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针对一阶非正则离散时间非线性系统追踪迭代域变化的参考轨迹问题,考虑系统中存在时变状态扰动和时变量测噪声的情况,提出了一种鲁棒迭代学习控制算法。迭代域变化的参考轨迹由高阶内模产生。该算法利用内模原理,先嵌入参考轨迹特征,然后针对时变扰动,用积分器剔除其影响。利用?范数,从理论上严格证明了系统跟踪误差的迭代域收敛性。对机械手模型的仿真结果表明了基于高阶内模的鲁棒迭代学习算法的有效性。
Aiming at the reference trajectory problem of first-order nonlinear time-varying discrete-time nonlinear systems tracking iterative domain changes, a robust iterative learning control algorithm is proposed considering the existence of time-varying state disturbances and time-varying noise in the system. The reference trajectory of the iteration domain change is generated by the high order mode. The algorithm utilizes the internal model principle to embed the reference trajectory characteristics first, and then remove the influence by the integrator for the time-varying disturbance. Using the norm, the iterative domain convergence of the system tracking error is strictly proved theoretically. The simulation results of the robot model show the validity of the robust iterative learning algorithm based on the high order internal model.