输入受限下永磁同步电机随机系统自适应控制OA

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.