Model Predictive Control of Wind Turbines: A Model-Oriented Review of Prediction Models, Control Objectives, and Nonlinear Aerodynamic Formulations
Model predictive control (MPC) has been applied to wind-turbine regulation with markedly different internal prediction models, ranging from linearized low-order turbine models to nonlinear, aeroelastic, floating, and data-driven predictors. This review organizes the literature primarily by the model propagated by the controller and treats model order, predictor representation, MPC formulation, control objective, and validation plant as separate descriptors. Primary studies show that low-order linear or piecewise-affine predictors are attractive for constrained power and speed regulation, whereas flexible drivetrain, structural, and floating models are needed when drivetrain twist, tower motion, blade loads, or platform dynamics are explicit control quantities. Nonlinear and economic MPC studies further demonstrate coordinated pitch/torque control across broad operating regions, while preview, repetitive, and data-driven formulations address wind prediction, periodic loads, and model mismatch. Particular attention is given to aerodynamic pitch dependence: variable-pitch turbine dynamics can be non-affine with respect to applied pitch, and the term NMPC alone does not prove that the original nonlinear pitch map is retained inside the optimizer. The review therefore adopts a conservative, equation-based classification for direct nonlinear applied-pitch propagation. Here, direct nonlinear applied-pitch propagation means that the applied pitch at each prediction step is evaluated inside the original nonlinear aerodynamic map, rather than replacing that dependence by a linearized, scheduled, piecewise-affine, or learned surrogate. The literature supports a remaining integration challenge at the intersection of flexible drivetrain prediction, nonlinear aerodynamic pitch treatment, explicit constraints and load objectives, and validated adaptive or learned model correction.