Adaptive Tracking Control for Active Seat Suspension System with Time-Varying Full State Constraints

被引：0
|
作者：
Chen, Aiqing [1 ]
Liu, Yan-Jun [1 ]
Liu, Lei [1 ]
Gao, Ying [2 ]
机构：
[1] Liaoning Univ Technol, Coll Sci, Jinzhou, Peoples R China
[2] Tangshan Normal Univ, Dept Math & Informat Sci, Tangshan, Peoples R China
来源：
2019 3RD CONFERENCE ON VEHICLE CONTROL AND INTELLIGENCE (CVCI) | 2019年
基金：
中国国家自然科学基金;
关键词：
active seat suspension; full state constraints; RBF; Tangent Barrier Lyapunov Function; WHOLE-BODY VIBRATION; EXPOSURES;
D O I：
10.1109/cvci47823.2019.8951618
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This study investigates a kind of active seat suspension systems with time-varying full state constraints (TFSCs), whose aim is to improve the riding comfort. In order to handle the unknown functions, the radial basis function neural networks (RBFNNs) are adopted. Combining the Tangent Barrier Lyapunov Function (BLF-Tan) with backstepping technique, a novel adaptive controller is designed for active seat suspension systems. It is proven that all signals in the resulted system are semiglobally uniformly ultimately bounded (SGUUB) with TFSCs. Further, the simulation results demonstrate the effectiveness of presented control method.
引用
收藏
页码：126 / 131
页数：6
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