Trajectory Tracking Control Using Echo State Networks for the CoroBot's Arm

Different neural network models have proven being useful for tracking purposes in robotic devices. However, some models have shown superior performances to others that generate a large computational cost. This is the case of recurrent neural networks, which due to the temporal relationship existing allows satisfactory answers. Furthermore, training used by traditional algorithms, require a relatively high convergence time for some applications, especially those that are on-line. Given this problematic, this paper suggests use Echo State Networks (ESN) to perform such tasks. Additionally, results are presented for two sets of predefined tests, which were used to validate control behavior of trajectories in a manipulator embedded in a mobile platform. The results presented are related to the planar control of the manipulator in a closed loop.

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