Purpose

This paper aims to extend the previous contributions about data-driven control in aircraft control system from academy and practice, respectively, combining iteration and learning strategy. More specifically, after returning output signal to input part, and getting one error signal, three kinds of data are measured to design the unknown controller without any information about the unknown plant. Using the main essence of data-driven control, iterative learning idea is introduced together to yield iterative learning data-driven control strategy. To get the optimal data-driven controller, other factors are considered, for example, adaptation, optimization and learning. After reviewing the aircraft control system in detail, the numerical simulation results have demonstrated the efficiency of the proposed iterative learning data-driven control strategy.

Design/methodology/approach

First, considering one closed loop system corresponding to the aircraft control system, data-driven control strategy is used to design the unknown controller without any message about the unknown plant. Second, iterative learning idea is combined with data-driven control to yield iterative learning data-driven control strategy. The optimal data-driven controller is designed by virtue of power spectrum and mathematical optimization. Furthermore, adaptation is tried to combine them together. Third, to achieve the combination with theory and practice, our proposed iterative learning data-driven control is applied into aircraft control system, so that the considered aircraft can fly more promptly.

Findings

A novel iterative learning data-driven strategy is proposed to efficiently achieve the combination with theory and practice. First, iterative learning and data-driven control are combined with each other, being dependent of adaptation and optimization. Second, iterative learning data-driven control is proposed to design the flight controller for the aircraft system. Generally, data-driven control is more wide in our living life, so it is important to introduce other fields to improve the performance of data-driven control.

Originality/value

To the best of the authors’ knowledge, this new paper extends the previous contributions about data-driven control by virtue of iterative learning strategy. Specifically, iteration means that the optimal data-driven controller is solved as one recursive form, being related with one gradient descent direction. This novel iterative learning data-driven control has more advanced properties, coming from data driven and adaptive iteration. Furthermore, it is a new subject on applying data-driven control into the aircraft control system.

中文翻译：

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飞机控制系统迭代学习数据驱动策略

目的

本文旨在结合迭代和学习策略，分别从学术和实践中扩展先前关于飞机控制系统数据驱动控制的贡献。更具体地说，在将输出信号返回到输入部分并得到一个误差信号之后，测量三种数据来设计未知控制器，而无需任何关于未知对象的信息。利用数据驱动控制的主要本质，引入迭代学习思想，产生迭代学习数据驱动控制策略。为了获得最佳的数据驱动控制器，需要考虑其他因素，例如适应、优化和学习。在详细审查了飞机控制系统后，

设计/方法论/途径

首先，考虑与飞机控制系统相对应的一个闭环系统，使用数据驱动的控制策略来设计未知控制器，而无需任何有关未知对象的消息。其次，将迭代学习思想与数据驱动控制相结合，产生迭代学习数据驱动控制策略。借助功率谱和数学优化设计最优数据驱动控制器。此外，改编试图将它们结合在一起。第三，为了实现理论与实践的结合，我们提出的迭代学习数据驱动控制应用于飞机控制系统，使所考虑的飞机能够更迅速地飞行。

发现

提出了一种新颖的迭代学习数据驱动策略，以有效实现理论与实践的结合。首先，迭代学习和数据驱动控制相互结合，依赖于适应和优化。其次，提出迭代学习数据驱动控制来设计飞行器系统的飞行控制器。一般来说，数据驱动控制在我们的生活中更为广泛，因此引入其他领域来提高数据驱动控制的性能非常重要。

原创性/价值

据作者所知，这篇新论文通过迭代学习策略扩展了之前关于数据驱动控制的贡献。具体来说，迭代意味着最优数据驱动控制器被求解为一种递归形式，与一个梯度下降方向相关。这种新颖的迭代学习数据驱动控制具有更先进的属性，来自数据驱动和自适应迭代。此外，将数据驱动控制应用于飞行器控制系统也是一个新课题。