小脑启发的学习规则用于反馈控制器的增益适应
Cerebellar-Inspired Learning Rule for Gain Adaptation of Feedback Controllers
AI总结：
本文提出基于小脑原理的反馈控制器增益自适应方法，通过引入前向模型实现对子系统动态的匹配，扩展了传统LMS算法以解决广泛适应控制问题。
摘要：
我们的神经系统如何在不同肌肉骨骼系统广泛响应动态下成功获取反馈控制策略？小脑是动物精确运动控制的关键脑结构。最新进展表明，小脑浦肯野细胞的突触可塑性涉及分子机制，这些机制模仿了它们控制的效应器运动系统的动态，使它们的学习规则能够匹配行为时间。反事实预测控制（CFPC）是一种基于小脑的前馈控制方案，利用该原理获取预见性动作。CFPC扩展了经典的 Widrow-Hoff/最小均方算法，通过在学习规则中插入下游闭环系统的前向模型。本文将相同洞察应用于反馈控制器增益学习问题。为此，我们提出一个模型参考自适应控制（MRAC）问题，并推导出将反馈控制器增益视为适应性线性单元权重的自适应控制方案。我们的结果表明，通过将子系统动态的前向模型用于控制可塑性，即我们称为模型增强最小均方（ME-LMS）的方法，不仅限于小脑学习，可以解决广泛适应控制问题。
英文摘要：
How does our nervous system successfully acquire feedback control strategies in spite of a wide spectrum of response dynamics from different musculo-skeletal systems? The cerebellum is a crucial brain structure in enabling precise motor control in animals. Recent advances suggest that synaptic plasticity of cerebellar Purkinje cells involves molecular mechanisms that mimic the dynamics of the efferent motor system that they control allowing them to match the timing of their learning rule to behavior. Counter-Factual Predictive Control (CFPC) is a cerebellum-based feed-forward control scheme that exploits that principle for acquiring anticipatory actions. CFPC extends the classical Widrow-Hoff/Least Mean Squares by inserting a forward model of the downstream closed-loop system in its learning rule. Here we apply that same insight to the problem of learning the gains of a feedback controller. To that end, we frame a Model-Reference Adaptive Control (MRAC) problem and derive an adaptive control scheme treating the gains of a feedback controller as if they were the weights of an adaptive linear unit. Our results demonstrate that rather than being exclusively confined to cerebellar learning, the approach of controlling plasticity with a forward model of the subsystem controlled, an approach that we term as Model-Enhanced Least Mean Squares (ME-LMS), can provide a solution to wide set of adaptive control problems.
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