Optimal Sliding Mode Robust Control for Fractional-Order Systems with Application to Permanent Magnet Synchronous Motor Tracking Control

Lan, Yong‐Hong; Wang, Liangliang; Chen, Caixue; Ding, Lei

doi:10.1007/s10957-015-0827-4

Cited by 7 publications

(3 citation statements)

References 21 publications

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“…Neural network methods are a powerful tool for real-time parallel processing that can be utilized to solve challenging nonlinear systems, particularly for situations in which an analytical solution is elusive. These methods have found application in various domains, including non-linear control problems (Xiao et al, 2019g;Li et al, 2020c;Jia et al, 2021), non-linear differential equations (Zhang et al, 2017(Zhang et al, , 2018dLiao et al, 2021), and nonlinear optimization problems (Liu et al, 2016;Lan et al, 2017;Xiao et al, 2019a;.…”

Section: Neural Network For Non-linear System Solvingmentioning

confidence: 99%

Advances on intelligent algorithms for scientific computing: an overview

et al. 2023

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The field of computer science has undergone rapid expansion due to the increasing interest in improving system performance. This has resulted in the emergence of advanced techniques, such as neural networks, intelligent systems, optimization algorithms, and optimization strategies. These innovations have created novel opportunities and challenges in various domains. This paper presents a thorough examination of three intelligent methods: neural networks, intelligent systems, and optimization algorithms and strategies. It discusses the fundamental principles and techniques employed in these fields, as well as the recent advancements and future prospects. Additionally, this paper analyzes the advantages and limitations of these intelligent approaches. Ultimately, it serves as a comprehensive summary and overview of these critical and rapidly evolving fields, offering an informative guide for novices and researchers interested in these areas.

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Section: Neural Network For Non-linear System Solvingmentioning

confidence: 99%

Advances on intelligent algorithms for scientific computing: an overview

et al. 2023

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show abstract

“…The parameter

δ

may be used to improve the control gain matrix

K_{p}

and the parameter

β

is used to adjust the gain parameter

K_{e}

. Thus, the parameters are chosen in order to get better tracking performance. Example 2 In this example, we consider the permanent magnet synchronous motor control system whose dynamics is described in [28]. The system parameters from [28] are considered such as

R = 0.56 Ω

p = 3

J = 0.0021 {kg normalm}^{2}

ϕ_{f} = 0.82 Wb

l = 0.0153 H

and

b = 0.0001

.…”

Section: Simulation Verificationmentioning

confidence: 99%

“…Thus, the parameters are chosen in order to get better tracking performance. Example 2 In this example, we consider the permanent magnet synchronous motor control system whose dynamics is described in [28]. The system parameters from [28] are considered such as