This paper introduces a novel concept of dual-rate non-linear high order holds, based on artificial neuronal networks, in order to improve control, robustness and stability margin of non-linear processes. The main idea is that artificial networks provide accurate inter-sampling data estimation in dual-rate systems, allowing controlling the process at the fastest possible rate. In addition to this, the paper compares the performance with other approaches taking into account the ideal but non-feasible closed loop at high frequency. For that purpose, the paper considers metrics such as mean square error and settling time to measure the overall performance. The proposed dual-rate non-linear holds have been tested in both, simulation and real processes, and particularly, in an industrial robot within an image-based visual servoing application. The new approach improves with respect to the conventional single-rate behavior and showing higher stability margin than conventional dual-rate holds. © 2012 Springer-Verlag.
CITATION STYLE
Solanes, J. E., Armesto, L., Tornero, J., Muñoz-Benavent, P., & Girbés, V. (2012). Dual-rate non-linear high order holds for visual servoing applications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7429 LNAI, pp. 152–163). https://doi.org/10.1007/978-3-642-32527-4_14
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