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|標題:||NEURAL-NETWORK-BASED PREDICTIVE CONTROLLER DESIGN AN APPLICATION TO TEMPERATURE CONTROL OF A PLASTIC INJECTION MOLDING PROCESS||作者:||Lu, C.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
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