Model-based robust H∞ control of a granulation process using Smith predictor with Reference updating. (May 2019)

• Record Type: Journal Article
• Authors: Shaqarin, T.; Al-Rawajfeh, A.E.; Hajaya, M.G.; Alshabatat, N.; Noack, B.R.
• Is Part Of: Journal of process control. Volume 77(2019)
• Page Start: 38
• Page End: 47
• Publication Date: 2019-05
• Subjects: Granulation -- Robust control -- Smith predictor -- Mixed sensitivity -- Reference updating; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2019.03.003
• Languages: English
• ISSNs: 0959-1524

Abstract

Graphical abstract: Highlights: Efficient controller was applied for a linear MIMO granulation model with time delay. Smith predictor & Reference updating were keys in applying the robust controller. Uncertainties in model parameters & time delay were studied successfully. Proposed controller managed time delay, physical constraints & model mismatch. Abstract: Model-based feedback control is developed for a continuous granulation process addressing the challenge of time delay and physics-based input-output constraints. The process plant is a multi-input multi-output (MIMO) linear model with time delay. A robust H∞ controller is designed using the mixed sensitivity loop shaping design. A framework has been laid down to insure the robustness of the Smith predictor by incorporating the model mismatch as an additive uncertainty in the predictor's structure. The control performance and robustness is assessed by simulations for regulation and reference tracking problems. We show significant performance gains by employing a Smith predictor and the technique of reference updating: The control is coping significantly better with time delay, physical constraints and model mismatch. The proposed control approach is more efficient as compared to other widely used methods such as model predictive control (MPC); obtaining a stable behaviour of the response and control effort while forcing them to remain within the desired bounds.