Muscle synergy reflects inherent coordination patterns of muscle groups as the human body finishes required movements. It may be still unknown whether the original muscle synergy of subjects may alter or not when exoskeletons are put on during their normal walking activities. This paper reports experimental results and presents analysis on muscle synergy from 17 able-bodied subjects with and without lower-limb exoskeletons when they performed normal walking tasks. The electromyography (EMG) signals of the tibialis anterior (TA), soleus (SOL), lateral gastrocnemius (GAS), vastus medialis oblique (VMO), vastus lateralis oblique (VLO), biceps femoris (BICE), semitendinosus (SEMI), and rectus femoris (RECT) muscles were extracted to obtain the muscle synergy. The quantitative results show that, when the subjects wore exoskeletons to walk normally, their mean muscle synergy changed from when they walked without exoskeletons. When the subjects walked with and without exoskeletons, statistically significant differences on sub-patterns of the muscles' synergies between the corresponding two groups could be found.

Fault tolerance is a critical requirement for robust motion control of redundant robotic manipulators. This article aims to endow the redundant manipulator with the capability to achieve the required path of end-effector in the condition that one or some of its joints’ motion fail. Although many fault-tolerant control algorithms of redundant manipulator have been proposed in recent years. However, few of them are on the basis of the condition that the robotic model is unknown. The complexity of the calculation model limits the efficiency and portability of these algorithms. For the first time, we proposed a discrete model-free fault-tolerant tracking control scheme of redundant manipulator, which takes into account the fault tolerance in the control system of redundant manipulator by formulating it into a quadratic programming (QP) framework. The core of the proposed scheme consists of a discrete kinematic estimator and a discrete QP solver, powered by which the fault-tolerant control problem is transformed into a unified computing problem relaxing the need of knowing the redundant manipulator’s kinematic model. A discrete joint space observer is proposed for the detection of the happening of faulty states. Extensive simulations and experiments based on a redundant manipulator are performed and analyzed to support the verification of the efficiency and effectiveness of the proposed scheme.

Manipulators actuate joints to let end effectors to perform precise path tracking tasks. Recurrent neural network which is described by dynamic models with parallel processing capability, is a powerful tool for kinematic control of manipulators. Due to physical limitations and actuation saturation of manipulator joints, the involvement of joint constraints for kinematic control of manipulators is essential and critical. However, current existing manipulator control methods based on recurrent neural networks mainly handle with limited levels of joint angular constraints, and to the best of our knowledge, methods for kinematic control of manipulators with higher order joint constraints based on recurrent neural networks are not yet reported. In this study, for the first time, a novel recursive recurrent network model is proposed to solve the kinematic control issue for manipulators with different levels of physical constraints, and the proposed recursive recurrent neural network can be formulated as a new manifold system to ensure control solution within all of the joint constraints in different orders. The theoretical analysis shows the stability and the purposed recursive recurrent neural network and its convergence to solution. Simulation results further demonstrate the effectiveness of the proposed method in end‐effector path tracking control under different levels of joint constraints based on the Kuka manipulator system. Comparisons with other methods such as the pseudoinverse‐based method and conventional recurrent neural network method substantiate the superiority of the proposed method.

The saturated PI (proportional-integral) based method is widely applied in nonlinear system control fields. It can be regarded as a black-box type approach with the saturated control input and utilizes the tracking error of the system output with its integral information. However, as precise description of plants’ system details is usually difficult, saturated PI control approaches have to empirically tune the proportional and integral parameters to guarantee reliable convergence, making its general convergence mechanism not interpreted. In this brief, for the first time, the convergence of the saturated PI control scheme is proved through an optimization solver based on a primal dual neural network. Illustrate examples including control of an inverted-pendulum mobile vehicle and a manipulator demonstrate the efficiency of the proposed saturated PI control method in such an optimization perspective.

Recently, neural network model-based control has received wide interests in kinematics control of manipulators. To enhance learning ability of neural network models, the autoencoder method is used as a powerful tool to achieve deep learning and has gained success in recent years. However, the performance of existing autoencoder approaches for manipulator control may be still largely dependent on the quality of data, and for extreme cases with noisy data it may even fail. How to incorporate the model knowledge into the autoencoder controller design with an aim to increase the robustness and reliability remains a challenging problem. In this work, a sparse autoencoder controller for kinematic control of manipulators with weights obtained directly from the robot model rather than training data is proposed for the first time. By encoding and decoding the control target though a new dynamic recurrent neural network architecture, the control input can be solved through a new sparse optimization formulation. In this work, input saturation, which holds for almost all practical systems but usually is ignored for analysis simplicity, is also considered in the controller construction. Theoretical analysis and extensive simulations demonstrate that the proposed sparse autoencoder controller with input saturation can make the end-effector of the manipulator system track the desired path efficiently. Further performance comparison and evaluation against the additive noise and parameter uncertainty substantiate robustness of the proposed sparse autoencoder manipulator controller.