Neuro controller design for aircraft auto-landing under actuator failure and severe wind condition

Zhifeng Wang, Zhongjian Li
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Abstract

In this paper, a robust Neural Network (neuro) aided H2 control scheme is developed for a nonlinear F16 aircraft in auto-landing phase under actuator failure and severe wind conditions. In this scheme, a dynamic Radial Basis Function network called Minimal Resource Allocating Network (MRAN) that incorporates a growing and pruning strategy, is utilized to aid H2 controller using a feedback-error-learning mechanism. Specifically, the performance of this neuro-H2 controller for an aircraft auto-landing is studied and compared with H2 control schemes. Simulation studies show that the performance obtained by the neuro-H2 controller scheme is better than H2 controller.
执行器失效和大风条件下飞机自动降落的神经控制器设计
针对F16飞机在执行器失效和大风条件下的自动着陆阶段,提出了一种鲁棒神经网络辅助H2控制方案。在这个方案中,一个动态的径向基函数网络称为最小资源分配网络(MRAN),它结合了生长和修剪策略,利用反馈-错误学习机制来帮助H2控制器。具体而言,研究了该神经H2控制器在飞机自动着陆中的性能,并与H2控制方案进行了比较。仿真研究表明,神经-H2控制器方案的性能优于H2控制器。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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