Automatic Parameter Selection for Model Predictive Control for Fluid Flows
In session: MON 1.2 - Aerodynamics I
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Model-based techniques, such as model predictive control (MPC), have recently gained remarkable interest for controlling complex dynamics. MPC has demonstrated excellent capability in constrained highly non-linear models which are difficult to handle with traditional linear control systems. MPC has taken advantage of progress in data-driven modeling techniques for system identification for the plant. Nonetheless, the performance of this control logic is highly dependent on parameters that are typically tuned by trial and error. Moreover, the tuning process is heavily influenced by the noise in the sensors and the plant parameters uncertainty. The goal of this work is to present a noise-robust self-tuning control framework for fluid flows based on MPC and characterized by minimal user interaction.