In this paper, an event-triggered robust fuzzy adaptive prescribed performance finite-time control strategy is presented for a class of strict-feedback nonlinear systems with external disturbances. The relative-threshold-based event-triggered signal is introduced to reduce communication burden, and the dynamic surface control technique is applied to address the computational complexity problem. A disturbance observer is designed to estimate the compounded disturbances, which are composed of external disturbances and fuzzy approximation errors. The proposed control strategy can guarantee that the closed-loop system is semi-globally practically finite-time stable, and the tracking error converges to a small residual set by incorporating the prescribed performance bound in finite-time. Finally, simulation results are provided to verify the effectiveness of the proposed robust fuzzy control strategy.

Gesture recognition for myoelectric prosthesis control utilizing sparse multichannel surface Electromyography (sEMG) is a challenging task, and from a Muscle-Computer Interface (MCI) standpoint, the performance is still far from optimal. However, the design of a well-performed sEMG recognition system depends on the flexibility of the input-output function and the dataset’s quality. To improve the performance of MCI, we proposed a novel gesture recognition framework that (i) Enrich the spectral information of the sparse sEMG signals by constructing a fused map image (denoted as sEMG-Map) that integrates a multiresolution decomposition (by means of orthogonal wavelets) through the raw signals then rely upon the Convolutional Neural Network (CNN) capacity to exploit the composite hierarchies in the constructed sEMG-Map input. (ii) Deals with the label noise by proposing a data-centric method (denoted as ALR-CNN) that synchronously refines the falsely labeled samples and optimizes the CNN model based on two basic assumptions. First, the deep model accuracy improves as the training progress. Second, a set of successive learnable max-activated outputs of a well-performed deep model is a reliable estimator for motion detection in the muscle activation pattern. Our proposed framework is evaluated on three large-scale public databases. The average classification accuracy is 95.50%, 95.85%, and 85.58% for NinaPro DB2, NinaPro DB7, and NinaPro DB3, respectively. The experimental results verify the effectuality of the proposed method and show high accuracy.

Current robotic endoluminal interventions require surgeons to hold a proximal joystick to control the distal flexible robot under 2-D X-ray guidance. However, the 2-D X-ray image is not intuitive, which not only increases the risk of surgical misoperation but also the workload of surgeons. Moreover, contact teleoperation exposes surgeons to the potentially infectious environment. To address it, this article proposes an augmented-reality-assisted touchless teleoperated robot for endoluminal intervention, called ARei. It aims to provide immersive experiences with augmented information. The robot integrates perceptual information obtained from an electromagnetic (EM) sensor, a shape sensor, and the virtual anatomy into a head-mounted display (HMD). The touchless teleoperation is used to control the robot, with the assistance of gesture recognition technology. Results show that the mean error of the calibration between the HMD and the EM tracking system is 4.67 mm, and the mean distance error between the points measured by the EM sensor and the points obtained by shape reconstruction with calibration is 5.19 mm (3.05%).

In this paper, a single leg platform for quadruped robots is designed based on the motivation of high-speed locomotion. The leg is designed for lightweight and low inertia with a structure of three joints by imitating quadruped animals. Because high acceleration and extensive loadings will be involved on the legs during the high-speed locomotion, the trade-off between the leg mass and strength is specifically designed and evaluated with the finite element analysis. Moreover, quadruped animals usually increase stride frequency and decrease contact time as the locomotion speed increases, while maintaining the swing duration during trot gait. Inspired by this phenomenon, the foot-end trajectory for quadruped robots with a high-speed trot gait is proposed. The gait trajectory is planned for swing and stance phase; thus the robot can keep its stability with adjustable trajectories while following a specific gait pattern. Especially for the swing phase, the proposed trajectory can minimize the maximum acceleration of legs and ensure the continuity of position, speed, and acceleration. Then, based on the kinematics analysis, the proposed trajectory is compared with the trajectory of Bézier curve for the power consumption. Finally, a simulation with Webots software is carried out for verifying the motion stability with two trajectory planning schemes respectively. Moreover, a motion capture device is used for evaluating the tracking accuracy of two schemes for obtaining an optimal gait trajectory suitable for high-speed trot gait.

This article focuses on the integral sliding mode control (SMC) issue for nonlinear singularly perturbed systems via T-S fuzzy models. A new sufficient criterion for the sliding surface design is attained by adopting convexification techniques, which also guarantees the asymptotic stability of the sliding motion. Then, a new dynamic SMC synthesis scheme is developed to ensure the finite-time reachability of the sliding surface. A simulation example is shown to illustrate the validity of the developed scheme.

Introducing interactive system into portable robots for hand rehabilitation has always been a crucial topic. Moreover, hand rehabilitation with force cognition can make patients participate actively and improve rehabilitation effect. In this paper, we design a portable robotic device with interactive system for patients to rehabilitate with force cognition. Firstly, an exoskeleton glove is designed with a compact mechanical structure which is controlled by a real-time feedback system. The portable device allows patients to rehabilitate not only in hospital. Next, an interactive system including virtual environment and force cognition is introduced to detect the hand motion and collision. At last, clinical tests of our portable device is carried out with 9 subjects after tendon injury to show the effective assistance with our device.