Model-free adaptive fault-tolerant control for subway trains with speed and traction/braking force constraints

被引:36
作者
Wang, Haojun [1 ]
Hou, Zhongsheng [2 ]
机构
[1] Beijing Jiaotong Univ, Sch Elect & Informat Engn, Beijing 100044, Peoples R China
[2] Qingdao Univ, Sch Automat, Qingdao 266071, Peoples R China
基金
中国国家自然科学基金; 北京市自然科学基金;
关键词
learning systems; discrete time systems; nonlinear control systems; braking; learning (artificial intelligence); control system synthesis; radial basis function networks; time-varying systems; traction; adaptive control; neurocontrollers; actuators; subway train fault-tolerant control problem; actuator fault; complex subway train dynamics; compact form dynamic linearisation data model; model-free adaptive control; uncertainty fault; model-free adaptive fault-tolerant control scheme; SYSTEMS; DESIGN; TRACKING;
D O I
10.1049/iet-cta.2019.1161
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This study investigates the subway train fault-tolerant control problem for the actuator fault with constraints of speed and traction/braking force. The complex subway train dynamics is first transformed into a compact form dynamic linearization data model with the help of the concept pseudo-partial derivative (PPD) proposed under the framework of model-free adaptive control. By using the approximation of the uncertainty fault with radial basis function neural network (RBFNN), then a model-free adaptive fault-tolerant control (MFAFTC) scheme is designed by only using saturated input/output data of a subway train. The proposed MFAFTC scheme consists of two data driven on-line learning updating algorithms for RBFNN-weights and PPD, and its convergence is strictly proved by rigorous theoretical analysis, and whose correctness and effectiveness are further verified by a numerical simulation.
引用
收藏
页码:1557 / 1566
页数:10
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