# Automatic Temperature Setpoint Tuning of a Thermoforming Machine using Fuzzy Terminal Iterative Learning Control

## TL;DR

This paper introduces a fuzzy TILC method for thermoforming machine heater control, utilizing a first-order Takagi Sugeno model with matrices for easier inversion, leading to improved initial setpoint estimation and reduced plastic waste.

## Contribution

It develops a novel fuzzy TILC approach using a first-order Takagi Sugeno model with matrix rules, enhancing initial setpoint accuracy over traditional crisp TILC methods.

## Key findings

- Fuzzy TILC provides better initial heater setpoints.
- Simulation shows reduced plastic sheet wastage.
- Method outperforms crisp TILC despite noisy data.

## Abstract

This paper presents a new way to design a Fuzzy Terminal Iterative Learning Control (TILC) to control the heater temperature setpoints of a thermoforming machine. This fuzzy TILC is based on the inverse of a fuzzy model of this machine, and is built from experimental (or simulation) data with kriging interpolation. The Fuzzy Inference System usually used for a fuzzy model is the zero order Takagi Sugeno Kwan system (constant consequents). In this paper, the 1st order Takagi Sugeno Kwan system is used, with the fuzzy model rules expressed using matrices. This makes the inversion of the fuzzy model much easier than the inversion of the fuzzy model based on the TSK of order 0. Based on simulation results, the proposed fuzzy TILC seems able to give a very good initial guess as to the heater temperature setpoints, making it possible to have almost no wastage of plastic sheets. Simulation results show the effectiveness of the fuzzy TILC compared to a crisp TILC, even though the fuzzy controller is based on a fuzzy model built from noisy data.