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针对一类带有非严格重复的未知参数的离散时间非线性系统跟踪迭代域变化的参考轨迹问题,考虑系统中存在未知时变控制增益和时变外部扰动的情况,提出了一种鲁棒自适应迭代学习控制算法.迭代域变化的未知参数由高阶内模产生.系统中还存在迭代域任意有界变化的状态初值问题.采用设计迭代轴观测器的方法,对产生未知参数的高阶内模的基函数进行估测.严格的理论推导证明了跟踪误差在有限时间区间上沿迭代轴的渐近收敛性.多个仿真实例表明了基于高阶内模的鲁棒自适应迭代学习算法的有效性.“,”Considering the unknown time-varying input gain and exogenous disturbances, a robust adaptive iterative learning control (AILC) algorithm is proposed to track iteration-varying trajectories for a class of discrete-time nonlinear systems with non-repetitive uncertainty. The iteratively varying unknown parameter is generated by high-order internal model (HOIM). Random bounded initial condition is also considered in this work. Using estimation in the iteration domain, a set of unknown basis functions of HOIM are estimated and updated. The rigorous proof is presented to show the tracking error converged to zero asymptotically along the iteration axis in finite time interval. The simulation results indicate the efficacy of the proposed HOIM-based robust AILC approach.