ADALINE Stabilization of an Inverted Robot Arm
Peter C. Jones and Scott Tepavich
Advisors: Dr. Thomas Stewart and Dr. Gary Dempsey
A useful control tool is one that can adapt to variances in a complex
system. One system that would benefit from such adaptive control is an
inverted robot arm, the exact nature of which is not known or can be changed
(length, mass, mass distribution, disturbances). This project is the implementation
of an ADALINE (ADAptive LInear NEuron) neural network learning control
system to stabilize the robot arm to a vertical position. The control network
is trained with the adjustment signals used to manually stabilize the robot
arm and the resulting robot arm angles. Once trained, the network will
stabilize the robot arm without manual input.
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