Pruning of RBF Networks in Robot Manipulator Learning Control
Radial Basis Function Neural Networks are well suited for learning the system
dynamics of a robot manipulator and implementation of these networks in the
control scheme for a manipulator is a good way to deal with the system uncertainties
and modeling errors which often occur. The problem with RBF networks
however is to nd a network with suitable size, not too computational demanding
and able to give accurate approximations. In general two methods for creating an
appropriate RBF network has been developed, 1) Growing and 2) Pruning.
In this report two dierent pruning methods which are suitable for use in a
learning controller for robot manipulators are proposed, Weight Magnitude Prun-
ing and Neuron Output Pruning. Weight Magnitude Pruning is based on a pruning
scheme in  while Neuron Output Pruning is based on . Both pruning methods
are simple, have low computational cost and are able to remove several units
in one pruning period. The thresholds used to nd which neurons to remove are
specied as a percent and hence less problem dependent to nd.
Simulations with the two proposed pruning methods in a learning inverse kinematic
controller for tracking a trajectory by using the three rst joints of the ABB
IRB140 manipulator are conducted. The result was that implementing pruned
RBF networks in the controller made it more robust towards system uncertainties
due to increased generalization ability. These pruned networks were found to
give better tracking in the case of unmodeled dynamics compared to the incorrect
system model, not pruning the RBFNNs and a type of growing network called
RANEKFs. Computational costs were also reduced when the pruning schemes
NTNU has a manipulator of the type ABB IRB140 and the learning inverse
kinematic controller with pruning of RBF networks should be implemented and
tested on this in real-life simulations.
Place, publisher, year, edition, pages
Institutt for teknisk kybernetikk , 2012. , 152 p.
ntnudaim:6788, MTTK teknisk kybernetikk
IdentifiersURN: urn:nbn:no:ntnu:diva-18591Local ID: ntnudaim:6788OAI: oai:DiVA.org:ntnu-18591DiVA: diva2:566105
Gravdahl, Jan Tommy, ProfessorGale, Serge