非最小相位系统的基函数型自适应迭代学习控制

针对重复运行的未知非最小相位系统的轨迹跟踪问题, 结合时域稳定逆特点, 提出了一种新的基函数型自适应迭代学习控制(Basis function based adaptive iterative learning control, BFAILC)算法. 该算法在迭代控制过程中应用自适应迭代学习辨识算法估计基函数模型, 采用伪逆型学习律逼近系统的稳定逆, 保证了迭代学习控制的收敛性和鲁棒性. 以傅里叶基函数为例, 通过在非最小相位系统上的控制仿真, 验证了算法的有效性.

Basis Function Based Adaptive Iterative Learning Control for Non-minimum Phase Systems

Combined with stable inversion, a new basis function based adaptive iterative learning control(BFAILC) algorithm is proposed to track the desired output trajectory for repetitive non-minimum phase systems. In this method, an adaptive iterative identification algorithm is designed to estimate the system's basis function space model, and a pseudo inverse type learning law is used to approximate the stable inversion of the non-minimum phase system, which guarantees the convergence and robustness of the control system. Using an extended time-domain Fourier basis function as an example, the performance and effectiveness of the proposed algorithm are verified through numerical simulations for the non-minimum phase system.