High order robust Terminal Iterative Learning Control design using Genetic Algorithm

In the thermoforming industry, the heater temperature set points can be automatically tuned with Terminal Iterative Learning Control (TILC). This cycle-to-cycle control is used to adjust the heater temperature set points so that the temperature profile at the surface of the plastic sheet converges to the desired one. The structure of the proposed high order TILC is based on the Internal Model Control (IMC). The robustness of a closed-loop system with this TILC algorithm is measured using the H_∞ Mixed Sensitivity approach. A Genetic Algorithm (GA) is used to find a high order TILC controller parameters giving the most robust closed-loop system. Simulation results are included to show the effectiveness of those designed robust TILC algorithms.