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Improved methods in neural network-based adaptive output feedback control, with applications to flight control.

机译:基于神经网络的自适应输出反馈控制中的改进方法及其在飞行控制中的应用。

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摘要

Utilizing the universal approximation property of neural networks, we develop several novel approaches to neural network-based adaptive output feedback control of nonlinear systems, and illustrate these approaches for several flight control applications. In particular, we address the problem of non-affine systems and eliminate the fixed point assumption present in earlier work. All of the stability proofs are carried out in a form that eliminates an algebraic loop in the neural network implementation. An approximate input/output feedback linearizing controller is augmented with a neural network using input/output sequences of the uncertain system. These approaches permit adaptation to both parametric uncertainty and unmodeled dynamics. All physical systems also have control position and rate limits, which may either deteriorate performance or cause instability for a sufficiently high control bandwidth. Here we apply a method for protecting an adaptive process from the effects of input saturation and time delays, known as “pseudo control hedging”. This method was originally developed for the state feedback case, and we provide a stability analysis that extends its domain of applicability to the case of output feedback. The approach is illustrated by the design of a pitch-attitude flight control system for a linearized model of an R-50 experimental helicopter, and by the design of a pitch-rate control system for a 58-state model of a flexible aircraft consisting of rigid body dynamics coupled with actuator and flexible modes.; A new approach to augmentation of an existing linear controller is introduced. It is especially useful when there is limited information concerning the plant model, and the existing controller. The approach is applied to the design of an adaptive autopilot for a guided munition. Design of a neural network adaptive control that ensures asymptotically stable tracking performance is also addressed.
机译:利用神经网络的通用逼近特性,我们开发了几种基于神经网络的非线性系统自适应输出反馈控制的新颖方法,并针对多种飞行控制应用说明了这些方法。特别是,我们解决了非仿射系统的问题,并消除了早期工作中存在的定点假设。所有稳定性证明均以消除神经网络实现中的代数环的形式进行。使用不确定系统的输入/输出序列,通过神经网络来增强近似输入/输出反馈线性化控制器。这些方法可以适应参数不确定性和未建模的动力学。所有物理系统还具有控制位置和速率限制,这可能会降低性能或导致对于足够高的控制带宽不稳定。在这里,我们采用了一种保护自适应过程不受输入饱和和时间延迟影响的方法,称为“伪控制套期”。此方法最初是针对状态反馈情况开发的,我们提供了稳定性分析,将其适用范围扩展到输出反馈情况。通过为R-50实验直升机的线性化模型设计俯仰-姿态飞行控制系统,以及为包含以下各项的柔性飞机的58状态模型设计俯仰角控制系统来说明该方法:刚体动力学结合执行器和灵活模式。介绍了一种增加现有线性控制器的新方法。当关于工厂模型和现有控制器的信息有限时,此功能特别有用。该方法适用于制导武器的自适应自动驾驶仪的设计。还解决了确保渐近稳定的跟踪性能的神经网络自适应控制的设计。

著录项

  • 作者

    Kim, Nakwan.;

  • 作者单位

    Georgia Institute of Technology.;

  • 授予单位 Georgia Institute of Technology.;
  • 学科 Engineering Aerospace.
  • 学位 Ph.D.
  • 年度 2003
  • 页码 p.6179
  • 总页数 211
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 航空、航天技术的研究与探索;
  • 关键词

  • 入库时间 2022-08-17 11:45:55

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