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Credit assigned CMAC and its application to online learning robust controllers

机译:信用分配的CMAC及其在在线学习鲁棒控制器中的应用

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摘要

In this paper, a novel learning scheme is proposed to speed up the learning process in cerebellar model articulation controllers (CMAC). In the conventional CMAC learning scheme, the correct numbers of errors are equally distributed into all addressed hypercubes, regardless of the credibility of the hypercubes. The proposed learning approach uses the inverse of learned times of the addressed hypercubes as the credibility (confidence) of the learned values, resulting in learning speed becoming very fast. To further demonstrate online learning capability of the proposed credit assigned CMAC learning scheme, this paper also presents a learning robust controller that can actually learn online. Based on robust controllers presented in the literature, the proposed online learning robust controller uses previous control input, current output acceleration, and current desired output as the state to define the nominal effective moment of the system from the CMAC table. An initial trial mechanism for the early learning stage is also proposed. With our proposed credit-assigned CMAC, the robust learning controller can accurately trace various trajectories online.
机译:本文提出了一种新颖的学习方案,以加快小脑模型关节控制器(CMAC)的学习过程。在常规的CMAC学习方案中,无论超立方体的可信度如何,正确数量的错误均等地分配到所有寻址的超立方体中。所提出的学习方法使用寻址的超立方体的学习时间的倒数作为学习值的可信度(置信度),从而导致学习速度变得非常快。为了进一步证明拟议的学分分配CMAC学习方案的在线学习能力,本文还提出了一种可以实际在线学习的学习鲁棒控制器。基于文献中提出的鲁棒控制器,提出的在线学习鲁棒控制器使用先前的控制输入,当前输出加速度和当前期望的输出作为状态,以从CMAC表定义系统的标称有效力矩。还提出了针对早期学习阶段的初步试用机制。利用我们提出的信用分配的CMAC,强大的学习控制器可以准确地在线跟踪各种轨迹。

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