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A Robust Neural Network Controller for a TITO Interactive Nonlinear System

机译:用于TITO交互式非线性系统的强大神经网络控制器

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This paper presents a robust NN control scheme for diving behavior of an autonomous underwater vehicle (AUV) whose dynamics can be simplified as a second-order TITO (two-input-two-output) nonlinear function. Because of singularity problem, above dynamics can't be properly solved using general backstepping method although it is in a well known strict-feedback form. Furthermore, the dynamics is in an interactive form so the traditional noninteracting control methods also can not be directly applied. In this paper, the value of one of two virtual inputs is derived from predefined vehicle's desired trajectory instead of stability point of view so the singularity problem can be avoided. Proposed scheme can guarantee all of the signals in the closed-loop system are semi-global uniformly ultimately bounded (SGUUB).
机译:本文介绍了一种稳健的NN控制方案,用于自主水下车辆(AUV)的潜水行为,其动力学可以被简化为二阶标准(二输入 - 二输出)非线性功能。由于奇点问题,使用普通反馈方法不能正确解决上述动态,尽管它处于众所周知的严格反馈形式。此外,动力学是以交互式形式的,因此传统的非交互控制方法也不能直接应用。在本文中,两个虚拟输入之一的值来自预定的车辆的期望轨迹而不是稳定性的观点来实现,因此可以避免奇点问题。所提出的方案可以保证闭环系统中的所有信号是半全局均匀最终有限(SGUUB)。

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