Title :
Iterative Learning Control and It´s Application to Batch Process Optimization
Author :
Song, J-R ; Wang, H-W ; Shi, H-B ; Zhang, SH-H
Author_Institution :
Sch. of Inf. Sci. & Eng., East China Univ. of Sci. & Technol., Shanghai, China
Abstract :
An iterative learning control (ILC) algorithm based on recurrent wavelet neural network(RWNN) is proposed to control product final quality in batch process. recurrent Wavelet neural network is used to modeling long range batch process model. Due to model-plant mismatches and unmeasured disturbances, the calculated control policy based on RWNN model may not be optimal when applied to the actual process. By utilizing the repetitive nature of batch process , ILC is used to improve product final quality from batch to batch. Prediction models are modified based on previous prediction model average errors. Model errors are gradually reduced from batch to batch, control inputs approach to optimal control policy. The effectiveness is verified on a simulated batch process.
Keywords :
batch processing (industrial); iterative methods; learning (artificial intelligence); optimal control; optimisation; process control; production control; quality control; recurrent neural nets; wavelet transforms; ILC; RWNN model; batch process model; batch process optimization; iterative learning control algorithm; optimal control policy; prediction model average error; product final quality control; recurrent wavelet neural network; Batch production systems; Indexes; Optimal control; Predictive models; Process control; Recurrent neural networks; Trajectory;
Conference_Titel :
Circuits, Communications and System (PACCS), 2011 Third Pacific-Asia Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4577-0855-8
DOI :
10.1109/PACCS.2011.5990241
