Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on robotic systems a re discussed in a new report. According to news reporting originating from Tunis,Tunisia, by NewsRx correspondents, research stated, "Rehabilitation devices su ch as actuated exoskeletons can provide mobility assistance for patients sufferi ng from paralysis or muscle weakness." The news reporters obtained a quote from the research from Laboratory of Robotic s: "In order to improve the well-being of patients, the control design of exoske letons is of paramount importance and highest priority. In this paper, we presen t a sliding reinforcement learning (RL) method control for an upper-limb exoskel eton, enabling it to learn following a desired trajectory in the Cartesian space . The deep deterministic policy gradient (DDPG) using an actor-critic architectu re is employed to continuously adjust the non-singular terminal sliding mode con trol (NSTSMC) control inputs, based on previous experiences. The designed actor network learns the policy and the critic evaluates the quality of the actions ch osen by the actor. The robustness of the proposed approach is studied when the s ystem is subjected to random disturbances. The simulation results demonstrate th at the proposed approach based on the RL method effectively fulfills exoskeleton tracking tasks."
NETL
NSTL