Speed tracking control of pneumatic motor servo systems using observation-based adaptive dynamic sliding-mode control

Syuan Yi Chen, Sheng Sian Gong

Research output: Contribution to journalArticle

25 Citations (Scopus)

Abstract

This study aims to develop an adaptive high-precision control system for controlling the speed of a vane-type air motor (VAM) pneumatic servo system. In practice, the rotor speed of a VAM depends on the input mass air flow, which can be controlled by the effective orifice area (EOA) of an electronic throttle valve (ETV). As the control variable of a second-order pneumatic system is the integral of the EOA, an observation-based adaptive dynamic sliding-mode control (ADSMC) system is proposed to derive the differential of the control variable, namely, the EOA control signal. In the ADSMC system, a proportional–integral–derivative fuzzy neural network (PIDFNN) observer is used to achieve an ideal dynamic sliding-mode control (DSMC), and a supervisor compensator is designed to eliminate the approximation error. As a result, the ADSMC incorporates the robustness of a DSMC and the online learning ability of a PIDFNN. To ensure the convergence of the tracking error, a Lyapunov-based analytical method is employed to obtain the adaptive algorithms required to tune the control parameters of the online ADSMC system. Finally, our experimental results demonstrate the precision and robustness of the ADSMC system for highly nonlinear and time-varying VAM pneumatic servo systems.

Original languageEnglish
Pages (from-to)111-128
Number of pages18
JournalMechanical Systems and Signal Processing
Volume94
DOIs
Publication statusPublished - 2017 Sep 15

Keywords

  • Dynamic sliding-mode control
  • Fuzzy neural network
  • Pneumatic servo system
  • Speed control
  • Vane-type air motor

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Civil and Structural Engineering
  • Aerospace Engineering
  • Mechanical Engineering
  • Computer Science Applications

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