阶跃负载下永磁同步电机动态预定性能模糊神经反步控制

IF 6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xuechun Hu , Yu Xia , Zsófia Lendek , Jinde Cao , Radu-Emil Precup
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引用次数: 0

摘要

为了满足具有时变模型参数和输入约束的永磁同步电机系统在阶跃负载下的性能要求,提出了一种动态规定性能模糊神经反步控制方法。首先,提出了一种新的有限时间非对称动态规定性能函数(FADPPF),解决了传统规定性能函数在负荷变化下出现的超预定误差、控制奇异性和系统不稳定等问题。针对永磁同步电机系统中非线性时变参数和输入约束导致的模型精度下降和控制质量下降的问题,将速度函数(SF)、模糊神经网络(FNN)和所提出的FADPPF相结合,设计了一种反步控制器。FNN逼近系统模型中的非线性不确定函数;作为一种误差放大机制,自适应滤波与FADPPF共同保证系统的暂态和稳态性能。利用李雅普诺夫分析证明了所设计控制策略的稳定性。仿真结果验证了FADPPF在阶跃负载下的动态自调节能力和有效性。最后,验证了所提控制方案的可行性和优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel dynamic prescribed performance fuzzy-neural backstepping control for PMSM under step load
In order to meet the performance requirements of permanent magnet synchronous motor (PMSM) systems with time-varying model parameters and input constraints under step load, this paper proposes a dynamic prescribed performance fuzzy-neural backstepping control approach. Firstly, a novel finite-time asymmetric dynamic prescribed performance function (FADPPF) is proposed to tackle the issues of exceeding predefined error, control singularity, and system instability that arise in the traditional prescribed performance function under load changes. To address model accuracy degradation and control quality deterioration caused by nonlinear time-varying parameters and input constraints in the PMSM system, a backstepping controller is designed by combining the speed function (SF), fuzzy neural network (FNN), and the proposed FADPPF. The FNN approximates nonlinear uncertain functions in the system model; the SF, as an error amplification mechanism, works together with FADPPF to ensure the transient and steady-state performance of the system. The stability of the devised control strategy is proved using Lyapunov analysis. Finally, simulation results demonstrate the dynamic self-adjusting ability and effectiveness of FADPPF under step load. In addition, the feasibility and superiority of the proposed control scheme are validated.
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来源期刊
Neural Networks
Neural Networks 工程技术-计算机:人工智能
CiteScore
13.90
自引率
7.70%
发文量
425
审稿时长
67 days
期刊介绍: Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.
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