Adaptive robust control of a class of non-affine variable-speed variable-pitch wind turbines with unmodeled dynamics. (July 2016) - Bagheri, Pedram; Sun, Qiao
Abstract: In this paper, a novel synthesis of Nussbaum-type functions, and an adaptive radial-basis function neural network is proposed to design controllers for variable-speed, variable-pitch wind turbines. Dynamic equations of the wind turbine are highly nonlinear, uncertain, and affected by unknown disturbance sources. Furthermore, the dynamic equations are non-affine with respect to the pitch angle, which is a control input. To address these problems, a Nussbaum-type function, along with a dynamic control law are adopted to resolve the non-affine nature of the equations. Moreover, an adaptive radial-basis function neural network is designed to approximate non-parametric uncertainties. Further, the closed-loop system is made robust to unknown disturbance sources, where no prior knowledge of disturbance bound is assumed in advance. Finally, the Lyapunov stability analysis is conducted to show the stability of the entire closed-loop system. In order to verify analytical results, a simulation is presented and the results are compared to both a PI and an existing adaptive controllers. Highlights: An adaptive robust controller is designed for variable-speed, variable-pitch wind turbines. An adaptive neural network is designed to cope with uncertainties and unknown disturbance sources. Non-affine nature of the dynamics of the turbine is handled using Nussbaum-type functions properties. The controller is able to achieve asymptotic stability, despite the uncertain and nonlinear dynamics of the turbine, unknown disturbance sources.