控制理论与应用2026，Vol.43Issue(1)：69-78,10.DOI:10.7641/CTA.2025.50087
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
Abstract
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.关键词
自适应控制/输入受限/永磁同步电机/随机系统/dSPACEKey words
adaptive control/input constraints/permanent magnet synchronous motor/stochastic systems/dSPACE引用本文复制引用
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):69-78,10.基金项目
国家重点研发计划项目(2022YFB3404804)资助.Supported by the National Key Research and Development Program of China(2022YFB3404804). （2022YFB3404804）
