@inproceedings{54293ce730a64cb58eb13a9a8d626ebf,
title = "Adaptive optimal integral sliding mode control for a dual-motor driving servo system",
abstract = "In this paper, an adaptive optimal integral sliding mode controller is presented to realize the load position tracking for a multi-motor driving servo system (MDSS). By reformulating the MDSS in a strict feedback form, a neural network state observer is designed to estimate both immeasurable states and unknown nonlinearities. Based on this state observer, two adaptive optimal integral sliding mode controllers are developed and integrated into one tracking controller through the backstepping approach. The proposed controllers can not only guarantee a satisfactory load tracking performance but also increase the robustness to system uncertainties. By using the Lyapunov stability theorem, it is certified that all signals of the MDSS are uniformly ultimately bounded. Simulation results on a four-motor driving servo system are conducted to validate the efficiency of the proposed control scheme.",
keywords = "Multi-motor driving servo system, integral sliding mode control, neural network state observer, optimal control",
author = "Minlin Wang and Xuemei Ren and Linwei Li and Qiang Chen",
note = "Publisher Copyright: {\textcopyright} 2017 Technical Committee on Control Theory, CAA.; 36th Chinese Control Conference, CCC 2017 ; Conference date: 26-07-2017 Through 28-07-2017",
year = "2017",
month = sep,
day = "7",
doi = "10.23919/ChiCC.2017.8027441",
language = "English",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "793--798",
editor = "Tao Liu and Qianchuan Zhao",
booktitle = "Proceedings of the 36th Chinese Control Conference, CCC 2017",
address = "United States",
}