首页> 外文会议>Decision and Control, 2000. Proceedings of the 39th IEEE Conference on >Iterative learning control using adjoint systems for nonlinear non-minimum phase systems
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Iterative learning control using adjoint systems for nonlinear non-minimum phase systems

机译:非线性非最小相位系统的伴随系统迭代学习控制

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Most iterative learning control (PLC) using a causal updating law obtains the input given by Silverman's (1969) or Hirshorn's (1979) causal inversion. When the objective system is that of a non-minimum phase, we cannot use those methods because the input is exponentially increasing. To overcome this difficulty, an approach called stable inversion was proposed to give a non-causal but bounded input instead. However, no simple iterative method to obtain this non-causal input was proposed. In this paper, from a viewpoint of minimization, we develop a simple iterative method for stable inversion toward ILC for non-minimum phase systems.
机译:大多数使用因果更新定律的迭代学习控制(PLC)获得Silverman(1969)或Hirshorn(1979)因果倒置给出的输入。当目标系统是非最小阶段的目标系统时,由于输入量呈指数增长,因此我们无法使用这些方法。为了克服这个困难,提出了一种称为稳定反演的方法来提供非因果但有界的输入。但是,没有提出获得这种非因果输入的简单迭代方法。本文从最小化的角度出发,针对非最小相位系统,开发了一种简单的迭代方法,可稳定地反转至ILC。

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