Abstract
High-speed on/off valves (HSVs) are recognized for their rapid response and high reliability, and are widely employed as pilot elements in proportional valves. However, their inherent switching behavior intensifies the nonlinear characteristics of the control system, thereby limiting fluid delivery precision. In addition, the main spool of the proportional valve is subjected to significantly time-varying load forces. To address these challenges, a nonlinear flow model of the HSV is established, and the dynamics of pressure pulsation propagation are systematically analyzed. Meanwhile, a dynamic model of the proportional valve main spool is developed, explicitly incorporating the effects of time-varying load forces. Based on these models, a load-adaptive sliding mode control (LASMC) strategy is proposed to improve the motion control performance of a high-speed on/off valve–piloted proportional valve (HSVPPV). Motion control experiments conducted on the HSVPPV and its valve-controlled system validated the proposed approach. Compared to conventional PI control, LASMC reduced the main spool’s maximum displacement error from 0.696 mm to 0.312 mm (a 55.2% reduction) and standard deviation from 0.213 mm to 0.127 mm (a 40.4% reduction). At the actuator level, the cylinder’s maximum error decreased from 14.996 mm to 11.527 mm (a 23.1% reduction) and standard deviation from 11.347 mm to 10.323 mm (a 9.0% reduction). These results demonstrate that the proposed controller significantly enhances both valve positioning accuracy and overall system tracking stability.
| Original language | English |
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| Journal | IEEE Transactions on Automation Science and Engineering |
| Early online date | 5 Jan 2026 |
| DOIs | |
| Publication status | E-pub ahead of print - 5 Jan 2026 |
Funding
This work was supported by the National Natural Science Foundation of China (Grant No. 52005441), the Young Elite Scientist Sponsorship Program by CAST (Grant 2022QNRC001), the Fundamental Research Funds for the Provincial Universities of Zhejiang (Grant RF-A2023007), and the Research Project of ZJUT (Grant GYY-ZH2023075).