Multi-level data-predictive control for linear multi-timescale processes with stability guarantee

Multi-timescale dynamics are common in chemical processes. These processes are often difficult to model and pose challenges in control system design. In this paper, we propose a data-based control approach for linear multi-timescale systems using a system behavioural framework. A data resampling method coupled with a novel data predictive control (DPC) design with multi-level optimisation horizons is developed to handle different timescales. To deal with the dynamics of different timescales, the optimisation horizons with small to large time intervals are used to predict and optimise control actions from near to distant future. Computational complexity wise, the multi-level structure allows horizon length to expand exponentially with optimisation steps. A trajectory-based dissipativity condition is also developed to ensure stability of the proposed DPC, while achieving disturbance rejection and tracking control. An example of controlling a multi-timescale reactive distillation column is presented to illustrate the proposed approach. •A novel multi-timescale Data-Predictive Control (DPC) approach is developed.•This approach is based on a system behavioural framework.•A data resampling approach is developed to capture multi-timescale dynamics.•A multi-level DPC approach is developed to control multi-timescale processes.•A trajectory-based dissipativity condition ensures stability of the proposed DPC.•Tracking control is achieved together with disturbance rejection.