A Design of Optimal Interval between Armatures in Long Distance Transportation PMLSM for End Cogging Force Reduction

Park, Eui-Jong; Jung, Sang-Yong; Kim, Yong-Jae

doi:10.5370/jeet.2016.11.2.361

Cited by 8 publications

(7 citation statements)

References 5 publications

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“…This means within the range of the sliding surface, the trajectory will reach the sliding surface in finite time and satisfy the local arrival condition. The equivalent form of the arrival condition is expressed as sṡ < 0 (10) In order to ensure the arrival within finite time and avoid asymptotic approximation, (10) can be modified as [29] ṡ > ε,…”

Section: A Csmc Designmentioning

confidence: 99%

“…Especially in the fields of CNC with high speed and precision, the requirements of servo system are even higher. In order to eliminate the uncertainties existing in the system and realize high precision servo performance of PMLSM, some researchers have proposed many control strategies, such as sliding mode control (SMC), backstepping control, adaptive control, and other intelligent control including expert control and neural network [10], [11].…”

Section: Introductionmentioning

confidence: 99%

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Complementary Sliding Mode Control via Elman Neural Network for Permanent Magnet Linear Servo System

Huang

Zhao

2019

IEEE Access

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The permanent magnet linear servo system is usually susceptible to uncertainties, such as parameter variations, external disturbances, and friction forces. To address this problem, a complementary sliding mode control (CSMC) via Elman neural network (ENN) was presented in this paper. First, the mathematical model of the permanent magnet linear synchronous motor (PMLSM) with a lumped uncertainty was established. Second, on the basis of the traditional sliding mode control (SMC), CSMC was designed by combining the integral sliding surface with the complementary sliding surface. CSMC is generally used to reduce the chattering phenomenon and, consequently, to improve the tracking performance. However, the values of the switching gain and the boundary layer thickness are difficult to select in CSMC. To deal with this problem, ENN was adopted in the proposed CSMC system to replace the switching control law. Due to its strong learning ability, ENN can estimate the value of the lumped uncertainty and adjust the parameters online, thus further improving the robustness of the system. In addition, to verify the control performance of the proposed method, a digital signal processor (DSP) was implemented as the experimental platform to control the mover of the PMLSM for the tracking of different reference trajectories. The experimental results show that the proposed control strategy not only improves tracking accuracy but also guarantees the robustness of the system.

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Section: A Csmc Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Complementary Sliding Mode Control via Elman Neural Network for Permanent Magnet Linear Servo System

Huang

Zhao

2019

IEEE Access

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“…However, uncertain disturbances, such as friction, magnetic resistance, and external disturbances directly affect the mover of a PMLSM without attenuation, leading to substantial tracking errors and complex disturbances during motor movements. This increases the difficulty of controlling the PMLSM servo system and degrades the operational performance [4], [5]. With increasingly stringent requirements in terms of operational efficiency and machining quality, simultaneous reduction in motion tracking errors and disturbances has become a key technical requirement.…”

Section: Introductionmentioning

confidence: 99%

Extended State Observer-Based IMC-PID Tracking Control of PMLSM Servo Systems

et al. 2021

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Servo systems driven by a permanent magnet linear synchronous motor (PMLSM) are often affected by uncertain disturbances, such as magnetic resistance, friction, and external disturbances, which increase tracking errors and reduce motion accuracy. Traditional control methods may have difficulty to achieve satisfactory control performance in terms of tracking accuracy and disturbance rejection. In this paper, we propose an internal model control PID method based on a model linear extended state observer, which is termed as IMC-PID-MLESO method. With this method, the nominal model parameters of the PMLSM servo system are obtained via system identification. The model linear extended state observer (MLESO), with known parameter information, is designed to improve the estimation accuracy for the system states and total unknown uncertainties. As the uncertainties are compensated in the feedback control law, the PMLSM servo system model is transformed into a known nominal model. Based on the nominal model, the IMC-PID feedback controller is designed to ensure satisfactory performance on disturbance rejection and tracking error reduction. Simulation and experimental results show that the IMC-PID-MLESO method can effectively improve the position tracking accuracy of the PMLSM servo system and rapidly suppress the system disturbance.INDEX TERMS Disturbance suppression, internal model control, permanent magnet linear synchronous motor, tracking accuracy, uncertainties.

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“…Industrial robots, semiconductor manufacturing, and similar technologies have benefited from innovations in terms of high‐precision accuracy [1]. As demands increase for the servo system, the permanent magnet linear synchronous motors (PMLSMs), which have many advantageous characteristics such as high reliability, low costs, and simpler structure, have been increasingly applied to precision applications [2].…”

Section: Introductionmentioning

confidence: 99%

Modified complementary sliding mode control with disturbance compensation for permanent magnet linear synchronous motor servo system

Huang

Zhao

Wang

2020

IET Electric Power Applications

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In this study, a modified complementary sliding mode control (MCSMC) method based on a disturbance force observer with mass identification (DFOB‐MI) applicable to the permanent magnet linear synchronous motor is proposed to achieve high‐performance servo control fields. MCSMC is an improvement on the complementary sliding mode control (CSMC) method, which incorporates an approach angle into the saturation function. MCSMC allows for asymptotic convergence of the position tracking errors and guarantees the global robustness of the system. In addition, compared to hybrid control strategies combining neural networks with CSMC, the MCSMC method has a simpler structure and faster response. However, in practical applications, the mass variation of the mover has a significant impact on system performance. To achieve better dynamic and static characteristics, a disturbance force observer capable of identifying the mass variation based on model reference adaptive identification theory is proposed. DFOB‐MI can identify the mass of the mover and provide information on the disturbance caused by the change of the load. Thus, the compensation current is calculated to reduce the disturbance and realise compensation. The more accurate tracking performance and stronger robustness of the proposed control scheme compared to conventional approaches have been confirmed through comparative experimental studies.

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A Design of Optimal Interval between Armatures in Long Distance Transportation PMLSM for End Cogging Force Reduction

Cited by 8 publications

References 5 publications

Complementary Sliding Mode Control via Elman Neural Network for Permanent Magnet Linear Servo System

Complementary Sliding Mode Control via Elman Neural Network for Permanent Magnet Linear Servo System

Extended State Observer-Based IMC-PID Tracking Control of PMLSM Servo Systems

Modified complementary sliding mode control with disturbance compensation for permanent magnet linear synchronous motor servo system

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