摘要
针对一类多输入多输出模型不确定系统,提出了一种基于广义模糊神经网络的自适应轨迹线性化控制方法(ATLC)。针对再入机动飞行器(MRV)进行了控制器设计和分析。MRV气动参数存在较大的不确定,这会导致轨迹线性化控制器(TLC)鲁棒性能下降。利用广义模糊神经网络(G-FNN)在线补偿系统的非线性建模不确定,改善了控制器性能。基于Lyapunov稳定性理论,证明了ATLC闭环控制系统的稳定性。仿真结果表明自适应轨迹线性化控制系统在飞行器气动参数大范围摄动时仍具有鲁棒性和稳定性,验证了所提出的控制策略的有效性。
An adaptive trajectory linearized control method（ATLC） based on generalized fuzzy neural network（G-FNN） is proposed for a kind of multi-input/multi-output system with nonlinear model uncertainties.The ATLC controller is designed and analyzed for the six-Dof nonlinear dynamics of a maneuvering reentry vehicle（MRV）.The aerodynamic parameter uncertainties of MRV can become large and result in poor robustness of the standard TLC controller.The G-FNN is introduced to approximate model nonlinear uncertainties adaptively,thus improving the controller performance.The stability of the closed-loop control system is proved by using the Lyapunov stability theory.Simulation studies demonstrate that the ATLC controller is robust with respect to large parametric uncertainties.
出处
《宇航学报》
EI
CAS
CSCD
北大核心
2011年第5期1039-1046,共8页
Journal of Astronautics
基金
国家自然科学基金(60874084)
关键词
自适应控制
模糊神经网络
轨迹线性化控制
再入飞行器
Adaptive control
Fuzzy neural networks
Trajectory linearization control
Reentry vehicle
