Abstract
This paper describes a neural network based controller for tracking moving objects with a two-joint robot arm. The neural network consists of a single layer neural map containing pairs of cells, and two output neurons. The inputs to the network are the position of the hand, and the position and velocity of the object relative to the hand. The outputs from the network are the torques to be applied to the two joints.
The network learns the mapping between joint torques and hand movements by making random movements of the arm. During learning, the delta rule is used to adjust the weights of the connections between the neural map and the output neurons.
The model has been tested by computer simulation of a system consisting of the control network, a two joint arm with inertia and damping, and a randomly moving object. In the simulation, the neural network controller tracks objects with position and velocity errors of the order of 0.5 percent of the range of movement of the arm. This technique can be applied to a wide range of tracking control problems.
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© 1992 Springer-Verlag London Limited
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Shumsheruddin, D. (1992). Neural Network Control of Robot Arm Tracking Movements. In: Taylor, J.G. (eds) Neural Network Applications. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-2003-2_10
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DOI: https://doi.org/10.1007/978-1-4471-2003-2_10
Publisher Name: Springer, London
Print ISBN: 978-3-540-19772-0
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