This paper presents a comparative analysis of model-based control strategies for a Bionic Ankle Tensegrity Exoskeleton (BATE), designed to emulate the self-stress equilibrium and self-supporting characteristics of the human ankle biotensegrity structure. Model-based control strategies are conventional methods that can discover the principles of the BATE exoskeleton. The high dimensions and non-linearity of the BATE pose challenges for theoretical modeling and model-based control strategies. To address this, we propose a modeling method based on the force density that accounts for interaction forces. We evaluated the trajectory tracking performance and robustness of BATE under three power-assisted control methods: position control (PC), force control (FC) and hybrid force-position control (FPC). Experimental results demonstrate that the PC method offers superior performance in both trajectory tracking and robustness, making it suitable for early rehabilitation training to enhance flexibility. Our findings highlight the advantages of tensegrity exoskeletons over current wearable exoskeletons and introduce novel concepts for developing high-performance exoskeletons.

This paper proposes a novel Tensegrity-Based Fracture Reduction Robot (TFRR) designed to enhance the safety and efficacy of orthopedic procedures through integrated force-sensing and control capabilities. Inspired by the biomechanics of skeletal muscles, the robot adopts a tensegrity architecture that enables real-time monitoring of internal force distribution and dynamic adjustment of posture and inter-bone contact forces via controlled tensioning of its string network. To establish a theoretical foundation for system control, a comprehensive static analysis of the tensegrity structure is conducted, allowing accurate modulation of topological configurations through systematic tension control. Extensive experimental validation demonstrates the robustness and reliability of the proposed method across a range of operating conditions. In particular, targeted experiments on contact-force regulation confirm the robot’s ability to precisely monitor and adjust inter-bone forces during fracture reduction. These features collectively enable safer, more controlled surgical interventions, with the potential to reduce tissue trauma and improve clinical outcomes.

Modeling and controlling the musculoskeletal system are crucial for understanding human motor functions, optimizing human-robot interaction, and developing embodied intelligence. However, existing musculoskeletal models are mainly limited to specific body parts and muscle groups, and still face challenges in large-scale muscle coordination and the generation of diverse movements. In this study, we propose a musculoskeletal deep reinforcement learning (DRL) control model. This model integrates a Long Short-Term Memory (LSTM) network and a Multi-Head Self-Attention (MHSA) mechanism into the Proximal Policy Optimization (PPO) algorithm. The LSTM-MHSA-enhanced PPO control approach generates accurate muscle activation, motion trajectories, and torque control strategies to precisely control and replicate diverse human gaits based on target joint movements. Experimental results demonstrate that this LSTM-MHSA-enhanced PPO algorithm significantly improves the model accuracy compared to the traditional PPO algorithm, with a 43.75% and 34.14% reduction in Mean Absolute Error (MAE) for walking and running tasks, respectively. Furthermore, for complex tasks such as striking and dancing, the MAE decreases by 46.97% and 41.78%, respectively. These findings highlight that integrating LSTM and MHSA into PPO algorithm not only enhances gait simulation accuracy but also improves the model’s generalization capability, particularly for complex motion patterns. This research provides an efficient tool for motion simulation and gait analysis, advancing the development of human musculoskeletal control systems.

Twisted String Actuators (TSAs) exhibit several advantages, including lightweight, compact, and having a high power-to-weight ratio. However, current research on kinematic models of TSAs is limited to deriving the relationship between motor input and output through idealized geometric calculations. Therefore, the accuracy of these models does not meet the requirements for practical applications. Previous studies on the kinematic modeling of TSA have not considered the impact of material plastic deformation and stroke times on TSA kinematics. Accumulation of plastic deformation over multiple strokes leads to changes in the output displacement of TSA, significantly affecting the accuracy of the kinematic model. This study aims to address the limitations of previous research by investigating the use of Invertible Neural Networks (INNs) in kinematic modeling of TSAs, taking into account material plastic deformation and stroke times. Through a series of TSA experiments, a kinematic model of TSA was established using an INN that considers stroke times. The INN model proves to be superior in both forward and inverse kinematic modeling by effectively compensating for the effects of plastic deformation during TSA operation. The experimental results demonstrate that the kinematic model established by the proposed INN is more aligned with the actual conditions when compared to traditional kinematic models. This insight can aid in predicting the lifespan of TSA in the future.

The differential mechanism can reduce the number of actuators and efficiently distribute force or power. We proposed a novel multiport output twisted string actuator (MO-TSA) with self-differential mechanism that employs a single actuator to achieve multiport outputs. The differential MO-TSA is adaptively controlled in accordance with the force differences at each output port, thus replacing the traditional differential gears and whiffletree mechanisms. Inspired by the hand muscles, we designed one hand glove using the MO-TSA, aiming to enhance the range of achievable grasp configurations. The hand glove is capable of performing various grasps with a single actuator, resulting in a lighter and simpler hand design and revolutionizing the field of twisted string actuators (TSAs) by offering a streamlined solution for achieving versatile actuation.