title: Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures.
abstract: 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.
authors.affiliation: State Key Laboratory of Rail Traffic Control and Safety, Beijng Jiaotong University, Beijing 100044, China. 09111018@bjtu.edu.cn
authors.forename: Qi
authors.initials: Q
authors.lastname: Song
authors.forename: Yong-Duan
authors.initials: YD
authors.lastname: Song
mesh_terms: D001185:Artificial Intelligence; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D019544:Equipment Failure Analysis / Q000295:instrumentation* / Q000379:methods; D005246:Feedback; D017711:Nonlinear Dynamics; D014159:Transducers; D014186:Transportation / Q000295:instrumentation*