Abstract: Reentry attitude control for reusable launch vehicles (RLVs) is challenging due to the characters of fast nonlinear dynamics and large flight envelop. A hierarchical structured attitude control system for an RLV is proposed and an unpowered RLV control model is developed. Then, the hierarchical structured control frame consisting of attitude controller, compound control strategy and control allocation is presented. At the core of the design is a robust adaptive control (RAC) law based on dual loop time-scale separation. A radial basis function neural network (RBFNN) is implemented for compensation of uncertain model dynamics and external disturbances in the inner loop. And then the robust optimization is applied in the outer loop to guarantee performance robustness. The overall control design frame retains the simplicity in design while simultaneously assuring the adaptive and robust performance. The hierarchical structured robust adaptive controller (HSRAC) incorporates flexibility into the design with regard to controller versatility to various reentry mission requirements. Simulation results show that the improved tracking performance is achieved by means of RAC.