Control of floating wind turbine: adaptive robust nonlinear solution based on self-tuning gains and neural network approach

This study proposes a hybrid control strategy utilizing an adaptive super-twisting (ASTW) methodology and artificial neural network (NN) techniques to regulate the collective blade pitch (CBP) of floating wind turbines (FWTs) operating in high wind conditions (Region III). In the presence of imposed perturbations, compounded by the complex nature of wind turbine dynamics, this research utilizes radial basis function (RBF) neural networks to approximate the unknown elements of the model. This methodology guarantees the controller’s stability and robustness, independent of the system’s precise mathematical model. The adaptive law is derived through the Lyapunov method, ensuring the convergence of rotor speed to the desired trajectory by fine-tuning the learning rate. The incorporation of the ASTW method, accompanied by self-tuning gains, aims to accelerate convergence speed and mitigate chattering synergistically with the NN approximation. The proposed controller was tested using the OpenFAST simulator, confirming its dynamic and static performance as well as validating its efficacy across scenarios with and without the RBF neural network approximator.