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Out-of-sample mapping of a two-link robotic manipulator

An approach to reduce dimension for a set of Multiple-Input-Multiple-Output (MIMO) non-linear system equations is tested in simulation. The methodology is implemented on a vertical two-link robotic manipulator. A manifold learning (ML) algorithm is used to reduce the dimension of the system. Finally, an artificial neural network (ANN) routine is trained to act as an out-of-sample (OoS) extension algorithm, and is used to map existing and newly generated points. The initial results of the study show promise in the application of ML routines for system identification (SI) on a reduced data set and eventually application to control setups.