Iterative Learning Control for Nonlinear Nonminimum Phase Plants
Iterative Learning Control for Nonlinear Nonminimum Phase Plants
复制标题
非线性非最小相对象的迭代学习控制
DOI：
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发表时间：
2001
期刊：
影响因子：
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通讯作者：
B. Paden
中科院分区：
文献类型：
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作者：
J. Ghosh;B. Paden
学习控制是一种非常有效的跟踪控制方法，用于在固定时间间隔内重复发生的过程。本文针对一类具有扰动和初始化误差的非线性、非最小相位对象，提出了一种鲁棒学习算法。应用Devasia、Chen和Paden的“稳定逆”方法设计了一种线性非最小相位对象的学习控制器。这适用于更一般的非线性植物。通过一个简洁的证明展示了学习输入的渐近误差的界。仿真研究表明，在无输入干扰的情况下，非线性非最小相位对象可以实现对期望轨迹的完美跟踪。此外，在存在随机干扰的情况下，跟踪误差收敛到A界的一个邻域内，得到了跟踪误差是干扰界的连续函数的结论。还观察到，如果输入干扰在每次迭代时都相同，则可以实现对期望trajec的完美跟踪。@DOI：10.1115/1.1341200 #
Learning control is a very effective approach for tracking control in processes occur repetitively over a fixed interval of time. In this paper a robust learning algorithm proposed for a generic family of nonlinear, nonminimum phase plants with disturba and initialization error. The ‘‘stable-inversion’’ method of Devasia, Chen and Paden applied to develop a learning controller for linear nonminimum phase plants. Thi adapted to accommodate a more general class of nonlinear plants. The bounds o asymptotic error for the learned input are exhibited via a concise proof. Simula studies demonstrate that in the absence of input disturbances, perfect tracking o desired trajectory is achieved for nonlinear nonminimum phase plants. Further, in presence of random disturbances, the tracking error converges to a neighborhood of A bound on the tracking error is derived which is a continuous function of the boun the disturbance. It is also observed that perfect tracking of the desired trajec is achieved if the input disturbance is the same at every iteration. @DOI: 10.1115/1.1341200 #