Your browser doesn't support javascript.
loading
Output feedback model predictive control of hydraulic systems with disturbances compensation.
Gu, Weiwei; Yao, Jianyong; Yao, Zhikai; Zheng, Jingzhong.
Afiliação
  • Gu W; School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China. Electronic address: guweiwei_njust@163.com.
  • Yao J; School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China. Electronic address: jerryyao.buaa@gmail.com.
  • Yao Z; School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China. Electronic address: zacyao.cn@gmail.com.
  • Zheng J; School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China. Electronic address: zjz@njust.edu.cn.
ISA Trans ; 88: 216-224, 2019 May.
Article em En | MEDLINE | ID: mdl-30580881
Enhancing the robustness of output feedback control has always been an important issue in hydraulic servo systems. In this paper, an output feedback model predictive controller (MPC) with the integration of an extended state observer (ESO) is proposed for hydraulic systems. The ESO was designed to estimate not only the unmeasured system states but also the disturbances, which will be synthesized into the design of the output prediction equation. Based on the mechanism of receding horizon and repeating optimization of MPC, the output prediction equation will be updated in real time and the future behavior of the system will be accurately predicted since the disturbances are compensated effectively. Hence, the ability of the traditional MPC to suppress disturbances will be improved evidently. The experiment results show that the proposed controller has high-performance nature and strong robustness against various model uncertainties, which verifies the effectiveness of the proposed control strategy.
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2019 Tipo de documento: Article