Robust Model Predictive Control Design for a Wind Turbine Using Invariant Set Estimation
Abstract
This study proposes a robust model predictive controller (RMPC) for the 5 MW fatigue, aerodynamics, structures, and turbulence (FAST) model, a popular wind turbine model developed by the National Renewable Energy Laboratory, to cope with fluctuations and uncertainties associated with the wind more effectively than existing controllers, such as the standard MPC. In line with other model‐based controllers, the proposed RMPC relies on linear control design models obtained by linearizing the high‐fidelity nonlinear aeroelastic FAST model. As with standard MPC, no prior information on the wind speed is given, and the wind speed is considered a disturbance. Robustness against wind disturbances and modeling errors that arise between the control design model and the process (i.e., the simulation model) is ensured by computing a feedback law and a robust invariant set through linear matrix inequalities. The resulting feedback control law is incorporated into the standard MPC, yielding the RMPC, which is thoroughly tested and analyzed by application to the FAST model, compared to the standard MPC, PI control, and light detection and ranging (LiDAR) based feedforward MPC under various realistic wind conditions. The results demonstrate that the proposed controller, which operates without relying on LiDAR measurements, achieves performance nearly comparable to that of a feedforward controller that relies on costly LiDAR technology.