Hardware implementation of a neural network controller for a manipulator arm
Rosalyn S. Hobson, Rafael M. Inigo · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1995
In a previous paper, a neural network with a reward/punish learning scheme controller for a manipulator arm was described. The inputs to the torque-generating neuron are the position error and the velocity of the joints. The output of the neuron is the torque required to control the arm to its desired position. The reward/punish learning mechanism is implemented to adaptively modify the weights. The neural network controller does not need a dynamic model of the arm. The dynamics are learned through training. In this paper we describe the hardware/software implementation of the neural network to control the shoulder joint of a Mitsubishi RM501 arm. Once the system was checked for correct operation the following tests were performed: (1) training the arm to hold is position at different angles (10, 40, 70, 100 and 120 degrees). The angle was to hold with very small error, even in the presence of significant disturbances, after a training period that varied from 3 to 12 seconds. (2) Training the arm at 50 degrees and then commanding it to follow a cosine trajectory from 50 to 70 degrees. The maximum error in this test was less than 1% of the desired value.