Model-based and data-driven model-reference control: A comparative analysis

Abstract

In many industrial applications, finding a model from physical laws that is both simple and reliable for control design is a hard and time-consuming undertaking. When a set of input/output (I/O) measurements is available, one can derive the controller directly from data, without relying on the knowledge of the physics. In the scientific literature, two main approaches have been proposed for control system design from data. In the "model-based" approach, a model of the system is first derived from data and then a controller is computed based on the model. In the "data-driven" approach, the controller is directly computed from data. In this work, the above approaches are compared from a novel perspective. The main finding of the paper is that, although from the standard perspective of parameter variance analysis the model-based approach is always statistically more efficient, the data-driven controller might outperform the model-based solution for what concerns the final control cost.

A review of failure prognostics for predictive maintenance of offshore wind turbines

“…Prognosis models are divided into model-based models, data-driven models and hybrid models. Model-based approach is defined as models that rely on physic configuration of system [19,20]. It includes physics-based model and digital simulation of the system.…”

A Data-Driven Approach to Set-Theoretic Model Predictive Control for Nonlinear Systems

“…Model predictive control strategies based on a datadriven approach (DDMPC) represent, in fact, an emerging paradigm that harnesses the vast amounts of data generated by modern systems to enhance control strategies without necessitating explicit mathematical models of a plant [2][3][4][5][6]. This paradigm shift allows for the development of robust control methods even in scenarios where obtaining an accurate mathematical model is challenging or impractical and has garnered significant interest due to several motivations stemming from its advantages over traditional model-based control methods (see [7] for a general discussion on the matter). One of the most relevant aspects is the reduced modeling effort since a data-driven approach circumvents the need for complex mathematical modeling, thus diminishing the effort required for controller design [8].…”