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

Qi Zhang; Fanda Pan; Lei Xie

doi:10.1016/j.cjche.2025.07.011

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

Updated：2026-02-12

- Robust particle filtering-based nonlinear model predictive control: Application to PEMFC process
- Chinese Journal of Chemical Engineering Vol. 88, Issue 12, Pages: 321-334(2025)
- Affiliations：
  
  1. State Key Laboratory of Industrial Control Technology, Zhejiang University,Hangzhou,China,310027
  2. BYD Automobile Industry Co., Ltd.,Shenzhen,China,518118
  3. Technical Center of Zhejiang China Tobacco Industrial Co. Ltd.,Hangzhou,China,310000
  4. Institute of Intelligence Science and Engineering, Shenzhen Polytechnic University,Shenzhen,China,518055
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2025.07.011
  CLC：
- Published：2025
- Accepted：
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Qi Zhang, Fanda Pan, Lei Xie. Robust particle filtering-based nonlinear model predictive control: Application to PEMFC process[J]. Chinese Journal of Chemical Engineering, 2025, 88(12): 321-334.
DOI：

Qi Zhang, Fanda Pan, Lei Xie. Robust particle filtering-based nonlinear model predictive control: Application to PEMFC process[J]. Chinese Journal of Chemical Engineering, 2025, 88(12): 321-334. DOI： 10.1016/j.cjche.2025.07.011.

摘要

Abstract

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.

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