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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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A two-stage robust iterative learning model predictive control for batch processes.

Chengyu Zhou1, Li Jia1, Yang Zhou1

  • 1Department of Automation, College of Mechatronics Engineering and Automation, Shanghai University, Shanghai 200072, China.

ISA Transactions
|October 17, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a robust two-stage iterative learning model predictive control (ILMPC) for batch processes. The new method enhances tracking performance and convergence speed despite model parameter uncertainty.

Keywords:
Batch processesIterative learning model predictive controlModel parameter uncertaintyRobust control

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Area of Science:

  • Chemical Engineering
  • Control Systems Theory

Background:

  • Iterative learning model predictive control (ILMPC) offers high precision for batch processes.
  • Model parameter uncertainty degrades ILMPC performance.
  • Robust control is crucial for reliable batch process operation.

Purpose of the Study:

  • Develop a robust ILMPC strategy for batch processes facing model parameter uncertainty.
  • Enhance tracking performance and convergence speed under uncertainty.
  • Ensure system stability within batches.

Main Methods:

  • A two-stage robust ILMPC strategy is proposed.
  • Integrates robust iterative learning control (batch-axis) and robust model predictive control (time-axis).
  • Control law derived from solving two convex optimization problems.

Main Results:

  • The developed method achieves faster convergence.
  • Demonstrates superior tracking performance with model parameter uncertainty.
  • Convergence analysis confirms theoretical predictions.

Conclusions:

  • The two-stage robust ILMPC strategy effectively addresses model parameter uncertainty in batch processes.
  • Offers improved control performance compared to standard ILMPC.
  • Validated through comparative simulations.