Nonlinear Control Design for a Plasma Process

This paper presents a nonlinear control design for a first-principles based model of an argon plasma process. In this study, a Hammerstein-type structure was employed as a basis for a feedback control design. Artificial neural networks were used to accurately model the static nonlinearity. In the developed Hammerstein model, variations in the process dynamics were accounted for by considering parametric uncertainty. A control design strategy based on μ-synthesis was applied to deliver good tracking performance and disturbance rejection.