Rate-dependent hysteretic nonlinearity, which is an inherent characteristic of piezoelectric actuators (PEAs), causes a significant challenge in precise motion control of piezoelectric nanopositioning stages. In this paper, by assuming that the model of PEA takes a Hammerstein structure, a novel control strategy that combines iterative learning control (ILC) and the direct inverse of hysteresis is proposed to compensate for both nonlinearities and uncertainties of system simultaneously. Different from those existing direct inverse compensation methods whose control performance highly relies on the accuracy of the hysteresis model, the proposed control strategy is more robust by adding an additional ILC loop. Since ILC is essentially a feedforward control scheme that fully utilizes the input and output information in previous iterations, the tracking precision can be improved promptly in the iteration domain. Comparative experiments are performed to test the efficacy of the proposed algorithm for polynomial, triangular, and step signals. Results show that it is superior to pure proportional–integral (PI) controller and even PI controller combined with inverse compensator in the sense that the root mean square, relative, as well as maximal absolute errors of output tracking have been decreased remarkably within five iterations.

The precise tracking control of high-speed train is an essential prerequisite to ensure the safety and comfort of the train. In this paper, an adaptive iterative learning control (ILC) scheme for the velocity and displacement tracking of high-speed train is proposed to handle the unknown time-varying parameters and lumped uncertainties. The composite energy function (CEF) method is used to analyze the stability of closed-loop system. Since the train usually runs on the same railway periodically, such as the same tunnels, slopes, bridges, etc., ILC is an inherent method for designing the tracking controller that is able to improve the operation performance of train iteratively. To the best of our knowledge, it is the first time that the multi-agent framework and ILC methodology are considered simultaneously in a single train, which can better reveal the coupled characteristic of adjacent cars and impose the repetitive operation pattern of train. The results of numerical simulations show that the tracking performance of the train toward the reference trajectory is significantly improved along with the increase of the number of operations.

Fault diagnosis of high-speed train (HST) bogie is essential in guaranteeing the normal daily operation of HST. In prior works, feature extraction from multi-sensor vibration signals mainly relies on signal processing methods, which is independent of the classification process. Based on convolutional neural networks (CNNs), this paper presents a novel fault diagnosis system using the residual-squeeze net (RSNet), which is directly applicable to raw data (time sequences) and does not require any signal transformation or post-processing. In this network, information fusion is achieved by using the convolutional layer. More specifically, via the squeeze operation, an optimal combination of channels is learnt by training the network. Experimental results obtained by using SIMPACK simulation data demonstrate the effectiveness of the proposed approach in both complete failure case and single failure case, with diagnosis accuracy near 100%. The proposed approach also shows good performance in identifying the locations of faulty components. Comparisons between RSNet and competitive methods shows the advantages of RSNet for fault classification.

Intelligent fault diagnosis of bogie is fundamental for the reliability, stability and security of high-speed train (HST). However, thorny issues, including complicated structure of bogie and complex motion states of HST, aggravate the difficulty of research, where traditional approaches for fault diagnosis might fail. In this paper, CapsNet, a contemporary novel neural network architecture, in which a single unit called capsule is unique for its multi-dimension and abundant spatial information, is adopted to accomplish the recognition and classification of 7 working conditions of HST bogie, comprising both single and compound faults. The experimental accuracy achieves 96.65%, which proves the efficiency and potency of CapsNet in this term. The ability of CapsNet to diagnose faults of bogie in this paper exceeds that of Convolutional Neural Network (CNN), especially for compound ones. Moreover, the method employed is capable of extracting features from raw data automatically and independent of expert experience or knowledge about signal processing.

As an important part of high-speed train (HST), the mechanical performance of bogies imposes a direct impact on the safety and reliability of HST. It is a fact that, regardless of the potential mechanical performance degradation status, most existing fault diagnosis methods focus only on the identification of bogie fault types. However, for application scenarios such as auxiliary maintenance, identifying the performance degradation of bogie is critical in determining a particular maintenance strategy. In this article, by considering the intrinsic link between fault type and performance degradation of bogie, a novel multiple convolutional recurrent neural network (M-CRNN) that consists of two CRNN frameworks is proposed for simultaneous diagnosis of fault type and performance degradation state. Specifically, the CRNN framework 1 is designed to detect the fault types of the bogie. Meanwhile, CRNN framework 2, which is formed by CRNN Framework 1 and an RNN module, is adopted to further extract the features of fault performance degradation. It is worth highlighting that M-CRNN extends the structure of traditional neural networks and makes full use of the temporal correlation of performance degradation and model fault types. The effectiveness of the proposed M-CRNN algorithm is tested via the HST model CRH380A at different running speeds, including 160, 200, and 220 km/h. The overall accuracy of M-CRNN, i.e., the product of the accuracies for identifying the fault types and evaluating the fault performance degradation, is beyond 94.6% in all cases. This clearly demonstrates the potential applicability of the proposed method for multiple fault diagnosis tasks of HST bogie system.

Realizing the accurate fault diagnosis of high-speed train (HST) bogie is of great significance for ensuring the safe operation of HSTs. This article proposes a novel fault diagnosis method to identify the fault states of HST bogie and localize the positions of faulty components simultaneously by virtue of the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and 1-D convolutional neural network (1-D CNN). First, the raw vibration signal is decomposed into multiple intrinsic mode functions (IMFs) via the ICEEMDAN. Then, the high-frequency components of IMFs obtained by ICEEMDAN are selected as the fault features of the HST bogie. Afterward, the 1-D CNN model is adopted to learn the deeper features from the high-frequency components to conduct fault classification and localization. The prediction accuracy of the proposed method is 99.3% and 98.7% on two bogie data sets, respectively. Meanwhile, experimental results demonstrate the effectiveness of the proposed method in identifying both the categories and the locations of faulty components, whose superiority has been verified by the comprehensive comparison analysis with traditional deep learning methods and state-of-art methods, including residual-squeeze net, convolutional recurrent neural network, and DenseNet.