Abstract
This paper uses simulation to investigate the convergence and the accuracy of the Bounded Error Algorithm (BEA) for Iterative Learning Control (ILC). The BEA resolves the problem of transient growth of the tracking error that appears as one main problem of the nonlinear ILC procedure. This algorithm ensures that trajectory-tracking errors are constrained by an upper limit of a given error norm. The results from a computer simulation of a PUMA 560 robot arm are hereby analysed. The overall convergence rate of the learning control method is evaluated subject to the precision of the differential equation solver and the influence of deterministic disturbances. The numerical procedures of this method are presented in a way suitable for implementation in real industrial robotics.
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Acknowledgment
This work is supported by the Fund for Scientific Research at Sofia University “St Kl. Ohridski” under grant 129/2016.
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Yovchev, K., Delchev, K., Krastev, E. (2017). Computer Simulation of Bounded Error Algorithm for Iterative Learning Control. In: Rodić, A., Borangiu, T. (eds) Advances in Robot Design and Intelligent Control. RAAD 2016. Advances in Intelligent Systems and Computing, vol 540. Springer, Cham. https://doi.org/10.1007/978-3-319-49058-8_15
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DOI: https://doi.org/10.1007/978-3-319-49058-8_15
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