Model-Free Adaptive Control for Tank Truck Rollover Stabilization | Zendy

Xiansheng Li | Zendy; Yuanyuan Ren | Zendy; Xuelian Zheng | Zendy

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Model-Free Adaptive Control for Tank Truck Rollover Stabilization

Author(s) -

Xiansheng Li,

Yuanyuan Ren,

Xuelian Zheng

Publication year - 2021

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2021/8417071

Subject(s) - rollover (web design) , truck , control theory (sociology) , engineering , slosh dynamics , vehicle dynamics , robustness (evolution) , controller (irrigation) , nonlinear system , oil tank , automobile handling , adaptive control , automotive engineering , control (management) , computer science , structural engineering , mechanical engineering , agronomy , biochemistry , chemistry , physics , quantum mechanics , artificial intelligence , biology , world wide web , gene

Influenced by lateral liquid sloshing in partially filled tanks, tank vehicles are apt to encounter with rollover accidents. Due to its strong nonlinearity and loading state uncertainty, it has great challenges in tank vehicle active control. Based on the model-free adaptive control (MFAC) theory, the roll stability control problem of tank trucks with different tank shapes and liquid fill percentages is explored. First, tank trucks equipped with cylinder or elliptical cylinder tanks are modelled, and vehicle dynamics is analyzed. This dynamic model is used to provide I/O data in the controlled system. Next, the control objective of tank vehicle rollover stabilization is analyzed and the controlled variable is selected. Subsequently, differential braking and active front steering controller are designed by MFAC algorithm. Finally, the effectiveness of the designed controllers is verified by simulation, and difference between the controllers is analyzed. The controller designed by MFAC algorithm is proven to be adaptive to vehicle loading and driving states. The controlled system has great robustness.

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