Abstract
The data-driven control paradigm allows overcoming conventional troubles in the controller design related to model identifications procedures. Raw data are directly exploited in the control input selection by forcing the future plant dynamics to be coherent with previously collected samples. This article focuses, in particular, on the data-enabled predictive control algorithm. A relevant disadvantage of this algorithm is the fact that the complexity of the online control program grows with the dimension of the dataset. This issue becomes particularly relevant when considering embedded applications, such as the control of synchronous motor drives, characterized by challenging real-time constraints. This work proposes a systematic approach for dramatically reducing the complexity of such algorithms. Such methodology enables real-time feasibility of the constrained version of this control structure, which was previously precluded. Simulations and experimental results are provided to validate the method, considering the current control of an interior permanent magnet motor as test-case.
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