We consider LiDAR-enabled model-based collective pitch and torque controllers that can be implemented onboard a wind turbine in a hard real-time environment, in the sense that they can be computed efficiently on standard computer hardware and that require a fixed deterministic number of operations at each call. At first, we show that linear parameter varying wind-scheduled models provide for a reasonable approximation (for control purposes) of the wind turbine response over its entire operating regime. Based on these results, we formulate two model predictive controllers making use of such wind-scheduled linear models and a quadratic cost. The first controller is based on a classical constrained receding horizon approach that leads to the efficient on-line solution of a quadratic problem. The second can be interpreted as its steady-state unconstrained approximation; its implementation is straightforward and leads to the off-line computation of gain matrices that are then wind-scheduled at run time. Both controllers are tested in a high fidelity environment comprising of both a LiDAR and an aero-servoelastic simulator, in deterministic and unfrozen turbulent wind conditions. The numerical experiments show that the receding horizon controller outperforms a standard non-LiDAR-enabled one, as expected and as already reported by other authors. More interestingly, the second simpler controller is shown to provide for an almost similar performance of the more sophisticated one, although at a much lower and trivial computational cost. This behavior is interpreted as being due to the fact that, given the high disturbance level and the frequent solution update, even a rough approximation of the control problem is still capable of capturing the essence of the LiDAR preview information. (C) 2014 Elsevier Ltd. All rights reserved.