Design, optimisation and testing of a compact, inexpensive elastic element for series elastic actuators

Design and Characterization of a Low-Cost and Efficient Torsional Spring for ES-RSEA

The design of torsional springs for series elastic actuators (SEAs) is challenging, especially when balancing good stiffness characteristics and efficient torque robustness. This study focuses on the design of a lightweight, low-cost, and compact torsional spring for use in the energy storage-rotary series elastic actuator (ES-RSEA) of a lumbar support exoskeleton. The exoskeleton is used as an assistive device to prevent lower back injuries. The torsion spring was designed following design for manufacturability (DFM) principles, focusing on minimal space and weight. The design process involved determining the potential topology and optimizing the selected topology parameters through the finite element method (FEM) to reduce equivalent stress. The prototype was made using a waterjet cutting process with a low-cost material (AISI-4140-alloy) and tested using a custom-made test rig. The results showed that the torsion spring had a linear torque-displacement relationship with 99% linearity, and the deviation between FEM simulation and experimental measurements was less than 2%. The torsion spring has a maximum torque capacity of 45.7 Nm and a 440 Nm/rad stiffness. The proposed torsion spring is a promising option for lumbar support exoskeletons and similar applications requiring low stiffness, low weight-to-torque ratio, and cost-effectiveness.

A Fuzzy Logic Reinforcement Learning Control with Spring-Damper Device for Space Robot Capturing Satellite

In order to prevent joints from being damaged by impact force in a space robot capturing satellite, a spring-damper device (SDD) is added between the joint motor and manipulator. The device can not only absorb and attrition impact energy, but also limit impact force to a safe range through reasonable design compliance control strategy. Firstly, the dynamic mode of the space robot and target satellite systems before capture are established by using a Lagrange function based on dissipation theory and Newton-Euler function, respectively. After that, the impact effect is analyzed and the hybrid system dynamic equation is obtained by combining Newton’s third law, momentum conservation, and a kinematic geometric relationship. To realize the buffer compliance stability control of the hybrid system, a reinforcement learning (RL) control strategy based on a fuzzy wavelet network is proposed. The controller consists of a performance measurement unit (PMU), an associative search network (ASN), and an adaptive critic network (ACN). Finally, the stability of system is proved by Lyapunov theorem, and both the impact resistance of SDD and the effectiveness of buffer compliance control strategy are verified by numerical simulation.

Design and Control of an Adaptive Knee Joint Exoskeleton Mechanism with Buffering Function

A knee exoskeleton with an adaptive instantaneous rotation center and impact absorption is used for rehabilitation. Due to the human knee joint's special physiological structure and motion characteristics, the exoskeleton mechanism needs to be designed for both static and dynamic aspects. Therefore, a novel knee exoskeleton mechanism was designed. To adapt to the rotation center of the knee joint, a mechanism with cross-configuration was designed according to the equivalent degree of freedom and the stiffness of the springs was calculated by its combination with gait motion, so that the average force of the human body was minimized. A dynamic model of the exoskeleton was established. To overcome the uncertainty in the parameters of the human and robotic limbs, an adaptive controller was designed and a Lyapunov stability analysis was conducted to verify the system. A simulation was conducted and experimental results show that the tracking error of the knee joint angle between the actual and desired trajectory was within the range of -1 to 1 degree and indicate the effectiveness of the controller.

A Human Joint Torque Estimation Method for Elbow Exoskeleton Control

Exoskeleton for motion assistance has obtained more and more attention due to its advantages in rehabilitation and assistance for daily life. This research designed an estimation method of human joint torque by the kinetic human–machine interaction between the operator’s elbow joint torque and the output of exoskeleton. The human elbow joint torque estimation was obtained by back propagation (BP) neural network with physiological and physical input elements including shoulder posture, elbow joint-related muscles activation, elbow joint position, and angular velocity. An elbow-powered exoskeleton was developed to verify the validity of the human elbow joint torque estimation. The average correlation coefficients of estimated and measured three shoulder joint angles are 97.9%, 96.2%, and 98.1%, which show that estimated joint angles are consistent with the measured joint angle. The average root-mean-square error between estimated elbow joint torque and measured values is about 0.143[Formula: see text]N[Formula: see text]m. The experiment results proved that the proposed strategy had good performance in human joint torque estimation.