How Real-Time Iteration NMPC Solves Offshore Wind Turbine Control in Under 5 Milliseconds

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Offshore wind turbines equipped with full-power converters face a complex control challenge that requires the simultaneous coordination of power tracking, electrical safety, and mechanical load mitigation. While Nonlinear Model Predictive Control offers a systematic framework for addressing this multi-objective problem, standard implementations often fail to meet the stringent computational demands of commercial turbines. Traditional methods solve the nonlinear program to full convergence at every control step, resulting in computation times that are incompatible with the 10 to 100 millisecond control periods required for stable operation.

Existing formulations frequently omit critical electrical hard constraints and rely on expensive lidar systems for wind speed feedforward, limiting their practical deployment. To overcome these limitations, a novel Real-Time Iteration NMPC framework has been proposed. This approach performs a truncated real-time iteration at each control step, executing a single inexact Sequential Quadratic Programming step realized by at most five interior-point Newton iterations on the parametric Nonlinear Program.

The system utilizes warm-start initialization and a solution-shift strategy to enhance efficiency. Crucially, torque command amplitude bounds and a DC-link voltage deadband are embedded as hard bounds on the predicted trajectory within the Optimal Control Problem. Additionally, the torque rate limit is enforced through a dual mechanism involving a quadratic penalty in the cost function and a plus or minus 15 kN·m/s hard saturation at the solver output.

The controller employs a second-order autoregressive predictor to supply wind speed feedforward over a two-second horizon, with the power-tracking weight adaptively adjusted based on prediction confidence. Ablation experiments highlight the importance of the warm-start solution-shift mechanism; removing it degrades power-tracking Root-Mean-Square Error by up to 370% under grid load-drop transients.

Comparative simulations across four operating scenarios demonstrate that the proposed controller achieves an average per-step computation time of 3.796 to 4.313 ms, approximately one order of magnitude faster than standard NMPC. Under grid load-drop scenarios, the root-mean-square power-tracking error is reduced by 78.1% and 78.5% relative to a PI controller and standard NMPC, respectively.

These results confirm the computational feasibility and engineering potential of electrically constrained, lidar-free RTI-NMPC for coordinated power-tracking and electrical constraint management in offshore FPC wind turbines. The ability to execute such sophisticated control logic within such tight timeframes opens new avenues for reliable renewable energy integration without the need for costly external sensing hardware.