Neural-based learning in grasp force control of a robot hand

In this paper, we present a new control system for the intelligent force control of multifingered robot grips which combines both fuzzy-based adaptation level and a neural-based one with a conventional PID-controller. The most attention is given to the neural-based force adaptation level implemented by three-layered back-propagation neural networks. A computer based simulation system for the peg-in-hole insertion task is developed to analyze the capabilities of the neural controllers. Their behaviour is discussed by comparing them to conventional and fuzzy-based force controllers performing the same task.