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Neural network‐based optimal iterative controller for nonlinear processes
Author(s) -
Gao Furong,
Wang Fuli,
Li Mingzhong
Publication year - 2000
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.5450780211
Subject(s) - control theory (sociology) , artificial neural network , nonlinear system , computer science , controller (irrigation) , convergence (economics) , iterative learning control , pid controller , computation , optimal control , process (computing) , tracking error , iterative method , control engineering , mathematics , mathematical optimization , algorithm , control (management) , artificial intelligence , engineering , temperature control , physics , quantum mechanics , agronomy , economics , biology , economic growth , operating system
A new optimal iterative neural network‐based control (OINNC) strategy with simple computation and fast convergence is proposed for the control of processes with nonlinear dynamics. The process dynamics is captured by a forward neural network, and the control is determined by a simple iterative optimization during each sampling interval based on a linearized neural network model. In addition, a feedback control is incorporated into the system to compensate for any model mismatches and to reject disturbances. With the proposed system, the tracking error is shown to be confined to the origin. An application of the proposed OINNC scheme to a nonlinear process results in superior performance when compared with a well‐tuned conventional PID controller.