Abstract
This paper proposes a nonlinear disturbance observer with recurrent neural network compensator applicable to nonlinear systems that is velocity controlled. In the proposed method, the recurrent neural network is trained to generate nonlinear velocity defined as the difference between the measured velocity of joints and a nominal linear velocity calculated via a linear model from the reference input to the velocity control system. The training results is evaluated based on the leave-one-out method, and it is shown that the RNN can be well-trained to estimate the nonlinear velocity. Then, a common DOB can be estimate disturbance from the nominal linear velocity restored by the trained RNN output and the velocity measurement. The validity of the proposed method is evaluated by simulation for a vertically-articulated two-link manipulator, comparing a conventional disturbance observer. The simulation result showed that the proposed disturbance observer can be comparable to a conventional DOB that works in an ideal condition where all the parameters of the manipulator are known, while the data needed to construct the proposed DOB is only the velocity measurement and its reference.
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Yamada, S., Ishikawa, J. (2020). Nonlinear Disturbance Observer with Recurrent Neural Network Compensator. In: Zelinka, I., Brandstetter, P., Trong Dao, T., Hoang Duy, V., Kim, S. (eds) AETA 2018 - Recent Advances in Electrical Engineering and Related Sciences: Theory and Application. AETA 2018. Lecture Notes in Electrical Engineering, vol 554. Springer, Cham. https://doi.org/10.1007/978-3-030-14907-9_56
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DOI: https://doi.org/10.1007/978-3-030-14907-9_56
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