Reduced-order adaptive controllers for MHD flows using proper orthogonal decomposition

We present a reduced-order adaptive controller design for MHD flows. Frequently, reduced-order models are derived from low-order bases computed by applying proper orthogonal decomposition (POD) on an a priori ensemble of data of ow model. This reduced-order model is then used-to derive a reduced-order controller. The approach discussed here differs from these approaches. It. uses an adaptive procedure that improves the reduced-order model by successively updating the ensemble of data. The idea is illustrated on a control problem in unsteady magneto-hydrodynamic (MHD) flows. Numerical implementations and results are provided illustrating feasibility.