Radical basis function (RBF) neural network is proposed for the upper limb rehabilitation robot driven by series elastic actuator (SEA). The human-in-charge (HIC) mode and the robot-in-charge (RIC) mode are performed via the devised interaction control algorithm, where in the different control modes can be switched smoothly to avoid human-robot confrontation and improve the rehabilitation effect. On the basis of the RBF neural network with nonlinear mapping property, the human-robot interaction control modes can be dynamically adjusted via utilizing human-robot interaction force, which realizes the human-robot collaboration and adaptive control. The simulation results prove that the advised control method has a excellent performance different control modes.
Human-Robot Interaction Control Based on the RBF Neural Network for Upper Limb Rehabilitation Robot
2023-11-10
12894786 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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