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Direct data-driven controller design using fictitious reference and regression
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DOI：10.1016/j.compchemeng.2026.109780.png)
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Abstract
En 中文
This study presents a direct data-driven method for designing feedback controllers without requiring explicit process model identification. The method constructs a fictitious setpoint from measured input–output data based on a user-specified desired closed-loop behavior, then estimates controller parameters by solving a least-squares regression problem. The proposed formulation can be interpreted as a practical specialization of a VRFT-type tuning framework for process-control applications. A key feature is that only a single parameter, the desired response delay, needs to be specified. This parameter can be optimized automatically or adjusted manually to achieve desired performance trade-offs. The method accommodates various linearly parameterized controller structures and can use operating data when the data contain sufficient dynamic variation for the resulting regression problem. The effectiveness of the method is demonstrated through simulation studies, including comparison with standard VRFT, and experimental validation on a physical water-level control system. The results indicate improved tracking performance compared with conventional tuning approaches while clarifying the practical trade-offs associated with the pure-delay reference model and the unity weighting filter.
Keywords:
Direct control design
Data-driven control
Model-free control
One-shot data
Controller tuning
Journal
C
IF:
3.9
Papers:
183
Citations:
0