Compliant actuators can offer many attractive features over stiff actuators in real human-robot interaction applications, such as low output impedance, smooth force transmission, and shock tolerance. This brief focuses on a robust sliding mode control (SMC) methodology for robotic systems with compliant actuators. First, a continuous SMC design is introduced due to its advantages of strong robustness and chattering attenuation. However, this continuous SMC structure cannot guarantee a high tracking performance in the presence of mismatched disturbances in compliantly actuated robots. Meanwhile, in many application fields, compliantly actuated robots are affected by different kinds of time-varying disturbances, including external environmental disturbances, internal parameter uncertainties, and frictions, which may be in the form of constant, ramp, and parabolic disturbances. To estimate such unknown disturbances, a generalized proportional integral observer (GPIO) technique is employed. By designing a new sliding surface with the help of disturbance estimation, a GPIO-based continuous SMC method is synthesized, which is used to deal with matched/mismatched time-varying disturbances. A detailed stability analysis of the closed-loop system is also presented. Experimental results under three different test conditions are provided to illustrate the promising tracking performance of the proposed control strategy.

Motor Imagery (MI) based on Electroencephalography (EEG), a typical Brain-Computer Interface (BCI) paradigm, can communicate with external devices according to the brain’s intentions. Convolutional Neural Networks (CNN) are gradually used for EEG classification tasks and have achieved satisfactory performance. However, most CNN-based methods employ a single convolution mode and a convolution kernel size, which cannot extract multi-scale advanced temporal and spatial features efficiently. What’s more, they hinder the further improvement of the classification accuracy of MI-EEG signals. This paper proposes a novel Multi-Scale Hybrid Convolutional Neural Network (MSHCNN) for MI-EEG signal decoding to improve classification performance. The two-dimensional convolution is used to extract temporal and spatial features of EEG signals and the one-dimensional convolution is used to extract advanced temporal features of EEG signals. In addition, a channel coding method is proposed to improve the expression capacity of the spatiotemporal characteristics of EEG signals. We evaluate the performance of the proposed method on the dataset collected in the laboratory and BCI competition IV 2b, 2a, and the average accuracy is at 96.87%, 85.25%, and 84.86%, respectively. Compared with other advanced methods, our proposed method achieves higher classification accuracy. Then we use the proposed method for an online experiment and design an intelligent artificial limb control system. The proposed method effectively extracts EEG signals’ advanced temporal and spatial features. Additionally, we design an online recognition system, which contributes to the further development of the BCI system.

A robust finite-time control (FTC) framework using continuous terminal sliding-mode control (SMC) and high-order sliding-mode observer (HOSMO) is discussed to realize the trajectory tracking of flexible-joint robots in this article. Control performances of the robots always suffer from unknown matched and mismatched time-varying disturbances. Traditional SMC exists with a chattering phenomenon and cannot cope with mismatched time-varying disturbances due to its inherent structure property. For this reason, two HOSMOs are devised to estimate the time-varying disturbances on the link and motor side, respectively. Then, by fusing the states and disturbance estimations into a novel terminal sliding-mode surface, a continuous robust FTC scheme is developed. The proposed control strategy can not only handle both matched and mismatched time-varying disturbances but also obtain a finite-time convergence performance. The rigorous finite-time stability analysis of the closed-loop system under the proposed control method is guaranteed. The results are illustrated to verify the effectiveness and robustness of the proposed design approach.