Abstract
The discrete controller is developed for the longitudinal dynamics of the hypersonic flight vehiclewith neural networks (NNs). The focus is on the control of the Euler equations of attitude dynamics. Withback-stepping design, for each step the virtual control is constructed with NNs approximating the unknowndynamics. The design uses less online adaption parameter learning scheme to achieve the control goal. It isguaranteed that the errors of all the signals in the system are uniformly ultimately bounded. The proposedmethod is verified by simulation of winged-cone model.
创新点
本文针对高超声速飞行器的纵向通道模型, 建立了一种离散控制方法。围绕姿态动力学的欧拉方程采用反步法策略进行控制器设计, 在每一步都设计了一个虚拟控制量。针对模型中存在的未知动力学, 通过神经网络来逼近。与已有的研究不同, 本文的创新点在于利用最小在线参数学习策略实现离散控制器的设计, 并通过李雅普诺夫方法证明了所建立的控制器可保证闭环系统信号是一致终值有界的。最后通过winged-cone模型进行了仿真, 验证了所建立算法的有效性。
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Zhi, Y., Yang, Y. Discrete control of longitudinal dynamics for hypersonic flight vehicle using neural networks. Sci. China Inf. Sci. 58, 1–10 (2015). https://doi.org/10.1007/s11432-015-5351-5
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DOI: https://doi.org/10.1007/s11432-015-5351-5