Hierarchical structured robust adaptive attitude controller design for reusable launch vehicles  

作　　者：Guangxue Yu Huifeng Li 

机构地区：[1]School of Astronautics, Beihang University [2]Beijing Institute of Astronautical Systems Engineering

出　　处：《Journal of Systems Engineering and Electronics》2015年第4期813-825,共13页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation of China(61174221)

摘　　要：Reentry attitude control for reusable launch vehicles （RLVs） is challenging due to the characters of fast nonlinear dy- namics and large flight envelop. A hierarchical structured attitude control system for an RLV is proposed and an unpowered RLV con- trol 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 op- timization 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 con- troller （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.Reentry attitude control for reusable launch vehicles （RLVs） is challenging due to the characters of fast nonlinear dy- namics and large flight envelop. A hierarchical structured attitude control system for an RLV is proposed and an unpowered RLV con- trol 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 op- timization 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 con- troller （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.

关 键 词：reusable launch vehicle （RLV） REENTRY hierarchicalstructured H∞ optimization neutral network adaptive （NNA） atti-tude control. 

分 类 号：V448.1[航空宇航科学与技术—飞行器设计]