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为解决反馈线性化(FL)方法的控制性能过于依赖精确系统模型的问题,提出了一种自适应的非线性控制策略,并应用于升力式飞行器的控制器设计.利用模糊小脑模型神经网络(FCMAC)良好的非线性逼近能力和自学习能力,设计了基于FCMAC的干扰观测器,对模型的不确定性和干扰进行在线估计.其网络权值更新规则采用李亚普诺夫方法设计,保证了闭环系统跟踪误差和干扰观测误差的有界.6自由度仿真结果显示该控制方案可实现姿态角对制导指令的稳定、快速跟踪,具有良好的鲁棒性.
In order to solve the problem that the control performance of the feedback linearization (FL) method is overly dependent on the exact system model, an adaptive nonlinear control strategy is proposed and applied to the controller design of a lift aircraft. The fuzzy cerebellar model neural network FCMAC), we designed an interference observer based on FCMAC to estimate the model’s uncertainty and interference online, and the network weight updating rule was designed by Lyapunov method to ensure the closed-loop The simulation results of the bounded .6 degrees of freedom of system tracking error and interference observation error show that the proposed control scheme can achieve the stability and fast tracking of the guidance commands with attitude angle and has good robustness.