A Novel Chaos Control Approach with Adaptive ANN Compensation for a DFIG-Based Wind Turbine

This paper proposes a novel chaos control approach with adaptive artificial neural network (ANN) compensation for a DFIG-based wind turbine. First, a nonlinear third-order DFIG model is established to characterize the complex dynamic and chaotic behavior of the system. Subsequently, a novel state-feedback control law is developed to stabilize the closed-loop system, suppress the chaotic dynamics, and ensure accurate electrical-speed tracking. To further enhance the tracking performance, an adaptive ANN is introduced as an auxiliary compensation mechanism to learn and compensate online for residual tracking errors without modifying the nominal feedback gains. A Lyapunov-based adaptation law is formulated to guarantee the boundedness of the adaptive parameters and the uniform ultimate boundedness of the tracking error. Simulation results demonstrate that the proposed control scheme effectively suppresses the chaotic dynamics of the DFIG while achieving accurate electrical-speed tracking. Moreover, the integration of the adaptive ANN significantly reduces the residual tracking error compared with the nominal controller, while maintaining the stability and robustness of the closed-loop system.