| 引用本文: | 舒永东,杜鹏,李俊阳.输入受限下永磁同步电机随机系统自适应控制[J].控制理论与应用,2026,43(1):69~78.[点击复制] |
| SHU Yong-dong,DU Peng,LI Jun-yang.Adaptive control of permanent magnet synchronous motor stochastic systems with input constraints[J].Control Theory & Applications,2026,43(1):69~78.[点击复制] |
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| 输入受限下永磁同步电机随机系统自适应控制 |
| Adaptive control of permanent magnet synchronous motor stochastic systems with input constraints |
| 摘要点击 205 全文点击 31 投稿时间：2025-03-07 修订日期：2026-01-01 |
| 查看全文 查看/发表评论 下载PDF阅读器 HTML |
| DOI编号 10.7641/CTA.2025.50087 |
| 2026,43(1):69-78 |
| 中文关键词 自适应控制 输入受限 永磁同步电机 随机系统 dSPACE |
| 英文关键词 adaptive control input constraints permanent magnet synchronous motor stochastic systems dSPACE |
| 基金项目 国家重点研发计划项目(2022YFB3404804)资助. |
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| 中文摘要 |
| 永磁同步电机因其高效能和优良动态特性而在工业领域得到广泛应用,但其控制性能常受建模误差、随机
干扰及输入饱和等因素影响.针对这一问题,本文提出了一种基于径向基神经网络的自适应控制策略.该方法首先
建立包含建模误差和随机干扰的动力学模型,并利用饱和函数处理输入约束;通过径向基神经网络在线逼近未知
非线性并设计自适应律实现参数动态调整;采用非递归跟踪微分器避免传统反步法中的“微分爆炸”;进一步引入补
偿机制以削弱滤波误差和饱和误差的影响.在随机系统Lyapunov稳定性理论下,严格证明了闭环系统误差依概率一
致最终有界.数值仿真与基于dSPACE平台的半实物实验结果表明,该控制策略能够在输入受限条件下保持良好的
鲁棒性与控制性能. |
| 英文摘要 |
| Permanent magnet synchronous motors (PMSM) are widely used in industrial applications due to their high
efficiency and favorable dynamic characteristics. However, its control performance is often limited by modeling uncer
tainties, stochastic disturbances, and input saturation. To address these challenges, this paper proposes an adaptive control
strategy based on radial basis function neural network (RBFNN). A stochastic PMSM model incorporating modeling errors
and stochastic disturbances is constructed, while input saturation is handled through a saturation function. The RBFNN is
employed to approximate unknown nonlinearities online, and adaptive laws are designed for parameter adjustment. A non
recursive tracking differentiator is introduced to avoid the “complexity explosion” problem in conventional backstepping,
and a compensation mechanism is further developed to mitigate filtering and saturation errors. Based on Lyapunov stability
theory for stochastic systems, it is rigorously proven that all system errors are probabilistically uniformly ultimately bound
ed. Numerical simulations and semi-physical experiments on dSPACE platform validate the effectiveness of the proposed
control strategy, demonstrating robust performance under input constraints. |