In this paper, an observer-based model reference adaptive controller is proposed to solve the vibration control problem for offshore wind turbines. The process begins with the formulation of a dynamic model that accounts for the inherent complex coupling among the deflection of the floating platform, the displacement at the top of the tower, and the behavior of winds and waves. Subsequently, a model reference adaptive control system is implemented with a disturbance rejection mechanism. A key disturbance observer for the complicated wind-wave mixed loads is developed using a stochastic configuration neural network. Then, the existence conditions and gains for the adaptive controller are meticulously derived from a specific Lyapunov function. The performance of the disturbance observer is evaluated through simulations under various sea conditions. Moreover, the proposed adaptive control system has been shown to significantly reduce half of the control cost while effectively attenuating the responses of deflection and displacement, outperforming existing control solutions.
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