Comparative analysis of cost functions for GA-based optimization of neural controllers for floating wind turbines

Maximizing power extraction while mitigating structural vibrations remains a major control challenge in floating offshore wind turbines (FOWTs) due to coupled wind–wave–platform dynamics. This study proposes a genetic algorithm–optimized artificial neural network (GA–ANN) control framework for the below-rated operating region of a 5 MW barge-type FOWT. The control strategy combines direct generator speed control for maximum power point tracking with an ANN-based torque controller whose parameters are optimized using genetic algorithms. The main contribution of this work is a comparative analysis of cost-function formulations used in the GA optimization of the neural controller. These formulations incorporate both generator speed regulation and tower-top dynamic response, enabling a systematic evaluation of their influence on the optimization process and the resulting control performance. In particular, additive and multiplicative cost-function structures are investigated to analyze how different aggregation strategies affect the trade-off between energy capture and structural vibration mitigation. Numerical simulations performed in OpenFAST demonstrate that the proposed GA–ANN control strategy outperforms a baseline variable-speed controller, delivering higher and less oscillatory electrical power while significantly reducing tower-top acceleration levels. The results highlight the critical role of cost-function design in evolutionary optimization of wind turbine controllers and demonstrate the potential of the proposed methodology to improve both performance and structural durability in floating offshore wind systems.