Adaptive Terminal Sliding Mode Control Using Deep Reinforcement Learning for Zero-Force Control of Exoskeleton Robot Systems
<|reference_start|>Adaptive Terminal Sliding Mode Control Using Deep Reinforcement Learning for Zero-Force Control of Exoskeleton Robot Systems: 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.<|reference_end|>
@article{mirzaee2024adaptive,
    title={Adaptive Terminal Sliding Mode Control Using Deep Reinforcement Learning
  for Zero-Force Control of Exoskeleton Robot Systems},
    author={Morteza Mirzaee, Reza Kazemi},
    journal={arXiv preprint arXiv:2407.18309},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.18309},
    primaryClass={cs.RO cs.SY eess.SY}
}