Premium
Neural‐network‐based predictive controller design: An application to temperature control of a plastic injection molding process
Author(s) -
Lu ChiHuang,
Tsai ChingChih,
Liu ChiMing,
Charng YuanHai
Publication year - 2010
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.244
Subject(s) - setpoint , artificial neural network , control theory (sociology) , mimo , controller (irrigation) , model predictive control , recurrent neural network , process (computing) , control engineering , computer science , convergence (economics) , stability (learning theory) , engineering , control (management) , artificial intelligence , machine learning , agronomy , economics , biology , operating system , computer network , channel (broadcasting) , economic growth
Abstract This paper presents a neural‐network‐based predictive control (NPC) method for a class of discrete‐time multi‐input multi‐output (MIMO) systems. A discrete‐time mathematical model using a recurrent neural network (RNN) is constructed and a learning algorithm adopting an adaptive learning rate (ALR) approach is employed to identify the unknown parameters in the recurrent neural network model (RNNM). The NPC controller is derived based on a modified predictive performance criterion, and its convergence is guaranteed by adopting an optimal algorithm with an adaptive optimal rate (AOR) approach. The stability analysis of the overall MIMO control system is well proven by the Lyapunov stability theory. A real‐time control algorithm is proposed which has been implemented using a digital signal processor, TMS320C31 from Texas Instruments. Two examples, including the control of a MIMO nonlinear system and the control of a plastic injection molding process, are used to demonstrate the effectiveness of the proposed strategy. Results from both numerical simulations and experiments show that the proposed method is capable of controlling MIMO systems with satisfactory tracking performance under setpoint and load changes. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society