A model-based tracking control scheme for nonlinear industrial processes involving joint unscented Kalman filter.

Bhadra, Sanjay; Panda, Atanu; Bhowmick, Parijat; Kannan, Somasundar

Abstract

This paper proposes a model-based reference tracking scheme for stable, MIMO, nonlinear processes. A Joint Unscented Kalman Filtering technique is exploited here to develop a stochastic model of the physical process via simultaneous estimation of the process states and the time-varying/uncertain parameters. Unlike the existing nonlinear model predictive controllers, the proposed scheme does not involve any dynamic optimisation process, which helps to reduce the overall complexity, computation overburden and execution time. Furthermore, the proposed methodology offers robustness to process model-mismatch and considers the effects of stochastic disturbances. A nonlinear two-tank liquid-level control problem and a nonlinear coupled level-temperature control process are studied to demonstrate the usefulness of the proposed scheme.

Citation

BHADRA, S., PANDA, A., BHOWMICK, P. and KANNAN, S. 2023. A model-based tracking control scheme for nonlinear industrial processes involving joint unscented Kalman filter. Journal of control and decision [online], Latest Articles. Available from: https://doi.org/10.1080/23307706.2023.2202183

• Journal Article Type: Article
• Acceptance Date: Apr 10, 2023
• Online Publication Date: May 31, 2023
• Deposit Date: Jun 29, 2023
• Publicly Available Date: Jun 1, 2024
• Journal: Journal of control and decision
• Print ISSN: 2330-7706
• Electronic ISSN: 2330-7714
• Publisher: Taylor and Francis Group
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1080/23307706.2023.2202183
• Keywords: Model-based control; JUKF; Nonlinear MPC; TITO coupled-tank process; Level-temperature control
• Public URL: https://rgu-repository.worktribe.com/output/1993106