Control oriented model order reduction for the hydraulic system of an autonomous wheel loader

Journal Article · 18 August 2023 · Control Engineering Practice

DOI: https://doi.org/10.1016/j.conengprac.2023.105628 · OSTI ID:2464099

Yu, Sencheng  ^[1]; Zhao, Gaonan ^[2]; Song, Xingyong ^[3]; Sun, Zongxuan ^[2]

---

1. Texas A&M University, College Station, TX (United States); University of Minnesota
2. University of Minnesota at Twin Cities, Minneapolis, MN (United States)
3. Texas A&M University, College Station, TX (United States)

Control design for an autonomous wheel loader requires a high-fidelity and low-order dynamics model. The objective of order reduction is to decrease the number of states in the model while maintaining performance comparable to the original model. Of all the power components in a wheel loader, the model order reduction for steering and working hydraulic systems is most demanding due to its nonlinear and stiff dynamical nature. This paper describes a physics-inspired model order reduction method that can reduce the model order by close to thirty percent. It is achieved by discarding orders with fast dynamics and consolidating multiple states into less new states. Furthermore, this method can also be extended to other types of off-road vehicles such as excavators, bulldozers, etc. Simulation results demonstrate that the reduced-order model exhibits very similar performance to the full-order model under typical operating conditions of wheel loaders, with an output error of less than six percent.

• Research Organization: University of Minnesota, Minneapolis, MN (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Vehicle Technologies Office (VTO)
• Grant/Contract Number: EE0009200
• OSTI ID: 2464099
• Journal Information: Control Engineering Practice, Journal Name: Control Engineering Practice Vol. 139; ISSN 0967-0661
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English