Feedback linearization and feedforward neural network control with application to twin rotor mechanism

Chia Wei Lin, Tzuu Hseng S. Li, Chung Cheng Chen

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

A twin rotor multi-input multi-output system (TRMMS) is a high-order nonlinear system with a significant cross-coupling effect. The control of TRMMSs is considered a markedly challenging topic in the field of robust control. This study proposes a novel feedback linearization and feedforward neural network controller design for a TRMMS with almost disturbance decoupling (ADD) capabilities. The proposed composite controller achieves exponentially global stability and ADD performance without applying any traditional parallel learning algorithms. This study proposes an organization of the feedforward neural network and the weights among the layers to guarantee the stability of the overall system. A number of nonlinear systems, which are too complex to be solved by general ADD studies, are proposed in this study to demonstrate that the proposed methodology can effectively achieve the tracking and ADD performances through Matlab. Moreover, an efficient algorithm is proposed for designing the feedback linearization and feedforward neural network control with ADD and tracking capabilities.

Original languageEnglish
Pages (from-to)351-362
Number of pages12
JournalTransactions of the Institute of Measurement and Control
Volume40
Issue number2
DOIs
Publication statusPublished - 2018 Jan 1

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All Science Journal Classification (ASJC) codes

  • Instrumentation

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