Dual-Rate Non-Linear High Order Holds for Visual Servoing Applications
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.
KeywordsVisual servoing non-linear control machine learning
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