Step response classification for model-based autotuning via polygonal curve approximation |
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Authors: | Luigi Piroddi Alberto Leva |
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Affiliation: | aDipartimento di Elettronica e Informazione, Politecnico di Milano, Via Ponzio, 34/5, 20133 Milano, Italy |
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Abstract: | A model-based autotuning method consists of an identification and a regulator tuning phase. To achieve satisfactory performance and robustness, it is advisable that both phases be tailored a priori to the characteristics of the observed process dynamics. Such characteristics include, but are not limited to, the model structure. For example, overdamped and underdamped models with the same pole-zero structure are parametrised and controlled in different ways. Step response data, that are typically used for the identification phase in the autotuning context, can also be pre-processed to reveal those characteristics. This paper presents a step response classification method suitable for the above purpose. The method is based on a polygonal curve approximation technique for data pre-processing, followed by a neural network classifier. Only normalised I/O data are employed, so that the neural network can be trained off-line with simulated data. Simulation results are reported to show the effectiveness of the proposed classification method in terms of the achievable tuning results. |
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Keywords: | Model classification System identification Pattern recognition Polynomial curve approximation Autotuning Industrial control Process control |
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