Online Autotuning Technique for IPMSM Servo Drive by Intelligent Identification of Moment of Inertia

Lin, Faa-Jeng; Chen, Shih-Gang; Ли, Шуай; Chou, Hsiao-Tse; Lin, Jyun-Ru

doi:10.1109/tii.2020.2965194

Cited by 32 publications

(12 citation statements)

References 30 publications

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“…and the complexity on the load side increase (cascaded mechanical contacts), the number of loops in the control and tuning blocks increases and this situation requires FPGA-based solutions. A summarized comparison hardware systems and embedded applications in the literature are given in Table 2 [21], [36], [69], [70]. In an optimized embedded system design, the industrial drive, parameter calculation and auto-tuning operations are divided into software blocks called macros.…”

Section: System Managementmentioning

confidence: 99%

Review on Embedded Methods in Parameter Estimation and Auto-tuning of Permanent Magnet Synchronous Servo Machine Driven Industrial Systems

Özküçük¹

2021

Preprint

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Section: System Managementmentioning

confidence: 99%

Review on Embedded Methods in Parameter Estimation and Auto-tuning of Permanent Magnet Synchronous Servo Machine Driven Industrial Systems

Özküçük¹

2021

Preprint

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“…Among (11), ε(k) is the difference between the two model outputs. β is a key calculation parameter, which has an obvious impact on the speed and accuracy of the identification algorithm.…”

Section: Mras Identification Modelmentioning

confidence: 99%

“…The change of load will also affect the moment of inertia converted to the motor shaft side [9]. In order to enable the joint motor to adapt to changing working conditions, it is necessary to make the motor control system have a high dynamic identification ability for the moment of inertia [10,11]. Therefore, to solve such problems, it is necessary to quickly and accurately identify the moment of inertia to further improve the servo performance of the motor control system.…”

Section: Introductionmentioning

confidence: 99%

Research on Dynamic Identification of Servo Motor Load Inertia Based on the Error Gain Factor Model

Xie

2021

Energies

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Aiming at solving the problems of slow motion and positioning deviation caused by the change of the moment of inertia of the servo motor due to different loads, an identification method for the moment of inertia on the basis of the error gain factor model is introduced into the controller, so that the moment of inertia can be obtained accurately and quickly under dynamic conditions. First, the electromagnetic and motion equation of the permanent magnet synchronous motor is built, and the logical relationship between the moment of inertia, torque, speed and other physical quantities is derived, so that the moment of inertia can be dynamically acquired. Second, in order to increase the identification accuracy, an adaptive function is introduced in the inertia identification model to replace the fixed parameters as an error gain factor (EGF). Third, the accuracy parameter is defined, and the identification algorithm on the basis of the EGF model is compared with the accuracy parameters of the existing identification method, which verifies that the improved algorithm has a better accuracy and speed. Finally, on the experimental platform, the working condition of unsteady speed is simulated. It is further verified that the proposed method has a high anti-interference capability.

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“…Finally, compared to the methods mentioned above, the neural network is more as an optimization tool to make the result of parameter identification even more excellent. In [ 21 ], a real-time moment of inertia, the identification technique using a Petriprobabilistic fuzzy neural network with an asymmetric membership function was proposed by combining the neural network and integral method to optimize the identification results for the servo drive system.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Parameter Identification for Robot Servo Controller Based on Improved Integration Method

Wang

Zhou

et al. 2021

Sensors

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With the rise of smart robots in the field of industrial automation, the motion control theory of the robot servo controller has become a research hotspot. The parameter mismatch of the controller will reduce the efficiency of the equipment and damage the equipment in serious cases. Compared to other parameters of servo controllers, the moment of inertia and friction viscous coefficient have a significant effect on the dynamic performance in motion control; furthermore, accurate real-time identification is essential for servo controller design. An improved integration method is proposed that increases the sampling period by redefining the update condition in this paper; it then expands the applied range of the classical method that is more suitable for the working characteristics of a robot servo controller and reducesthe speed quantization error generated by the encoder. Then, an optimization approach using the incremental probabilistic neural network with improved Gravitational Search Algorithm (IGSA-IPNN) is proposed to filter the speed error by a nonlinear process and provide more precise input for parameter identification. The identified inertia and friction coefficient areused for the PI parameter self-tuning of the speed loop. The experiments prove that the validity of the proposed method and, compared to the classical method, it is more accurate, stable and suitable for the robot servo controller.

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Online Autotuning Technique for IPMSM Servo Drive by Intelligent Identification of Moment of Inertia

Cited by 32 publications

References 30 publications

Review on Embedded Methods in Parameter Estimation and Auto-tuning of Permanent Magnet Synchronous Servo Machine Driven Industrial Systems

Review on Embedded Methods in Parameter Estimation and Auto-tuning of Permanent Magnet Synchronous Servo Machine Driven Industrial Systems

Research on Dynamic Identification of Servo Motor Load Inertia Based on the Error Gain Factor Model

Intelligent Parameter Identification for Robot Servo Controller Based on Improved Integration Method

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