Force control of robotic manipulator using neural network

Abstract

Artificial neural network (NN) can be applied to complex dynamical control system. The multilayer neural network with sigmoid function is often used in this field. But this type of NN cannot learn the patterns additionally. It must learn both unlearned patterns and patterns given before. The neural network based on the distance between patterns (NDP) can memorize patterns additionally and recognize unlearned patterns. Adaptive force control using NDP is proposed in this paper. Hierarchical neuromorphic controller is used, in which the higher level NDP detects changes in environments and activates a corresponding lower level controller. Multilayer neural networks are used at the lower level for the control of unknown plant. Hierarchical structure can enlarge the range of the adaptation autonomously, and learn additionally.