We present an adaptive extremum seeking scheme for the optimization and control of retention in the wet-end of a paper machine. An adaptive learning technique is introduced to construct an algorithm that drives the system to the optimal retention value. Lyapunov's stability theory is used in the design of the extremum seeking controller structure and the development of the parameter learning laws. The performance of the technique is demonstrated through simulation based on a first-principles dynamic model developed previously for a inicroparticulate system.
展开▼
