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针对广义系统的迭代学习算法设计过程中的收敛速度问题,基于广义系统的奇异值分解方法提出一类二阶迭代学习算法并借助于Q因子方法讨论了广义系统迭代学习控制算法的收敛速度问题.该方法可以将算法的收敛速度问题转化为Q因子值大小问题.数值仿真证明了迭代学习控制系统在有限次运行后收敛于期望轨迹,以及算法的收敛速度大小与算法增益矩阵取值有关.“,”To solve the convergence speed problem in the design process for iterative learning algorithms for singular systems, we propose a class of second-order iterative learning algorithms based on the singular value decomposition method for singular systems. In addition, we discuss the convergence rate for iterative learning control algorithms for singular systems with the help of the Q factor method. This method can transform the convergence speed of the algorithm into a Q factor size problem. The numerical simulation shows that the iterative learning control system converges to the desired trajectory after the finite operation, and the convergence speed of the algorithm is related to the value of the algorithm gain matrix.