The active steering system can enhance the vehicle yaw stability, which is essential to road safety. However, control of vehicle yaw rate is very challenging due to the presence of nonlinearity and uncertainties in the vehicle dynamics. To address the problems, an extreme-learning-machine (ELM)-based adaptive control algorithm is proposed, and a vehicle dynamic model is also developed to identify the necessary exogenous variables for control system inputs. To validate the performance of the proposed controller, simulation is conducted with an industry software. Simulation result indicates that the proposed controller achieves superior performance in tracking nominal vehicle yaw rate. A comparison is also carried out with fuzzy logic control. The pilot result shows that the proposed controller outperforms the fuzzy logic control.