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Neural-Based Compensation of Nonlinearities in an Airplane Longitudinal Model with Dynamic-Inversion Control

机译:具有动态反演控制的飞机纵向模型中基于神经的非线性补偿

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摘要

The inversion design approach is a very useful tool for the complex multiple-input-multiple-output nonlinear systems to implement the decoupling control goal, such as the airplane model and spacecraft model. In this work, the flight control law is proposed using the neural-based inversion design method associated with the nonlinear compensation for a general longitudinal model of the airplane. First, the nonlinear mathematic model is converted to the equivalent linear model based on the feedback linearization theory. Then, the flight control law integrated with this inversion model is developed to stabilize the nonlinear system and relieve the coupling effect. Afterwards, the inversion control combined with the neural network and nonlinear portion is presented to improve the transient performance and attenuate the uncertain effects on both external disturbances and model errors. Finally, the simulation results demonstrate the effectiveness of this controller.
机译:对于复杂的多输入多输出非线性系统,实现飞机系统和航天器系统等解耦控制目标,反演设计方法是非常有用的工具。在这项工作中,使用与飞机一般纵向模型的非线性补偿相关的基于神经的反演设计方法,提出了飞行控制律。首先,基于反馈线性化理论将非线性数学模型转换为等效线性模型。然后,开发了与该反演模型集成的飞行控制律,以稳定非线性系统并减轻耦合效应。然后,提出了结合神经网络和非线性部分的反演控制,以改善暂态性能并减弱对外部干扰和模型误差的不确定性影响。最后,仿真结果证明了该控制器的有效性。

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