Title :
Short- and long-term online combined learning for robotic control
Author :
Bassi, Danilo F.
Author_Institution :
Dept. de Ingenieria Ind., Chile Univ., Santiago, Chile
Abstract :
Combined short- and long-term online learning for connectionist robotic feedforward control is presented. The online adjustment of the neural network is achieved by comparison of the actual applied torque with a fictitious torque generated by applying the observed acceleration through the feedforward controller. The online procedure has a time-differentiated learning paradigm that is implemented by a dual learning paradigm. Short-term, fast learning, implemented by a simple adjustable matrix, helps in controlling the system at the beginning of the training procedure or in the presence of perturbations. The neural network model provides for the long-term learning, which is convenient for obtaining maximum dynamic performance from a robot, since the effect of undesirable perturbations required for short-term adaptive schemes is absent
Keywords :
learning systems; neural nets; robots; combined short/long term online learning; connectionist robotic feedforward control; dual learning paradigm; maximum dynamic performance; neural network; time-differentiated learning paradigm; training; Acceleration; Adaptive control; Control systems; Feedforward neural networks; Manipulator dynamics; Motion control; Neural networks; Programmable control; Robot control; Torque control;
Conference_Titel :
Neural Networks, 1991. 1991 IEEE International Joint Conference on
Print_ISBN :
0-7803-0227-3
DOI :
10.1109/IJCNN.1991.170686