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|Title:||A proposed neural internal model control for robot manipulators|
|Keywords:||Alopex learning algorithm;Back propagation;Diagonal recurrent network;Feed forward neural network;Internal model control;Recurrent hybrid network|
|IPC Code:||B25J9/00; G05B13/04|
|Abstract:||This paper describes the design of a Neural Internal Model Control (NIMC) system for robots, based on Recurrent Hybrid Networks (RHNs). The NIMC, an 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.|
|ISSN:||0975-1084 (Online); 0022-4456 (Print)|
|Appears in Collections:||JSIR Vol.65(09) [September 2006]|
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