Skin effect impact on induction motor parameters estimation using an output-error identification method

Parallel robots present outstanding advantages compared with their serial counterparts; they have both a higher force-to-weight ratio and better stiffness. However, the existence of closed-chain mechanism yields difficulties in designing control system for practical applications, due to its highly coupled dynamics. This paper focuses on the dynamic model identification of the 6-DOF parallel robots for advanced model-based visual servoing control design purposes. A visual closed-loop output-error identification method based on an optical coordinate-measuring-machine (CMM) sensor for parallel robots is proposed. The main advantage, compared with the conventional identification method, is that the joint torque measurement and the exact knowledge of the built-in robot controllers are not needed. The time-consuming forward kinematics calculation, which is employed in the conventional identification method of the parallel robot, can be avoided due to the adoption of optical CMM sensor for real time pose estimation. A case study on a 6-DOF RSS parallel robot is carried out in this paper. The dynamic model of the parallel robot is derived based on the virtual work principle, and the built dynamic model is verified through Matlab/SimMechanics. By using an outer loop visual servoing controller to stabilize both the parallel robot and the simulated model, a visual closed-loop output-error identification method is proposed and the model parameters are identified by using a nonlinear optimization technique. The effectiveness of the proposed identification algorithm is validated by experimental tests.

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Accuracy Improvement of Stator Inductance Identification Method Based on Low-Frequency Current Injection for Three-Level NPC Inverter-Fed IM Drives in Locked-Rotor Standstill Condition

Electronics ◽

10.3390/electronics10040488 ◽

2021 ◽

Vol 10 (4) ◽

pp. 488

Author(s):

Yerganat Khojakhan ◽

Kyoung-Min Choo ◽

Junsin Yi ◽

Chung-Yuen Won

Keyword(s):

Induction Motor ◽

Low Frequency ◽

Current Injection ◽

Heavy Load ◽

Injection Test ◽

Low Speed ◽

Injection Speed ◽

Speed Test ◽

Identification Method ◽

Drive Systems

In this paper, a stator inductance identification process is proposed. The process is based on a three-level neutral-point-clamped (NPC) inverter-fed induction motor (IM) drive with a standstill condition. Previously, a low-speed alternating current (AC) injection test for stator inductance identification was proposed to overcome practical problems in conventional identification methods for three-level NPC inverter-based IM drives. However, the low-speed AC injection test-based identification method has some problems if a heavy load or mechanical brake is connected, as these can forcibly bring the rotor to a standstill during parameter identification. Since this low-speed testing-based identification assumes the motor torque is considerably lower in low-speed operations, some inaccuracy is inevitable in this kind of standstill condition. In this paper, the proposed current injection speed generator is based on the previously studied low-speed test-based stator inductance identification method, but the proposed approach gives more accurate estimates under the aforementioned standstill conditions. The proposed method regulates the speed for sinusoidal low-frequency AC injection on the basis of the instantaneous reactive and air-gap active power ratio. This proposed stator inductance identification method is more accurate than conventional fixed low-frequency AC signal injection identification method for three-level NPC inverter-fed IM drive systems with a locked-rotor standstill condition. The proposed method’s accuracy and reliability were verified by simulation and experiment using an 18.5 kW induction motor.

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Robust Sensorless Model-Predictive Torque Flux Control for High-Performance Induction Motor Drives

Mathematics ◽

10.3390/math9040403 ◽

2021 ◽

Vol 9 (4) ◽

pp. 403

Author(s):

Ahmed G. Mahmoud A. Aziz ◽

Hegazy Rez ◽

Ahmed A. Zaki Diab

Keyword(s):

Induction Motor ◽

High Performance ◽

Core Loss ◽

Parameters Estimation ◽

Torque Control ◽

Flux Control ◽

Control Approach ◽

Light Load ◽

Wide Range ◽

Lyapunov’S Theorem

This paper introduces a novel sensorless model-predictive torque-flux control (MPTFC) for two-level inverter-fed induction motor (IM) drives to overcome the high torque ripples issue, which is evidently presented in model-predictive torque control (MPTC). The suggested control approach will be based on a novel modification for the adaptive full-order-observer (AFOO). Moreover, the motor is modeled considering core losses and a compensation term of core loss applied to the suggested observer. In order to mitigate the machine losses, particularly at low speed and light load operations, the loss minimization criterion (LMC) is suggested. A comprehensive comparative analysis between the performance of IM drive under conventional MPTC, and those of the proposed MPTFC approaches (without and with consideration of the LMC) has been carried out to confirm the efficiency of the proposed MPTFC drive. Based on MATLAB® and Simulink® from MathWorks® (2018a, Natick, MA 01760-2098 USA) simulation results, the suggested sensorless system can operate at very low speeds and has the better dynamic and steady-state performance. Moreover, a comparison in detail of MPTC and the proposed MPTFC techniques regarding torque, current, and fluxes ripples is performed. The stability of the modified adaptive closed-loop observer for speed, flux and parameters estimation methodology is proven for a wide range of speeds via Lyapunov’s theorem.

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