparative study, the conventional SAN and SAN-GrHDP approaches are set up under identical conditions and parameters. Simulation and experiment results verify that the proposed controller can improve the speed control performance of PMSMs.

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An Algorithm for Online Inertia Identification and Load Torque Observation via Adaptive Kalman Observer-Recursive Least Squares

Cited by 14 publications

References 27 publications

Voltage Vector Directional Control for IPMSM Based on MTPA Strategy

Voltage Vector Directional Control for IPMSM Based on MTPA Strategy

Model-based model predictive control for a direct-driven permanent magnet synchronous generator with internal and external disturbances

A Novel Adaptive Neuro-Control Approach for Permanent Magnet Synchronous Motor Speed Control

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