Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/68109
標題: NEURAL-NETWORK-BASED PREDICTIVE CONTROLLER DESIGN AN APPLICATION TO TEMPERATURE CONTROL OF A PLASTIC INJECTION MOLDING PROCESS
作者: Lu, C.H.
Tsai, C.C.
Liu, C.M.
Charng, Y.H.
關鍵字: Plastic injection molding process;predictive control;recurrent neural;network;temperature control;nonlinear-systems;discrete-time;algorithm;approximation;barrel
Project: Asian Journal of Control
期刊/報告no:: Asian Journal of Control, Volume 12, Issue 6, Page(s) 680-691.
摘要: 
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
URI: http://hdl.handle.net/11455/68109
ISSN: 1561-8625
DOI: 10.1002/asjc.244
Appears in Collections:期刊論文

Show full item record
 

Google ScholarTM

Check

Altmetric

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.