Stochastic model predictive control of a 10-MW semi-submersible floating offshore wind turbine: A comparative study with gain-scheduled PI, LQR and MPC controllers

• Stochastic model prediction control strategy is proposed for blade pitch control. • Controllers are evaluated in offshore conditions from below to above rated winds. • SMPC controller reduces rotor overspeed and fatigue loads at lower wind speeds. • At higher wind speeds, SMPC and MPC show comparable control performance. This paper proposes a stochastic model predictive control (SMPC) strategy for the collective blade pitch control of the LIFES50+ OO-Star Wind Floater Semi 10 MW turbine. The control objective is to regulate rotor speed and reduce platform motion under stochastic offshore conditions. The performance of the SMPC controller is evaluated under stochastic wind and wave conditions across below-rated and above-rated mean wind speeds and compared to that of a gain-scheduled proportional-integral (PI) controller (baseline), a linear-quadratic regulator (LQR), and a deterministic model predictive controller (MPC). At a mean wind speed of 12 m/s, the SMPC controller outperforms the other controllers by reducing the occurrence of rotor overspeed by more than 10 %, with negligible loss or a slight gain in the mean electrical generator power, along with decreased power fluctuations, reduced platform pitch motion, and lower fatigue loads. This comparative study highlights the potential of SMPC to improve control of floating offshore wind turbines and motivates further research into hierarchical and probabilistic control frameworks.