Combined Iterative Learning and Model Predictive Control Scheme for Nonlinear Systems

Yuanqiang Zhou

doi:10.1109/TSMC.2024.3368108

Combined Iterative Learning and Model Predictive Control Scheme for Nonlinear Systems

2024-06-01

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DOI：10.1109/TSMC.2024.3368108

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Batch processes are typically nonlinear systems with constraints. Model predictive control (MPC) and iterative learning control (ILC) are effective methods for controlling batch processes. By combining batch-wise ILC and time-wise MPC, this article proposes a multirate control scheme for constrained nonlinear systems. Two-dimensional (2-D) framework is used to combine historical batch data with current measurements. The ILC part uses run-to-run control with previous iteration data, and the MPC part uses real-time control with current sampled measurements. Real-time feedback-based MPC in the time axis and run-to-run ILC in the batch axis are combined to optimize the current inputs based on previous batch input-output data and real-time system measurements. Rather than achieving control objectives in a single batch, our design allows multiple batches to be executed successively. To establish the stability of the combined scheme, rigorous theoretical analysis is presented next. The combined scheme with improved performance is then validated through two illustrative numerical examples.

Keywords:

Batch process

iterative learning

model predictive control (MPC)

nonlinear systems

process control

Combined Iterative Learning and Model Predictive Control Scheme for Nonlinear Systems

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