Robust fatigue-aware adaptive control via cascaded SMO-UKF estimation for nonlinear systems under uncertainties

This paper proposes an adaptive torque control and state estimation method that effectively addresses the trade-off between fatigue load reduction and power generation performance in a nonlinear wind energy conversion system subject to strong turbulence, measurement noise, and model uncertainties. The proposed fatigue load-aware adaptive torque controller dynamically adjusts control weights based on the variability, mismatch, and standard deviation of the aerodynamic torque, thereby reducing drivetrain fatigue loads while minimizing power loss. In addition, to accurately estimate the highly nonlinear and unknown aerodynamic torque, a combined estimator based on SMO and UKF was designed. SMO provides robustness against disturbances and noise, while the UKF enhances estimation accuracy in nonlinear systems, enabling reliable aerodynamic torque estimation. To validate the performance, simulations were conducted under three different turbulence intensities and wind speed profiles. Simulation results demonstrated that the proposed SMO-UKF estimator achieves up to a 2.77% improvement in estimation performance compared to conventional methods. Furthermore, the proposed control strategy reduces fatigue loads by up to 15.52% with negligible reduction in power output.