Virtual Reference Feedback Tuning with data-driven reference model selection
In control applications where finding a model of the plant is the most costly and time consuming task, Virtual Reference Feedback Tuning (VRFT) represents a valid - purely data-driven - alternative for the design of model reference controllers. However, the selection of a proper reference model within a model-free setting is known to be a critical task, with this model typically playing the role of a hyper-parameter. In this work, we extend the VRFT methodology to compute both a proper reference model and the corresponding optimal controller parameters from data by means of Particle Swarm optimization. The effectiveness of the proposed approach is illustrated on a benchmark simulation example.
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