基于鲁棒迭代学习的永磁直线电机控制

针对永磁直线电机速度和位置迭代学习跟踪控制中,由测量扰动引起的跟踪误差有界收敛问题,提出一种带有衰减因子的鲁棒迭代学习控制算法.采用λ范数方法分析鲁棒迭代学习控制算法的收敛性,理论结果表明,鲁棒迭代学习控制算法可以保证跟踪误差收敛到零,而P型迭代学习控制算法仅保证直线电机速度和位置的跟踪误差收敛到一个与扰动信号上界有关的域内.仿真结果表明,所提出的鲁棒迭代学习控制算法可以有效抑制测量扰动,获得较好的跟踪性能.带有衰减因子的迭代学习控制方法是一种抑制非重复测量扰动的有效方法.

Robust iterative learning control for permanent magnet linear motor

When the measurement contains disturbance,the tracking error of iterative learning control for the speed and position tracking control of Permanent Magnet Linear Motor is bounded convergence.To suppress disturbance,a robust iterative learning control algorithm with a decreasing gain was proposed.The convergence analysis was given using λ norm.Theoretical analysis indicated that the P-type iterative learning control algorithm guaranteed the tracking error converges to a bound depending on the disturbance bound,and the robust iterative learning control algorithm could guarantee converges 0.Simulation results also illustrated the validity of measurement disturbance attenuation,and the better tracking performance was obtained.Hence,the robust iterative learning control algorithm with a decreasing gain is an effective approach attenuating non-repetitive disturbance.