论文标题
离散时间自适应迭代学习控制的多个估计模型
Multiple Estimation Models for Discrete-time Adaptive Iterative Learning Control
论文作者
论文摘要
本文着重于使用多个估计模型更有效地使离散时间自适应迭代学习控制（ILC）更有效。现有策略使用跟踪误差来调整参数估计。我们的策略使用识别误差的最后一个组件来调整模型参数的这些估计值。我们证明，这种策略会导致对参数的有限估计以及有限和收敛的识别和跟踪错误。我们强调的是，证明不使用关键的技术引理。相反，它使用方形可亮序的属性。我们将此策略扩展到包括多个估计模型，并表明所有信号都是有界的，并且错误会收敛。还表明，无论我们在每个瞬间，每次迭代还是在每次迭代的末尾都可以在模型之间切换，这都可以。仿真结果证明了该方法使用多个估计模型更快地收敛的功效。
This article focuses on making discrete-time Adaptive Iterative Learning Control (ILC) more effective using multiple estimation models. Existing strategies use the tracking error to adjust the parametric estimates. Our strategy uses the last component of the identification error to tune these estimates of the model parameters. We prove that this strategy results in bounded estimates of the parameters, and bounded and convergent identification and tracking errors. We emphasize that the proof does not use the key technical lemma. Rather, it uses the properties of square-summable sequences. We extend this strategy to include multiple estimation models and show that all the signals are bounded, and the errors converge. It is also shown that this works whether we switch between the models at every instant and every iteration or at the end of every iteration. Simulation results demonstrate the efficacy of the proposed method with a faster convergence using multiple estimation models.
