In this paper, a novel high-order disturbance observer (HODO) for the Mobile Wheeled Inverted Pendulum (MWIP) system is firstly proposed. Based on a choice method of optimal gain matrices, the estimation accuracy of the HODO can be improved. Combining the proposed HODO and sliding mode control (SMC), a new control strategy is designed for the balance and speed control of the MWIP system. The boundness of the estimation error of HODO is proved and the stability of the closed-loop control system is achieved through the appropriate selection of sliding surface coefficients. The effectiveness of all proposed methods is verified by experiments on a real MWIP system.

This study aims to present a robust environmental features recognition system (EFRS) for lower limb prosthesis, which can assist the control of prosthesis by predicting locomotion modes of amputees and estimating environmental features in the following steps. A depth sensor and an inertial measurement unit (IMU) are combined to stabilize the point cloud of environments. Subsequently, the 2D point cloud is extracted from origin 3D point cloud and is classified through a neural network. Environmental features, including slope of road, width, and height of stair, were also estimated via the 2D point cloud. Finally, EFRS is evaluated through classifying and recognizing five kinds of common environments in simulation, indoor experiments and outdoor experiments by six healthy subjects and three transfemoral amputees, and databases of five healthy subjects and three amputees are used to validate without training. The classification accuracy of five kinds of common environments reach up to 99.3% and 98.5% for the amputees in the indoor and outdoor experiments, respectively. The locomotion modes are predicted at least 0.6 s before the switch of actual locomotion modes. Most estimation errors of indoor and outdoor environments features are lower than 5% and 10%, respectively. The overall process of EFRS takes less than 0.023 s. The promising results demonstrate the robustness and the potential application of the presented EFRS to help control of lower limb prostheses.

Soft robots exhibit many exciting properties due to their softness and body compliance. However, to interact with the environment safely and to perform a task effectively, a soft robot faces a series of challenges such as dexterous motion, proprioceptive sensing, and robust control of its deformable bodies. To address these issues, this article presents a method for fabrication and dynamic modeling of a novel bidirectional bending soft pneumatic actuator that embeds a curvature proprioceptive sensor. The bidirectional bending deformation was generated by two similar chambers with a sinusoidal shape for reducing the internal dampness during bending deformation. An optical waveguide made from flexible poly (methyl methacrylate) material that is immune to the inlet pressure was embedded into the actuator body to measure its bending angle. A dynamic modeling framework based on step response and parameter fitting was proposed to establish a simple differential equation that can describe the nonlinear behavior of the soft actuator. Hence, a sliding mode controller is designed based on this differential equation and the Taylor expansion. The proposed dynamical model and the sliding mode controller were validated by trajectory tracking experiments. The performance of the bidirectional bending soft actuator, such as the linear output of the curvature sensor in different inflating patterns, the proprioceptive sensitiveness to the external environment, the output force, and large bending range under relatively small pressure, was evaluated by relevant experimental paradigms. Prototypes from the novel design and fabrication process demonstrated the soft actuator's potential applications in industrial grasping and hand rehabilitation.

Wearable robots have become a prevalent method in the field of human augmentation and medical rehabilitation. Typical wearable robots mainly include exoskeletons and prostheses. However, their functions are limited due to dedicated design. In recent years, Supernumerary Robotic Limbs (SRLs) have become a hot spot in the field of wearable robots. Different from exoskeletons and prostheses, SRLs compensate and strengthen human abilities by providing extra limbs. This advantage allows SRLs to assist users in a novel way, rather than substituting missing limbs or enhancing existing limbs. However, finding a trade-off between wearability, efficiency, and usability of those SRLs is still an issue. This paper presents the state of the art in SRLs and discusses some open questions about SRLs' design and control for further research. This review covers the following areas: (1) Basic concepts and classifications of SRLs; (2) The literature retrieval methodology; (3) Design functions of different types of SRLs, including their positive and negative aspects; (4) Different control strategies of SRLs, including positive and negative aspects, and some improvement methods in applying SRLs; (5) The impact on human body schema while using SRLs; (6) Open challenges and suggestions for future development. This review will help researchers understand the current state of SRLs and provide comprehensive knowledge foundations for them.

In this paper, we present a simultaneous and continuous kinematics estimation method for multiple DoFs across shoulder and elbow joint. Although simultaneous and continuous kinematics estimation from surface electromyography (EMG) is a feasible way to achieve natural and intuitive human-machine interaction, few works investigated multi-DoF estimation across the significant joints of upper limb, shoulder and elbow joints. This paper evaluates the feasibility to estimate 4-DoF kinematics at shoulder and elbow during coordinated arm movements. Considering the potential application of this method in exoskeleton, prosthetics and other upper limb rehabilitation techniques, the estimation performance is presented with different muscle activity decomposition and learning strategies. Principle component analysis (PCA) and independent component analysis (ICA) are respectively employed for EMG mode decomposition with artificial neural network (ANN) for electromechanical association learning. Four joint angles across shoulder and elbow are estimated from EMG simultaneously and continuously. With 70-s inter-cross validation and 2-min intra-cross validation, the feasibility, effectiveness and robustness of the proposed myoelectric decoding are presented.

Pneumatic muscle actuators (PMAs), a kind of soft/compliant actuators, have been attracted a great deal of attention in the studies of rehabilitation robots. However, the nonlinearities, uncertainties, hysteresis, and time-varying features of PMAs bring a lot of difficulties in their high-precision trajectory tracking tasks. In this paper, an echo state Gaussian process-based nonlinear model predictive control (ESGP-NMPC) is designed for the PMAs. The proposed strategy is comprised of an ESGP, which is suitable for modeling unknown nonlinear systems as well as measuring their uncertainties, and a gradient descent optimization algorithm for calculating the control signal sequences. Based on the Lyapunov theorem, characteristics of the closed-loop system are analyzed to guarantee the asymptotical stability. Both simulations and physical experiments are carried out to illustrate the validity of the proposed control strategy. Compared with other conventional methods, the ESGP-NMPC can achieve a better model fitting for the PMA and control performance for the high-precision tracking tasks.