Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS
Volume 22, Issue 12, Pages 2250-2261Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNN.2011.2175451
Keywords
Data-based; fault-tolerant; input nonlinearities; neuroadaptive control
Categories
Funding
- National Natural Science Foundation of China [60974052, 61134001]
- Program for Changjiang Scholars and Innovative Research Team in University [IRT0949]
- Beijing Jiaotong University [RCS2008ZT002, 2009JBZ001, 2009RC008]
- Innovation Foundation of Beijing Jiaotong University [2011YJS009]
- Major State Basic Research Development Program 973 [2012CB215202]
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This paper investigates the position and velocity tracking control problem of high-speed trains with multiple vehicles connected through couplers. A dynamic model reflecting nonlinear and elastic impacts between adjacent vehicles as well as traction/braking nonlinearities and actuation faults is derived. Neuroadaptive fault-tolerant control algorithms are developed to account for various factors such as input nonlinearities, actuator failures, and uncertain impacts of in-train forces in the system simultaneously. The resultant control scheme is essentially independent of system model and is primarily data-driven because with the appropriate input-output data, the proposed control algorithms are capable of automatically generating the intermediate control parameters, neuro-weights, and the compensation signals, literally producing the traction/braking force based upon input and response data only-the whole process does not require precise information on system model or system parameter, nor human intervention. The effectiveness of the proposed approach is also confirmed through numerical simulations.
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