Title: Automatic Temperature Setpoint Tuning of a Thermoforming Machine using Fuzzy Terminal Iterative Learning Control,
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.