比例减压阀控缸输出力的非线性自适应控制

Translated title of the contribution: Nonlinear Adaptive Control of Output Force for a Cylinder Controlled by Proportional Pressure Reducing Valves

Xiangzhou Wang, Qian Han, Ningning Zhang*, Shuhua Zheng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

The paper focuses on a blade-type conveyor belt cleaner operated by a dual direct-acting three-way proportional pressure-reducing valve-controlled (PPRV) asymmetric hydraulic cylinder. It proposed an adaptive control algorithm based on PI (Prandtl-Ishlinskii) inverse feedforward compensation to mitigate the pressure hysteresis,dead zone and gain nonlinearity inherent in the PPRVs. The primary aim was to ensure precise control of the blade's driving torque in a conveyor belt cleaner. The algorithm was designed to maintain consistent pressure against the conveyor belt and ensure effective cleaning throughout the blade's entire lifecycle. Through online identification of PI model parameters using a recursive least squares method with forgetting factors, the inverse model was resolved, enabling feedforward compensation for linear active disturbance rejection control based on the hysteresis characteristics of the PPRVs. Simulation and experimental results demonstrate the effectiveness of the proposed algorithm in reducing PPRV hysteresis,dead zone and gain nonlinearity, thereby improving the system's dynamic response. Compared to the PID algorithm, the proposed method exhibits a 65% increase in dynamic response, with blade driving torque error controlled within ±2 N·m in the torque range of 100~700 N·m.

Translated title of the contributionNonlinear Adaptive Control of Output Force for a Cylinder Controlled by Proportional Pressure Reducing Valves
Original languageChinese (Traditional)
Pages (from-to)1066-1076
Number of pages11
JournalBeijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
Volume44
Issue number10
DOIs
Publication statusPublished - Oct 2024

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