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Iterative Learning Control of Nonlinear Non-Minimum Phase Systems and its Application to System and Model Inversion

机译:非线性非最小相位系统的迭代学习控制及其在系统与模型反演中的应用

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摘要

In this contribution we place ILC in the realm of numerical optimization. This clarifies the role played by the design variables and how they affect e.g. convergence properties. We give a model based interpretation of these design variables and also a sufficient condition for convergence of ILC which is similar in spirit to the sufficient and necessary condition previously derived for linear systems. This condition shows that the desired performance has to be traded against modelling accuracy. Finally, one of the main benefits of ILC when non-minimum phase systems are concerned, the possibility of non-causal control, is given a comprehensive coverage.
机译:在此贡献中,我们将ILC置于数值优化领域。这阐明了设计变量所起的作用以及它们如何影响例如收敛特性。我们对这些设计变量给出了基于模型的解释,并给出了ILC收敛的充分条件,其本质上类似于先前为线性系统得出的充分必要条件。这种情况表明,必须在期望的性能与建模精度之间进行权衡。最后,当涉及到非最小相位系统时,ILC的主要好处之一就是无因果控制的可能性得到了全面的介绍。

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