Performance Monitoring of Iterative Learning Control and Development of Generalized Predictive Control for Batch Processes

• Author / Creator

  Farasat, Ehsan

• Unlike continuous processes, a batch process has a certain period of operation time, and there are a number of batches in a typical operation. Hence variables in a batch process have dynamics in two dimensions, along time and across batches. Besides, batch processes involve large transient phases covering a wide range of operating envelopes, which cause challenges in both modeling and control.

  To meet the control objectives of batch processes, set-point tracking and disturbance rejection, iterative learning control (ILC) has been widely attempted. This thesis is concerned with the optimal design and performance assessment of ILC based on the minimum variance benchmark.

  When performance of ILC is unsatisfactory, alternative control strategies should be considered. Generalized predictive control (GPC) is a popular control strategy for continuous processes. Developing a two-dimensional GPC structure for batch processes is another focus of this research. Finally, ILC and suggested GPC are compared through simulation studies.

• Subjects / Keywords

  • Iterative learning control
  • Batch processes
  • Generalized predictive control
  • Two-dimensional identification
  • Performance assessment

• Graduation date

  Spring 2012

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/R3388K

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.