Robust adaptive observer-based finite control set model predictive current control for sensorless speed control of surface permanent magnet synchronous motor

Usama, Muhammad; Kim, Jae Hong

doi:10.1177/0142331220979264

Cited by 9 publications

(11 citation statements)

References 38 publications

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“…The switching state corresponding to voltage vector is applied to inverter, which can realize fast tracking of the reference current. In this paper, the FCS-MPCC strategy adopted in current inner loop is similar to the current control strategy in Usama and Kim (2021). Meanwhile, the current constraints in cost function and control delay problem need to be considered further.…”

Section: Full Predictive Control Of Pmtmmentioning

confidence: 99%

Full prediction cascade control of permanent magnet toroidal motor with time-varying parameters

Liu

Wang

2023

Transactions of the Institute of Measurement and Control

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This paper presents the modeling and predictive control of permanent magnet toroidal motor (PMTM). Based on the spatial structure characteristics and working principle of toroidal motor, the time-varying inductance parameters are derived. The nonlinear mathematical model of toroidal motor is established in rotating coordinate system. The output fluctuation of the toroidal motor is analyzed. To improve the output performance of toroidal motor, the full predictive cascade speed and current control (F-MPC) strategy for toroidal motor is proposed. For F-MPC of toroidal motor, the finite control set model predictive current control (FCS-MPCC) strategy with delay compensation is adopted in current inner loop, and the deadbeat model predictive speed control (DB-MPSC) strategy is adopted in speed outer loop. Meanwhile, to achieve DB-MPSC, the reduced-order Luenberger observer is designed to obtain load torque in real time. F-MPC is the combination of current-speed prediction and Luenberger observer; it can overcome the limitation of excessive parameter adjustment of traditional cascade PI (Proportional Integral) controller. The simulation results show that compared with FOC (Field Oriented Control) and FCS-MPCC, F-MPC can improve the dynamic response and anti-interference performance of toroidal motor, and it can also reduce the fluctuation of output speed and torque significantly.

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Section: Full Predictive Control Of Pmtmmentioning

confidence: 99%

Full prediction cascade control of permanent magnet toroidal motor with time-varying parameters

Liu

Wang

2023

Transactions of the Institute of Measurement and Control

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“…the equation for i * d reference current can be expressed in terms of i * q reference current as [28]:…”

Section: Maximum Torque Per Armature (Mtpa)mentioning

confidence: 99%

Low-Speed Transient and Steady-State Performance Analysis of IPMSM for Nonlinear Speed Regulator with Effective Compensation Scheme

Usama

Kim

2021

Energies

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The speed response of the interior permanent magnet synchronous motor (IPMSM) drive at low speeds was analyzed. To eliminate the effect of external disturbance or parameter uncertainty, a nonlinear speed control loop was designed based on the sliding-mode exponential reaching law, which reduces chatter, which is the major drawback of the constant reaching law sliding-mode control technique. The proposed nonlinear speed control eliminates speed ripples at low speed under load disturbance. The problem of speed convergence at low speed is caused by electromagnetic torque ripples, which cause shaft speed oscillations that affect drive performance. The main objective of the proposed method is to change the traditional IPMSM control design by compensating with an appropriate signal along the reference current and across the output of the speed control loop. To optimize the speed tracking performance during disturbances or parametric variations, a nonlinear speed control scheme is designed that can vigorously adapt to the change in the controlled system. The comparative analysis shows that the method provides excellent transient performance (e.g., fast convergence response, less overshoot, and fast settling time) and standstill performance (e.g., reduced steady-state error) compared with conventional control methods at low speed under varying load conditions. The method is easy to implement and does not require additional computational cost. To demonstrate the effectiveness and feasibility of the design approach, a numerical analysis was conducted, and the control scheme was verified using MATLAB/Simulink considering various operating conditions.

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“…However, the excellent performance of the system cannot be guaranteed because of non-linearities in the dynamic model equations caused by the non-linear features of the magnets and cross-coupling between the state variables [ 3 , 4 ]. Consequently, numerous control approaches have been proposed in recent years including predictive current control, artificial neural network (ANN) control, direct torque control, robust hysteresis current control, and H∞ control [ 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. The predictive current control method in [ 5 ] predicts the current of the next sample and shows the fast convergence of the reference current to the actual motor phase current.…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, numerous control approaches have been proposed in recent years including predictive current control, artificial neural network (ANN) control, direct torque control, robust hysteresis current control, and H∞ control [ 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. The predictive current control method in [ 5 ] predicts the current of the next sample and shows the fast convergence of the reference current to the actual motor phase current. However, the performance degrades with the parameter uncertainties [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Non-Linear Current Controller Based on Deep Symbolic Regression for SPMSM

Usama

Lee

2022

Sensors

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This study designs a simple current controller employing deep symbolic regression (DSR) in a surface-mounted permanent magnet synchronous machine (SPMSM). A novel DSR-based optimal current control scheme is proposed, which after proper training and fitting, generates an analytical dynamic numerical expression that characterizes the data. This creates an understandable model and has the potential to estimate data that have not been seen before. The goal of this study was to overcome the traditional linear proportional–integral (PI) current controller because the performance of the PI is highly dependent on the system model. Moreover, the outer speed control loop gains are tuned using the cuckoo search algorithm, which yields optimal gain values. To demonstrate the efficacy of the proposed design, we apply the control design to different test cases, that is varied speed and load conditions, as well as sinusoidal speed reference, and compare the results with those of a traditional vector control design. Compared with traditional control approaches, we deduce that the DSR-based control design could be extrapolated far beyond the training dataset, laying the foundation for the use of deep learning techniques in power conversion applications.

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Robust adaptive observer-based finite control set model predictive current control for sensorless speed control of surface permanent magnet synchronous motor

Cited by 9 publications

References 38 publications

Full prediction cascade control of permanent magnet toroidal motor with time-varying parameters

Full prediction cascade control of permanent magnet toroidal motor with time-varying parameters

Low-Speed Transient and Steady-State Performance Analysis of IPMSM for Nonlinear Speed Regulator with Effective Compensation Scheme

Data-Driven Non-Linear Current Controller Based on Deep Symbolic Regression for SPMSM

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