System Identification Technology Research Articles

Modeling for lithography temperature control system based on gray identification

In view of the characteristic of the temperature control system, which is subsystem of the lithography tools, with big inertia, multiple time-delay and multiple perturbation, a dynamical mathematical model of the temperature control system is pre- sented by using theory modeling and system identification technology. High dimensional model is decoupled by using large-scale system decoupling theory, and the model parameters are estimated by using system identification method. This model is used in the temperature control system of lithography tools. The experiment shows that the model is strong robust and self-adaptive. The tem- perature of the lithography tools can be achieved very high temperature stability within ±0.006 ℃ by using this model.

Sludge bulking analysis and forecasting: Application of system identification and artificial neural computing technologies

The phenomenon of sludge bulking plays a major role in the treatment efficiency performance of activated sludge wastewater treatment plants. Sludge bulking has been widely studied from the biological point of view, but the current state of knowledge about its causes has not yet allowed the formulation of deterministic cause-effect relationships that can be used as prediction models. In this paper, system identification techniques, based on the analysis of the input and output of the activated sludge system are applied to the modeling of the phenomenon. Specifically, stochastic models and artificial neural system models are identified using treatment plant data. The models are subsequently applied to predict the occurrence of future bulking episodes. Comparison of the results obtained by these two methods with other prediction techniques is also presented. These modeling techniques yield very accurate results that surpass other traditional prediction methods.