Finite-Time Stabilization-Based Neural Control for the Synchronous Generator

This paper focuses on the problem of finite-time stabilization-based adaptive neural control design for the synchronous generator with unknown nonlinearities. The fast finite-time practical stability criteria is used to address the control design. Neural networks are used to overcome the obstacles appeared in the unknown nonlinear functions. And a systematic finite-time excitation control design is developed by embedding the practical finite-time stability theory and adaptive backstepping technology. Through the closed-loop stability analysis, it’s shows that all the signals in the closed-loop systems are bounded. At last, the presented results are tested by the numerical model of the single-machine power system.