This paper proposes an adaptive hybrid high-order terminal sliding mode (HHOTSM) control approach for a class of high-order multiple-input and multiple-output (MIMO) uncertain nonlinear systems and its application to robotic manipulators. The techniques of the terminal sliding mode (TSM) control and a type of traditional sliding mode control (SMC) are combined to establish the HHOTSM controller, in which the first-order sliding mode term is designed based on the TSM, while the high-order sliding mode term is defined based on the idea of the high-order sliding mode approach. Thus, the proposed method offers a valuable elimination of the singularity problem encountered in traditional TSM control, as well as the reaching phase problem. It is guaranteed that the tracking errors will converge to zero in some finite time that can be set arbitrarily, and the actual control input signal is smooth and free of chattering effects. Furthermore, an adaptive tuning law is incorporated to reject the effects of unknown system uncertainties. The convergence and stability of the proposed method are verified by the Lyapunov stability theory. Simulation results of a robot manipulator are presented to demonstrate the effectiveness and applicability of the proposed method.

This paper presents a novel method for fusing information from multiple sensor systems for bearing fault diagnosis. In the proposed method, a convolutional neural network is exploited to handle multiple signal sources simultaneously. The most important finding of this paper is that a deep neural network with wide structure can extract automatically and efficiently discriminant features from multiple sensor signals simultaneously. The feature fusion process is integrated into the deep neural network as a layer of that network. Compared to single sensor cases and other fusion techniques, the proposed method achieves superior performance in experiments with actual bearing data.

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Feature extraction from a signal is the most important step in signal-based fault diagnosis. Deep learning or deep neural network (DNN) is an effective method to extract features from signals. In this paper, a novel vibration signal-based bearing fault diagnosis method using DNN is proposed. First, the measured vibration signals are transformed into a new data form called multiple-domain image-representation. By this transformation, the task of signal-based fault diagnosis is transferred into the task of image classification. After that, a DNN with a multi-branch structure is proposed to handle the multiple-domain image representation data. The multi-branch structure of the proposed DNN helps to extract features in multiple domains simultaneously, and to lead to better feature extraction. Better feature extraction leads to a better performance of fault diagnosis. The effectiveness of the proposed method was verified via the experiments conducted with actual bearing fault signals and its comparisons with well-established published methods.