Cerebellar-Inspired Learning Rule for Gain Adaptation of Feedback Controllers

TL;DR

This paper introduces a novel cerebellar-inspired adaptive control scheme called ME-LMS, which uses a forward model to adapt feedback gains, demonstrating its effectiveness across various control problems.

Contribution

The paper proposes the Model-Enhanced Least Mean Squares (ME-LMS) algorithm, extending cerebellar learning principles to adaptive control of feedback gains with a forward model approach.

Findings

ME-LMS effectively adapts feedback gains in control systems.

The approach generalizes cerebellar learning mechanisms to broader adaptive control.

Results show improved adaptation performance over traditional methods.

Abstract

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.