JOURNAL OF SCIENTIFIC & INDUSTRIAL RESEARCH, vol.65, no.9, pp.713-720, 2006 (SCI-Expanded)
This paper describes the design of a Neural Internal Model Control (NIMC) system for robots, based oil Recurrent Hybrid Networks (RHNs). The NIMC, all alternative to the basic inverse control scheme, consists of a forward internal neural model of robot, a neural controller and a conventional feedback controller. An Alopex Learning Algorithm (ALA) was used to adjust weights of the proposed neural network. Backpropagation (BP) algorithm is also employed for comparison. Diagonal Recurrent Networks (DRNs) and Feedforward Neural Networks (FNNs) controllers were used for comparison. The robot in this study was adept one SCARA type robot manipulator.