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Related Experiment Videos

Improved design of constrained model predictive tracking control for batch processes against unknown uncertainties.

Sheng Wu1, Qibing Jin2, Ridong Zhang3

  • 1Key Lab for IOT and Information Fusion Technology of Zhejiang, Information and Control Institute, Hangzhou Dianzi University, Hangzhou 310018, PR China.

ISA Transactions
|April 17, 2017
PubMed
Summary

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This study introduces an enhanced constrained tracking control for batch processes with uncertainties. The new model predictive control (MPC) offers improved tuning for better tracking performance despite model/plant mismatches.

Area of Science:

  • Chemical Engineering
  • Control Systems Engineering
  • Process Optimization

Background:

  • Batch processes often face uncertainties leading to model/plant mismatches.
  • Effective tracking control is crucial for maintaining desired performance in these processes.
  • Conventional robust model predictive control (MPC) may lack sufficient tuning for complex uncertainties.

Purpose of the Study:

  • To propose an improved constrained tracking control design for batch processes operating under uncertainties.
  • To develop a novel process model for state and tracking error augmentation with enhanced tuning capabilities.
  • To formulate a robust stable constrained MPC optimization for superior tracking control.

Main Methods:

  • A new process model is developed for state and tracking error augmentation.
Keywords:
Batch processesRobust model predictive controlState space modelsTracking control

Related Experiment Videos

  • A constrained Model Predictive Control (MPC) optimization is formulated for controller design.
  • The proposed controller design allows for increased tuning degrees compared to conventional methods.
  • The effectiveness is demonstrated using an injection molding process.

    • Main Results:

    • The proposed MPC approach provides improved tracking control performance in the presence of uncertainties.
    • The enhanced tuning capabilities of the controller address model/plant mismatches more effectively.
    • Comparative analysis shows superior performance against conventional robust MPC.

    Conclusions:

    • The developed constrained tracking control design offers a significant improvement for uncertain batch processes.
    • The enhanced MPC approach provides a more flexible and effective solution for practical process control.
    • This method is particularly valuable for applications like injection molding where precision is key.