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A 2D-FM model-based robust iterative learning model predictive control for batch processes.

Limin Wang1, Jingxian Yu2, Ping Li2

  • 1School of Mathematics and Statistics, Hainan Normal University, Haikou, 571158, China.

ISA Transactions
|October 17, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces composite iterative learning model predictive control (CILMPC) for uncertain batch processes. The novel approach ensures faster tracking error convergence compared to traditional methods.

Keywords:
Asymptotically stableBatch processesComposite iterative learning model predictive controlTwo dimensional Fornasini–Marchesini model

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Area of Science:

  • Control Engineering
  • Process Systems Engineering
  • Artificial Intelligence

Background:

  • Batch processes often involve uncertainties, challenging traditional control methods.
  • Model Predictive Control (MPC) is effective but can be computationally intensive for uncertain systems.
  • Iterative Learning Control (ILC) enhances performance over repeated operations.

Purpose of the Study:

  • To develop a Composite Iterative Learning Model Predictive Control (CILMPC) strategy for uncertain batch processes.
  • To enhance tracking performance and stability in the presence of process uncertainties.
  • To provide a computationally feasible online optimization method for advanced process control.

Main Methods:

  • Utilized a two-dimensional Fornasini-Marchesini (2D-FM) model for uncertain batch processes.
  • Developed a novel equivalent error system combining state and tracking errors.
  • Constructed an iterative learning predictive updating law with 2D state feedback and a worst-case linear quadratic function.
  • Optimized the controller using a worst-case objective function over an infinite moving horizon, considering input/output constraints.
  • Derived solvable conditions for real-time online optimization using linear matrix inequalities (LMIs).

Main Results:

  • The proposed CILMPC scheme guarantees stability upon successful optimization.
  • The approach offers superior tuning capabilities compared to traditional 1D robust MPC.
  • Demonstrated faster convergence of tracking error in uncertain industrial batch processes.
  • Validated the control strategy's feasibility and superiority through simulations on an injection molding process and a three-tank system.

Conclusions:

  • The CILMPC strategy effectively addresses uncertainties in batch processes.
  • The method provides enhanced performance and stability over existing control techniques.
  • The developed LMI-based optimization ensures real-time applicability and robustness.