Adaptive RBF Neural Network Control Using Fractional-order Disturbance Observer for variable-Speed Wind Turbines

Key Points

• The control scheme ensures finite-time convergence of tracking and estimation errors, improving system accuracy and reliability.
• Simulations highlight a significant enhancement in dynamic performance with a robust disturbance rejection mechanism using fractional calculus.
• The adaptive radial basis function neural network addresses time-varying disturbances, significantly improving wind turbine control methodologies.
• This approach may enable the development of more resilient and efficient wind energy systems, crucial for sustainable energy solutions.