---
title: "Learning to Tune with Biased Gradients: Iterative Feedback Tuning of Model-Free Control on Throttle Valves"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.06338v1"
permalink: "https://feed.astralor.com/items/itm_c5679ca17327ac231b42b712"
published: "2026-11-04T11:01:04.000Z"
score: 60
channel: "tech"
tags: ["Learning","Tune","Biased Gradients","Iterative Feedback Tuning","Model-Free Control","Throttle Valves Iterative","IFT","Iterative"]
---

# Learning to Tune with Biased Gradients: Iterative Feedback Tuning of Model-Free Control on Throttle Valves

## At a glance

Iterative feedback tuning (IFT) learns the parameters of a fixed-structure controller by stochastic gradient descent. At each iteration, two closed-loop experiments are run on the plant: the first one measures the track…

## Key points

- Iterative feedback tuning (IFT) learns the parameters of a fixed-structure controller by stochastic gradient descent.
- At each iteration, two closed-loop experiments are run on the plant: the first one measures the tracking error, and the second one, driven by this error, provides the sensitivities from which the gradient of the cost is…
- This estimate is unbiased for linear plants only.

## Full text

Iterative feedback tuning (IFT) learns the parameters of a fixed-structure controller by stochastic gradient descent. At each iteration, two closed-loop experiments are run on the plant: the first one measures the tracking error, and the second one, driven by this error, provides the sensitivities from which the gradient of the cost is estimated. This estimate is unbiased for linear plants only. We study the biased case on automotive electronic throttle valves, whose friction and stiction vary between valves and runs, controlled by an intelligent proportional (model-free) controller, which is an incremental PI controller. We show that the mismatch of the controller state at the start of the two experiments, which stiction makes unavoidable, adds an error inversely proportional to the amplitude of the second experiment. On digital twins of ten valves, the IFT gradient is a descent direction in only 69\% of the low-cost region, friction being the cause, and all update rules plateau and then drift. On two hardware valves, the best update rule depends on the noise structure of the gradient. On the first valve, where one gradient component is buried in noise, Adam outperforms Gauss--Newton, its normalisation and its momentum each improving one parameter. On the second valve, where both components are consistent, the same normalisation amplifies a direction that Gauss--Newton rightly discounts, and Adam drifts to a controller worse than the initial one. The twins reproduce the mechanism but not this ranking.

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