Noise Handling in Data-driven Predictive Control: A Strategy Based on Dynamic Mode Decomposition

A major issue when exploiting data for direct control design is noise handling, since overlooking or improperly treating noise might have a catastrophic impact on closed-loop performance. Nonetheless, standard approaches to mitigate its effect might not be easily applicable for data-driven control design, since they often require tuning a set of hyper-parameters via potentially unsafe closed-loop experiments. By focusing on data-driven predictive control, we propose a noise handling approach based on truncated dynamic mode decomposition, along with an automatic tuning strategy for its hyper-parameters. By leveraging on pre-processing only, the proposed approach allows one to avoid dangerous closed-loop calibrations while being effective in coping with noise, as illustrated on a benchmark simulation example.