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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。
Aiming at a class of uncertain systems with multiple input and multiple output models, an adaptive trajectory linearization control method (ATLC) based on generalized fuzzy neural network is proposed. Controller Design and Analysis for Reentry Maneuver Vehicle (MRV). The MRV aerodynamic parameters have large uncertainties, which leads to the degradation of robustness of the trajectory linearization controller (TLC). The nonlinear modeling of G-FNN on-line compensation system is uncertain and improves the performance of the controller. Based on Lyapunov stability theory, the stability of ATLC closed-loop control system is proved. The simulation results show that the adaptive trajectory linearized control system is robust and stable when the aerodynamic parameters of the aircraft are widely perturbed, and the effectiveness of the proposed control strategy is verified.