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Adaptive Dynamic Surface Control for a Hypersonic Aircraft Using Neural Networks

机译：基于神经网络的超音速飞机自适应动态表面控制

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摘要

A hypersonic aircraft dynamic model is highly nonlinear because its flight conditions are usually determined at high altitude and Mach number. Therefore, there always exist differences between the dynamical model and real system, and uncertainties during the flight, thus, leading significant degradation of control performance. To solve the performance degradation problem, this paper proposes neural networks-based adaptive velocity and altitude tracking controllers. In order for that, the hypersonic aircraft model is transformed into an uncertain feedback system, which has both matched and unmatched uncertainties, by differentiating the velocity and altitude with respect to time. Then, the overall tracking control system is designed systematically by introducing virtual control inputs and dynamic surface control. During the design process, an inverse of an input gain matrix is directly trained and adapted to remove the matched uncertainty and controller singularity problem simultaneously. In addition, several adaptive elements with saturation functions are added to handle all the matched and unmatched uncertainties. The proposed controller guarantees the uniformly ultimate boundedness of the tracking error by utilizing deadzoned errors. Finally, numerical simulations with the uncertain hypersonic aircraft are performed to demonstrate the effectiveness of the proposed approach.
机译：高超声速飞行器动力学模型是高度非线性的，因为其飞行条件通常是在高海拔和马赫数下确定的。因此，动力学模型和实际系统之间始终存在差异，并且飞行过程中存在不确定性，从而导致控制性能显着下降。为了解决性能下降的问题，本文提出了一种基于神经网络的自适应速度和高度跟踪控制器。为此，通过区分速度和高度随时间的变化，将高超音速飞机模型转换为具有匹配和不匹配不确定性的不确定反馈系统。然后，通过引入虚拟控制输入和动态表面控制系统地设计了整个跟踪控制系统。在设计过程中，直接训练输入增益矩阵的逆并进行调整，以同时消除匹配的不确定性和控制器奇点问题。此外，添加了几个具有饱和功能的自适应元件，以处理所有匹配和不匹配的不确定性。所提出的控制器通过利用死区误差来保证跟踪误差的统一极限极限。最后，用不确定的高超音速飞机进行了数值模拟，以证明所提方法的有效性。

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