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Related Experiment Videos

Control of nonlinear processes by using linear model predictive control algorithms.

Bingfeng Gu1, Yash P Gupta

  • 1Department of Process Engineering and Applied Science, Dalhousie University, Halifax, Canada.

ISA Transactions
|February 8, 2008
PubMed
Summary
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This study enhances nonlinear process control using dynamic matrix control and model predictive control. A novel switching modification improves performance in pH neutralization, demonstrating better control for complex chemical processes.

Area of Science:

  • Chemical Engineering
  • Process Control
  • Nonlinear Systems

Background:

  • Chemical processes often exhibit inherent nonlinearity.
  • Linear control algorithms are frequently applied to nonlinear systems due to their simplicity.
  • Controlling nonlinear processes effectively remains a challenge in chemical engineering.

Purpose of the Study:

  • To investigate the application of dynamic matrix control (DMC) and a simplified model predictive control (MPC) algorithm for nonlinear process control.
  • To address process nonlinearity by implementing a sub-region switching strategy for controller models.
  • To present a modification for MPC algorithms to effectively manage model switching in nonlinear systems.

Main Methods:

  • Utilizing dynamic matrix control (DMC) and a simplified model predictive control (MPC) algorithm.

Related Experiment Videos

  • Dividing the operating region into sub-regions to handle nonlinearity.
  • Implementing a controller model switching strategy based on the process's operating region.
  • Developing a simple modification for MPC to manage controller switching.

    • Main Results:

    • Simulation and experimental results demonstrate the effectiveness of the proposed approach.
    • The modified MPC algorithm showed significant improvement in controlling the bench-scale pH neutralization process.
    • The sub-region switching strategy successfully managed the inherent nonlinearity of the process.

    Conclusions:

    • The developed modification for model predictive control significantly enhances the control of nonlinear chemical processes.
    • Switching controller models across different operating regions is an effective strategy for nonlinear process control.
    • This approach offers a practical solution for improving the performance of chemical process control systems.