A Recurrent Neural Network-Enhanced Supertwisting Pitch Controller for Floating Offshore Wind Turbines: Nonlinear Synthesis and HIL Validation

Abstract

This article proposes a novel adaptive pitch control strategy for floating offshore wind turbines (FOWTs) operating in Region III. The controller combines a supertwisting algorithm (STA) with a recurrent radial basis function neural network (RRBFNN) to estimate and compensate for external disturbances in real time. The control law is derived from a simplified nonlinear control-oriented model of a 5-MW semisubmersible FOWT, without relying on linearization. A Lyapunov-based framework guarantees closed-loop stability and online neural adaptation. The proposed RRBFNN-STA controller is implemented on a hardware-in-the-loop platform integrating OpenFAST, a CompactRIO emulator, and a Beckhoff industrial controller. Validation is performed under three realistic wind and wave conditions, including extreme turbulence scenarios. Compared to a conventional STA and a gain-scheduled proportional–integral baseline controller, the RRBFNN-STA achieves significantly lower root-mean-square errors (RMSE), reducing rotor speed tracking error from over 1.5 to 0.24 r/min, and platform pitch velocity RMSE to 0.24 $^\circ$/s. These results confirm the robustness and real-time applicability of the proposed control approach for pitch regulation in FOWTs under nonlinear and turbulent operating conditions.