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Dual-valve parallel prediction control for an electro-hydraulic servo system.
Su, Shi-Jie; Zhu, Yuan-Yuan; Li, Cun-Jun; Tang, Wen-Xian; Wang, Hai-Rong.
Afiliación
  • Su SJ; School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang, China.
  • Zhu YY; School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang, China.
  • Li CJ; Zhoushan Institute of Calibration and Testing for Quality and Technology Supervision, Zhoushan, China.
  • Tang WX; School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang, China.
  • Wang HR; Zhoushan Institute of Calibration and Testing for Quality and Technology Supervision, Zhoushan, China.
Sci Prog ; 103(1): 36850419875662, 2020.
Article en En | MEDLINE | ID: mdl-31829861
ABSTRACT
To improve the dynamic response performance of a high-flow electro-hydraulic servo system, scholars have conducted considerable research on the synchronous and time-sharing controls of multiple valves. However, most scholars have used offline optimization to improve control performance. Thus, control performance cannot be dynamically adjusted or optimized. To repeatedly optimize the performance of multiple valves online, this study proposes a method for connecting a high-flow proportional valve in parallel with a low-flow servo valve. Moreover, this study proposes an algorithm in which a proportional-integral-derivative system and multivariable predictive control system are used as an inner loop and outer loop, respectively. The simulation and experimental results revealed that dual-valve parallel control could effectively improve the control accuracy and dynamic response performance of an electro-hydraulic servo system and that the proportional-integral-derivative-multivariable predictive control controller could further dynamically improve the control accuracy.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Algoritmos Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Sci Prog Año: 2020 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Algoritmos Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Sci Prog Año: 2020 Tipo del documento: Article País de afiliación: China