平衡重式叉车防侧翻分层控制研究

中国机械工程

平衡重式叉车防侧翻分层控制研究

夏光1,3;张洋1,3;唐希雯2;谢海1,3;杨猛1,3;高军1,3

1.合肥工业大学汽车工程技术研究院,合肥,230009
2.国防科技大学电子对抗学院,合肥,230037
3.合肥工业大学汽车与交通工程学院,合肥,230009

出版日期:2019-09-10 发布日期:2019-09-11
基金资助:
国家自然科学基金资助项目（51875151）

Research on Anti-rollover Hierarchical Control of Counterbalanced Forklifts

XIA Guang1,3;ZHANG Yang1,3;TANG Xiwen2;XIE Hai1,3;YANG Meng1,3;GAO Jun1,3

1. Automotive Research Institute,Hefei University of Technology,Hefei,230009
2.School of Radar Confrontation,National University of Defense Technology,Hefei,230037
3.School of Automotive and Traffic Engineering,Hefei University of Technology,Hefei,230009

Online:2019-09-10 Published:2019-09-11

摘要/Abstract

摘要： 在分析叉车结构和侧翻机理的基础上，设计了一种防侧倾液压油缸作为防侧翻控制的执行机构，以提供侧向支撑力；提出了一种基于T-S模糊神经网络的防侧翻分层控制方法，将叉车防侧翻进行分层控制：上层采用T-S模糊神经网络对叉车的实时运动状态进行判断，并作为下层控制的依据；中层控制层依据运动状态的划分分别选取对应的策略；下层执行层利用不同策略下执行机构的动作形式来控制模型的输出。仿真与实车试验结果表明，所提方法能够在叉车处于紧急工况下对安全域进行划分，以实现提高叉车安全性的目的。

关键词: 平衡重式叉车, T-S模糊神经网络, 防侧翻, 分层控制

Abstract: Based on the analysis of forklift structures and rollover mechanism, an anti-roll hydraulic cylinder was designed as the actuator for anti-rollover control to provide lateral support forces. An anti-rollover hierarchical control method was proposed based on T-S fuzzy neural network. The forklift anti-rollover was controlled hierarchically: the T-S fuzzy neural network was used by the upper identification layer to judge the real-time motion states of the forklifts and as the basis of the lower layer control, the middle control layer selected the corresponding strategies according to the divisions of the motion states，the lower execution layer controlled the outputs of the model by the action forms of the actuators under different strategies. The simulation and real vehicle test results show that the proposed method may divide the safety domains of the forklifts under the emergency conditions and achieve the purpose of improving the safety of the forklifts.

Key words: counterbalanced forklift, T-S fuzzy neural network, anti-rollover, hierarchical control

中图分类号:

U270.11

夏光1,3;张洋1,3;唐希雯2;谢海1,3;杨猛1,3;高军1,3. 平衡重式叉车防侧翻分层控制研究[J]. 中国机械工程.

XIA Guang1,3;ZHANG Yang1,3;TANG Xiwen2;XIE Hai1,3;YANG Meng1,3;GAO Jun1,3. Research on Anti-rollover Hierarchical Control of Counterbalanced Forklifts[J]. China Mechanical Engineering.

参考文献

[1]龙云波，周成山. EHS成为世界叉车发展的主导因素[J]. 中小企业管理与科技， 2009（1）:187-188.
LONG Yunbo, ZHOU Chengshan. EHS Is the Dominant Factor of Forklift Development[J].Management & Technology of SME，2009（1）:187-188.
[2]FELEZ J, BERMEJO A. Design of a Counterbalance Forklift Based on a Predictive Anti-tip-over Controller[J].Integrated Computer-Aided Engineering,2018,25(3):273-288.
[3]HUANG J, XIAO B. Research on Variable Transmission Ratio and Yaw Rate Control Strategy of Electric Forklift Steering-by-wire System[J]. Advances in Mechanical Engineering, 2016, 8(10):21-30.
[4]谢海. 平衡重式叉车主动安全技术研究[D].合肥：合肥工业大学，2017.
XIE Hai. Research on Active Safety Technology of Counterbalanced Forklift [D].Hefei：Hefei University of Technology,2017.
[5]REBELLE J, MISTROT P, POIROT R. Development and Validation of a Numerical Model for Predicting Forklift Truck Tip-over[J]. Vehicle System Dynamics, 2009, 47(7): 771-804.
[6]LAMBERT J. Forklift Stability and Other Technical Safety Issues[J]. Accident Research Centre Report, 2013,15（4）:68-77.
[7]ISHIKAWA K. Apparatus and Method for Restricting Pivoting of Industrial Vehicles Axles: US, 6,175,796[P]. 2001-1-16.
[8]ISHIKAWA K. Swing Control Apparatus for Industrial Vehicle: US, 5,947,516[P]. 1999-9-7.
[9]陈璜.基于虚拟样机技术的叉车工作装置的建模与动力学研究[D].福州：福州大学,2014.
CHEN Huang.Modeling and Dynamics Research on Forklift Based on Virtual Prototype Technology[D].Fuzhou：Fuzhou University,2014.
[10]JANG J S R, SUN C T. Neuro-fuzzy Modeling and Control[J]. Proceedings of the IEEE, 1995, 83(3): 378-406.
[11]CPALKA K, RUTKOWSKI L. Flexible Takagi-Sugeno Neuro-fuzzy Structures for Nonlinear Approximation[J]. WSEAS Transactions on Systems, 2005, 4(9): 1450-1458.
[12]ISHIKAWA K, SUGIURA K, SUZUKI M, et al. Stability Control Apparatus for Industrial Vehicles: US， 6,719,098[P]. 2004-4-13.
[13]NAUCK D, KRUSE R. A Neuro-fuzzy Method to Learn Fuzzy Classification Rules from Data[J]. Fuzzy Sets and Systems, 1997, 89(3): 277-288.
[14]LIN F J, LIN C H. A Permanent-magnet Synchronous Motor Servo Drive Using Self-constructing Fuzzy Neural Network Controller[J]. IEEE Transactions on Energy Conversion, 2004, 19(1): 66-72.
[15]王俊国. 基于神经网络的建模方法与控制策略研究[D].武汉：华中科技大学,2004.
WANG Junguo.Research on Modeling Method and Control Strategy Based on Neural Network[D]. Wuhan：Huazhong University of Science and Technology,2004.
[16]CHU Z, ZHU D, YANG S X. Observer-based Adaptive Neural Network Trajectory Tracking Control for Remotely Operated Vehicle[J]. IEEE Transactions on Neural Networks and Learning Systems, 2017，28（7）：1633-1645.
[17]毕成亚, 郑颖龙,吴燕红. 平衡重式叉车动态稳定性试验研究[J]. 工程机械，2014，45(12)：19-24.
BI Chengya，ZHENG Yinglong，WU Yanhong. Experimental Study on Dynamic Stability of Balance Truck[J]. Engineering Machinery，2014，45(12)：19-24.