I O Stability for Robot Control with a Global Neural Net Inverse Model in the Feedback Loop
نویسنده
چکیده
A set of Neural Networks learns matrix components representing mass coupling coriolis and friction forces in an inverse robot model While obtaining training data from speci c random trajectories the inverse model becomes highly precise over the entire robot working range Global model precision and subsequently L stability for a controller using the networks in the feedback loop following the learning phase are established by a new method The approach is demonstrated for a simulated planar joint machine
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