Determination of parameters in interior permanent magnet synchronous motors with iron losses without torque measurement

Fernandez-Bernal, F.; García-Cerrada, Aurelio; Faure, R.

doi:10.1109/ias.2000.881143

Cited by 44 publications

(21 citation statements)

References 7 publications

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“…Analytical methods trying to solve this problem appear quite difficult, because loss equations include partial differential equations [5][6][7][19][20][21][22][23][24]35,36]. After investigating some previous studies [4,7,8,13,17,20,22,37], the usage of a motor model developed by the finite elements method (FEM) was chosen as the most suitable solution to the problem.…”

Section: Finite Element Analysis Of Core Losses In Pmsmmentioning

confidence: 99%

Neural network approach on loss minimization control of a PMSM with core resistance estimation

Erdoğan¹,

Özdemir²

2017

Turk J Elec Eng & Comp Sci

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Abstract:Permanent magnet synchronous motors (PMSMs) are often used in industry for high-performance applications.Their key features are high power density, linear torque control capability, high efficiency, and fast dynamic response.Today, PMSMs are prevalent especially for their use in hybrid electric vehicles. Since operating the motor at high efficiency values is critically important for electric vehicles, as for all other applications, minimum loss control appears to be an inevitable requirement in PMSMs. In this study, a neural network-based intelligent minimum loss control technique is applied to a PMSM. It is shown by means of the results obtained that the total machine losses can be controlled in a way that keeps them at a minimum level. It is worth noting here that this improvement is achieved compared to the case with I d set to zero, where no minimum loss control technique is used. Within this context, hysteresis and eddy current losses are primarily obtained under certain conditions by means of a PMSM finite element model, initially developed by CEDRAT as an educational demo. A comprehensive loss model with a dynamic core resistor estimator is developed using this information. A neural network controller is then applied to this model and comparisons are made with analytical methods such as field weakening and maximum torque per ampere control techniques. Finally, the obtained results are discussed.

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Section: Finite Element Analysis Of Core Losses In Pmsmmentioning

confidence: 99%

Neural network approach on loss minimization control of a PMSM with core resistance estimation

Erdoğan¹,

Özdemir²

2017

Turk J Elec Eng & Comp Sci

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show abstract

“…It can also guarantee the asymptotic stability of the control system. In this paper, the SMC term is designed as: (19) where η 1 > 0 and η 2 > 0 are the learning rates.…”

Section: Proposed Sliding Mode Speed Control Law With Online Gain mentioning

confidence: 99%

“…In addition, motor parameter variations are applied to confirm the robustness of the designed control system. Note that the electrical motor parameters can be changed depending on the temperature and stator currents during the operation of the drive system [19]. In the paper, the motor parameter variations are put as ∆R s = +50% of R s = 0.215 W, and ∆L s = -30% of L s = -0.96 mH.…”

Section: Laboratory Testmentioning

confidence: 99%

Sliding Mode Control of SPMSM Drivers: An Online Gain Tuning Approach with Unknown System Parameters

Jung¹,

Leu²,

Dang³

et al. 2014

Journal of Power Electronics

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This paper proposes an online gain tuning algorithm for a robust sliding mode speed controller of surface-mounted permanent magnet synchronous motor (SPMSM) drives. The proposed controller is constructed by a fuzzy neural network control (FNNC) term and a sliding mode control (SMC) term. Based on a fuzzy neural network, the first term is designed to approximate the nonlinear factors while the second term is used to stabilize the system dynamics by employing an online tuning rule. Therefore, unlike conventional speed controllers, the proposed control scheme does not require any knowledge of the system parameters. As a result, it is very robust to system parameter variations. The stability evaluation of the proposed control system is fully described based on the Lyapunov theory and related lemmas. For comparison purposes, a conventional sliding mode control (SMC) scheme is also tested under the same conditions as the proposed control method. It can be seen from the experimental results that the proposed SMC scheme exhibits better control performance (i.e., faster and more robust dynamic behavior, and a smaller steady-state error) than the conventional SMC method.

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“…In this work, four case studies summarized in Table 1 are simulated using MATLAB/SIMULINK and tested to verify the feasibility of the proposed observer-based NFC scheme and the observer-based FLC scheme. Cases 1 to 3 show the speed dynamic behaviors after a sudden change in the desired speed (ω d [24][25], the stator resistance (R s ) of the IPMSM varies as a function of the temperature, while the λ m , L ds and L qs vary as a function of the operating current. The R s increases as the temperature rises [24], and the λ m and L qs decrease as the i qs increases [25].…”

Section: Performance Investigation By Simulation and Experimental Resmentioning

confidence: 99%

“…Cases 1 to 3 show the speed dynamic behaviors after a sudden change in the desired speed (ω d [24][25], the stator resistance (R s ) of the IPMSM varies as a function of the temperature, while the λ m , L ds and L qs vary as a function of the operating current. The R s increases as the temperature rises [24], and the λ m and L qs decrease as the i qs increases [25]. However, the L ds slightly increases as the i ds is negative [25].…”

Section: Performance Investigation By Simulation and Experimental Resmentioning

confidence: 99%

Neuro-Fuzzy Control of Interior Permanent Magnet Synchronous Motors: Stability Analysis and Implementation

Dang¹,

Vu²,

Choi³

et al. 2013

Journal of Electrical Engineering and Technology

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-This paper investigates a robust neuro-fuzzy control (NFC) method which can accurately follow the speed reference of an interior permanent magnet synchronous motor (IPMSM) in the existence of nonlinearities and system uncertainties. A neuro-fuzzy control term is proposed to estimate these nonlinear and uncertain factors, therefore, this difficulty is completely solved. To make the global stability analysis simple and systematic, the time derivative of the quadratic Lyapunov function is selected as the cost function to be minimized. Moreover, the design procedure of the online self-tuning algorithm is comparatively simplified to reduce a computational burden of the NFC. Next, a rotor angular acceleration is obtained through the disturbance observer. The proposed observer-based NFC strategy can achieve better control performance (i.e., less steady-state error, less sensitivity) than the feedback linearization control method even when there exist some uncertainties in the electrical and mechanical parameters. Finally, the validity of the proposed neuro-fuzzy speed controller is confirmed through simulation and experimental studies on a prototype IPMSM drive system with a TMS320F28335 DSP.

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Determination of parameters in interior permanent magnet synchronous motors with iron losses without torque measurement

Cited by 44 publications

References 7 publications

Neural network approach on loss minimization control of a PMSM with core resistance estimation

Neural network approach on loss minimization control of a PMSM with core resistance estimation

Sliding Mode Control of SPMSM Drivers: An Online Gain Tuning Approach with Unknown System Parameters

Neuro-Fuzzy Control of Interior Permanent Magnet Synchronous Motors: Stability Analysis and Implementation

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