Backstepping direct thrust force control for sensorless PMLSM drive

Ting, Chen‐Sheng; Chang, Yong‐Nong; Chen, Yen-Yo

doi:10.1049/iet-epa.2018.5269

Cited by 13 publications

(6 citation statements)

References 24 publications

(61 reference statements)

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“…....................... (11) When the state x is controlled in the region , x S the observation is stable under the condition of x S x  for all time. The Lyapunov functional candidate for building SOS observer is chosen as a conventional form…”

Section: Define the Estimation Error Asmentioning

confidence: 99%

“…Based on the current control strategy, a backstepping thrust controller is proposed to realize the position servo of nonlinear PMLSM. Finally, to demonstrate the effectiveness of proposed control scheme, a recent work on sensorless direct thrust control (11) is adopted as a comparison study. Experimental results show that this method has satisfactory tracking performance for different prescribed trajectories.…”

Section: Introductionmentioning

confidence: 99%

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Position Sensorless Control of PMLSM by Sum-of-Squares Approach

2022

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This paper presents an observer-based sensorless drive system for the position control of the permanent magnet linear synchronous motor (PMLSM).To achieve the purpose of position control and maintain the stability of the global system, the drive system is designed using the sum-of-squares (SOS) method. With the help of the SOSTOOLS toolbox, the control algorithm can be established without the need for complicated mathematical derivation. First, to apply the SOS technique, the motor model is treated as a fifth-order polynomial dynamics model. Based on this model, the state estimation of the full order Luenberger observer is studied. The feedback gain of the observer is obtained in polynomial form rather than a constant, thus making the observer nonlinear and time-varying. Compared with existing studies, the SOS observer construction requires no additional assumptions and no adaptive mechanisms to cope with changes in velocity. By combining the SOS method with the Lyapunov analysis, the observerbased control system is systematically developed to ensure the stability of the global system. The experimental results verify the proposed control scheme.

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Section: Define the Estimation Error Asmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Position Sensorless Control of PMLSM by Sum-of-Squares Approach

2022

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“…Parameter identification methods mainly include the least squares methods, 34,35 the sliding mode identification method, 36 the model reference adaptive method, 37,38 and the Kalman filter based methods, 39,40 and so forth. The Kalman filtering technique is a class of effective filtering methods for multi‐dimensional states and non‐stationary process with high estimation accuracy even in the presence of interference.…”

Section: Introductionmentioning

confidence: 99%

Convergence analysis of the modified adaptive extended Kalman filter for the parameter estimation of a brushless DC motor

Ding

Chen

Cao

et al. 2021

Intl J Robust & Nonlinear

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This article concentrates on the parameter estimation of brushless DC motor, where the stator current and winding back electromotive force are taken as the motor states, while the stator resistance and inductance are taken into consideration and augmented into the state vector. Based on this augmented model, a modified adaptive extended Kalman filter is proposed which updates the process noise covariance matrix in real time with the current input‐output data, and takes the state estimates by the traditional extended Kalman filter as one‐step estimation for the calculation of the covariance matrix. The convergence analysis is given to verify the theoretical results. Finally, the simulation results show that the proposed algorithm can effectively improve the estimation accuracy.

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“…With the substantial improvements of power electronics technology and high‐performance permanent magnetic materials, permanent magnet linear synchronous motor (PMLSM) has achieved extremely high attention [1]. Owing to the advantageous characteristics such as high reliability, low costs and simpler structure, the PMLSM has been widely used in transportation, aerospace, industrial automation and similar fields [2, 3].…”

Section: Introductionmentioning

confidence: 99%

Intelligent second‐order sliding mode control for permanent magnet linear synchronous motor servo systems with robust compensator

Zhao

Wang

Huang

2020

IET Electric Power Applications

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In this study, an intelligent second‐order sliding mode control (SMC) method combining second‐order SMC (SOSMC) and recurrent radial basis function neural network (RRBFNN) applicable to the permanent magnet linear synchronous motor (PMLSM) is proposed to achieve high‐performance servo control fields. On the basis of a dynamic model of PMLSM and the SMC theory, the chattering problem in SMC is weakened and the tracking accuracy is improved by the design of SOSMC. As for the boundary of the uncertainty factors is difficult to obtain, the optimal performance of SOSMC is hard to achieve, the RRBFNN uncertainty observer is introduced for estimating the value of the uncertainty factors. Owing to the strong learning ability, the network parameters can be trained online. Besides, a robust compensator is developed to suppress the uncertainties such as approximation error, optimal parameter vector and higher Taylor series for further improving the robustness. Moreover, the adaptive learning algorithms are obtained by using the Lyapunov theorem to guarantee the asymptotical stability of the system. The experiments demonstrate that the proposed scheme provides high performance dynamic characteristics and strong robustness to uncertainties.

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Backstepping direct thrust force control for sensorless PMLSM drive

Cited by 13 publications

References 24 publications

Position Sensorless Control of PMLSM by Sum-of-Squares Approach

Position Sensorless Control of PMLSM by Sum-of-Squares Approach

Convergence analysis of the modified adaptive extended Kalman filter for the parameter estimation of a brushless DC motor

Intelligent second‐order sliding mode control for permanent magnet linear synchronous motor servo systems with robust compensator

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