Identification-Based Closed-Loop Control Strategies for a Cylinder Wake Flow

Abstract

Four closed-loop control strategies are discussed to reduce the drag of a cylinder wake flow: Linear Quadratic Gaussian (LQG) control, gain-scheduled LQG (GS-LQG) control, gain-scheduled PI control, and multimodel predictive control (M-MPC). The control models are obtained in an input–output framework through ARMAX (for LQG control), multi-ARMAX (for GS-LQG control and M-MPC), and multi-ARX (for gain-scheduled PI control). The use of system identification for the underlying flow control problem gets rid of the difficult task of developing accurate and robust reduced-order models for the Navier–Stokes (NS) equations. The control is introduced through sucking of fluid through the cylinder surface. The drag on the cylinder is reduced for all control methods. The robustness of all control strategies is tested against unmodeled disturbances and/or dynamics through a detailed simulation of an NS equation-based model by varying in time the Reynolds number around its nominal value 200. For the considered cylinder wake, the M-MPC approach is the best solution. The application of the presented closed-loop control algorithms for the cylinder drag control as a benchmark problem constitutes promising solutions for other related flow control problems in industries.