Purpose

To guarantee the asymptotic stability of discrete-time nonlinear systems, this paper aims to propose an evolved bat algorithm fuzzy neural network (NN) controller algorithm.

Design/methodology/approach

In evolved fuzzy NN modeling, the NN model and linear differential inclusion representation are established for the arbitrary nonlinear dynamics. The control problems of the Fisher equation and a temperature cooling fin for high-speed aerospace vehicles will be described and demonstrated. The signal auxiliary controlled system is represented for the nonlinear parabolic partial differential equation (PDE) systems and the criterion of stability is derived via the Lyapunov function in terms of linear matrix inequalities.

Findings

This representation is constructed by sector nonlinearity, which converts the nonlinear model to a multiple rule base for the linear model and a new sufficient condition to guarantee the asymptotic stability.

Originality/value

This study also injects high frequency as an auxiliary and the control performance to stabilize the nonlinear high-speed aerospace vehicle system.

中文翻译：

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应用于航空航天的改进型辅助控制器

目的

为了保证离散时间非线性系统的渐近稳定性，本文旨在提出一种进化蝙蝠算法模糊神经网络（NN）控制器算法。

设计/方法/方法

在进化模糊神经网络建模中，为任意非线性动力学建立神经网络模型和线性微分包含表示。将描述和演示费舍尔方程的控制问题和高速航天飞行器的温度冷却翅片。信号辅助控制系统表示为非线性抛物线偏微分方程 (PDE) 系统，稳定性判据是通过 Lyapunov 函数根据线性矩阵不等式导出的。

发现

该表示由扇区非线性构造，将非线性模型转化为线性模型的多重规则库和保证渐近稳定性的新充分条件。

原创性/价值

本研究还注入高频作为辅助和控制性能来稳定非线性高速航天器系统。