Stochastic Iterative Learning Model Predictive Control based on Stochastic Approximation
Year: 2019
Type: article
Abstract: Iterative learning model predictive control (ILMPC) is an effective control technique for improving the performance of a batch process under model uncertainty and rejecting real-time disturbances. Industrial batch processes often have stochastic disturbance and noise and ILMPC cannot guarantee convergence for such systems. In this work, we propose a novel stochastic ILMPC that combines stochastic approximation with ILMPC algorithm. The proposed algorithm ensures the almost sure convergence property. In comparison with the ILMPC, the proposed control algorithm also shows better performance in terms of the tracking error.
Source: IFAC-PapersOnLine
Authors: ByungJun Park, Se-Kyu Oh, Jong Min Lee
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W2956001754