Robust Particle filtering-based nonlinear model predictive control: Application to PEMFC process

Qi Zhang

doi:10.1016/j.cjche.2025.07.011

Robust Particle filtering-based nonlinear model predictive control: Application to PEMFC process

2025-08-22

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DOI：10.1016/j.cjche.2025.07.011

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The application of plant measurement data for system identification and model predictive control (MPC) has garnered significant interest. However, the pervasive presence of noise and contamination in industrial data often compromises data quality, thereby degrading performance and reliability of model. To address this challenge, this study proposes a nonlinear MPC method based on robust time delay particle filtering (RPF-MPC). This method is specifically designed to mitigate the impact of stochastic time delays and noise on both model learning and control. RPF-MPC utilizes robust particle filtering with a Laplace distribution to reliably estimate parameters and unknown time delays. In this way, the controller is able to efficiently handle noise and outliers even when the data deviates from a Gaussian distribution. The proposed algorithm is presented in detail, a nonlinear numerical case and a fuel cell water cooling control case are presented to validate the effectiveness of the RPF-MPC method. Simulation results validate the effectiveness and robustness of the RPF-MPC method in handling uncertainty and improving control performance in the PEMFC process.

Keywords:

plant measurement data

system identification

model predictive control

robust time delay particle filtering

nonlinear MPC

uncertainty handling

Robust Particle filtering-based nonlinear model predictive control: Application to PEMFC process

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