Perfect tracking of the tip position of a flexible-link manipulator(FLM) is unable to be achieved by causal control because it is a typical non-minimum phase system. Combined with non-causal stable inversion, an adaptive iterative learning control scheme based on Fourier basis function is presented for the tip trajectory tracking of FLM performing repetitive tasks. In this method,an iterative identification algorithm is used to construct the Fourier basis function space model of the manipulator, and a pseudoinverse type iterative learning law is designed to approximate the stable inversion of the non-minimum phase system, which guarantees the convergence and robustness of the control system. Simulation results show the performance and effectiveness of the proposed scheme.
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.
