Adaptive neural network finite-time command filtered tracking control of fractional-order permanent magnet synchronous motor with input saturation

Lu, Senkui; Wang, Xingcheng; Li, Yanan

doi:10.1016/j.jfranklin.2020.10.021

Cited by 25 publications

(15 citation statements)

References 41 publications

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“…Compared with the asymptotically tracking control method, the finite‐time control method has many advantages such as faster convergence rate, higher precision and better disturbance rejection ability 24,25 . Therefore, the finite‐time control technology has been applied to PMSMs systems in References 26‐30. Moreover, the following finite‐time Lyapunov stability conditions

\dot{V}\le - aV-b{V}^{\gamma }+c

in Reference 31 and

\dot{V}\le -b{V}^{\gamma }+c

a,b,c>0

0<\gamma <1

in Reference 32 are used to discuss the convergence region

\Omega

.…”

Section: Introductionmentioning

confidence: 99%

Adaptive fuzzy gain‐varying finite‐time command filtered backstepping control for permanent magnet synchronous motors with mismatched disturbances

Wang

Zhao

2023

Adaptive Control & Signal

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This article investigates the position tracking control problem of permanent magnet synchronous motors (PMSMs) with mismatched disturbances based on adaptive fuzzy gain-varying finite-time command filtered backstepping method. First, the anti-disturbance ability of the system is improved by constructing gain-varying functions instead of the traditional static gains. Then, the command filter is adopted to overcome the explosion of complexity in classical backstepping method, and the gain-varying error compensation mechanisms are introduced to reduce the errors produced by command filters. In addition, the finite-time control technique makes the tracking error converges to a small neighborhood of origin in finite time. The fuzzy logic systems (FLSs) are used to approximate the nonlinear functions with strong coupling of PMSMs systems, and this article further utilizes the characteristics of FLSs to separate the states and handle the coupling of PMSMs systems. Finally, the simulation results prove the validity of the proposed strategy.

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\dot{V}\le - aV-b{V}^{\gamma }+c

in Reference 31 and

\dot{V}\le -b{V}^{\gamma }+c

a,b,c>0

0<\gamma <1

in Reference 32 are used to discuss the convergence region

\Omega

.…”

Section: Introductionmentioning

confidence: 99%

Adaptive fuzzy gain‐varying finite‐time command filtered backstepping control for permanent magnet synchronous motors with mismatched disturbances

Wang

Zhao

2023

Adaptive Control & Signal

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“…In a sensorless permanent magnet synchronous motor, we extract the parameters related to speed and position from the stator voltage and current, which are easy to measure at the stator edge, to replace the mechanical sensor, and to realize the motor closed-loop control (Xiong et al, 2016a;Xiong et al, 2016b;Yi et al, 2020). At present, the sensorless control technology of a permanent magnet synchronous motor is mainly divided into three categories: high-frequency signal injection method (Li and Wang, 2018;Xiong et al, 2020;Yu et al, 2021), motor model method (Chen and Pi, 2016;Zhong and Lin, 2017;Luo et al, 2019;Sun et al, 2019;He et al, 2020;Rongyn et al, 2020), and intelligent algorithm (Urbanski and Janiszewski, 2019;Lu et al, 2020;Ma et al, 2020). Since high-frequency signal injection methods may affect the current signal and many artificial intelligence methods are still in the stage of simulation and theoretical exploration, motor model-based methods are widely used and they have received considerable research attention in the past few years.…”

Section: Introductionmentioning

confidence: 99%

SMO-Based Sensorless Control of a Permanent Magnet Synchronous Motor

Liu¹,

Wang²,

Wang³

et al. 2022

Front. Energy Res.

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In order to reduce the chattering caused by the discontinuity of the control function in the traditional sliding mode observer (SMO), this article proposes a sliding mode observer with phase-locked loop (PLL) to estimate the speed and position of the rotor. The back electromotive force (EMF) of a permanent magnet synchronous motor (PMSM) in a static coordinate system is accurately estimated by SMO, and then, PLL is constructed to combine the observed rotor position angle and back EMF to compensate the phase lag in angle estimation so as to obtain a more accurate speed. It solves the problems of poor robustness and complex algorithms in the traditional SMO prediction algorithm. The simulation results show that the SMO with PLL can effectively reduce the system chattering and effectively improve the accuracy of rotor speed and position estimation.

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“…Thus, the finite-time dissipative problem has become increasingly valuable. [18][19][20][21] In practical engineering problems, we have to optimize the estimation of stochastic variables based on the observation process, which is called as the filtering problem. In the past few decades, Wiener filter, Kalman filter, and other filtering methods have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Finite-time dissipative filter design for discrete-time stochastic interval systems with time-varying delays

Chen

Zhou

Zhang

2022

Measurement and Control

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The finite-time dissipative filtering problem for a kind of discrete-time stochastic interval system with time-varying delays whose parameters are taken in a convex hull is investigated in this paper. Taking a representative subsystem from a stochastic convex hull system, based on convex analysis and matrix theory, a new interval matrix method is proposed to study the finite-time dissipative filter problem, which can deduct the conservativeness. Then, the finite-time dissipative filter is designed by employing a complex Lyapunov-Krasovskii functional together with the improved Wirtinger inequality technique. Correspondingly, some novel sufficient conditions are obtained to ensure the filtering error system with time-varying delays robustly stochastically finite-time bounded and the dissipative index is satisfied. Next, the desired filter gains are achieved in terms of linear matrix inequalities. Finally, the effectiveness of the designed filter is demonstrated by a numerical example with simulations.

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Adaptive neural network finite-time command filtered tracking control of fractional-order permanent magnet synchronous motor with input saturation

Cited by 25 publications

References 41 publications

Adaptive fuzzy gain‐varying finite‐time command filtered backstepping control for permanent magnet synchronous motors with mismatched disturbances

Adaptive fuzzy gain‐varying finite‐time command filtered backstepping control for permanent magnet synchronous motors with mismatched disturbances

SMO-Based Sensorless Control of a Permanent Magnet Synchronous Motor

Finite-time dissipative filter design for discrete-time stochastic interval systems with time-varying delays

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