This paper introduces a novel zero-force control method for upper-limb exoskeleton robots, which are used in a variety of applications including rehabilitation, assistance, and human physical capability enhancement. The proposed control method employs an Adaptive Integral Terminal Sliding Mode (AITSM) controller, combined with an exponential reaching law and Proximal Policy Optimization (PPO), a type of Deep Reinforcement Learning (DRL). The PPO system incorporates an attention mechanism and Long Short-Term Memory (LSTM) neural networks, enabling the controller to selectively focus on relevant system states, adapt to changing behavior, and capture long-term dependencies. This controller is designed to manage a 5-DOF upper-limb exoskeleton robot with zero force, even amidst system uncertainties. The controller uses an integral terminal sliding surface to ensure finite-time convergence to the desired state, a crucial feature for applications requiring quick responses. It also includes an exponential switching control term to reduce chattering and improve system accuracy. The controller's adaptability, facilitated by the PPO system, allows real-time parameter adjustments based on system feedback, making the controller robust and capable of dealing with uncertainties and disturbances that could affect the performance of the exoskeleton. The proposed control method's effectiveness and superiority are confirmed through numerical simulations and comparisons with existing control methods.

翻译：本文提出了一种用于上肢外骨骼机器人的新型零力控制方法，该机器人广泛应用于康复、辅助及人体机能增强等领域。所提出的控制方法采用自适应积分终端滑模控制器，结合指数趋近律以及近端策略优化——一种深度强化学习算法。该PPO系统融合了注意力机制和长短期记忆神经网络，使控制器能够选择性地关注相关系统状态、适应行为变化并捕捉长期依赖关系。该控制器旨在管理一个5自由度上肢外骨骼机器人实现零力控制，即使在系统存在不确定性的情况下也能稳定运行。控制器采用积分终端滑模面来确保系统在有限时间内收敛至期望状态，这对需要快速响应的应用至关重要。同时引入指数切换控制项以抑制抖振并提高系统精度。通过PPO系统实现的控制器自适应能力，可根据系统反馈实时调整参数，使其具备鲁棒性，能够有效处理可能影响外骨骼性能的不确定性和干扰。通过数值仿真及与现有控制方法的对比，验证了所提控制方法的有效性和优越性。