  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.
