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.

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.