Iterative Learning Control and It's Application to Batch Process Optimization
- Resource Type
- Conference
- Authors
- Song, J.-R.; Wang, H.-W.; Shi, H.-B.; Zhang, SH.-H.
- Source
- 2011 Third Pacific-Asia Conference on Circuits, Communications and System (PACCS) Circuits, Communications and System (PACCS), 2011 Third Pacific-Asia Conference on. :1-4 Jul, 2011
- Subject
- Computing and Processing
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Signal Processing and Analysis
Batch production systems
Predictive models
Optimal control
Process control
Trajectory
Recurrent neural networks
Indexes
- Language
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.