This paper proposes a new modeling approach which is experimentally validated on piezo-electric systems in order to provide a robust Black-box model for complex systems control. Industrial applications such as vibration control in machining and active suspension in transportation should be concerned by the results presented here. Generally one uses physical based approaches. These are interesting as long as the user cares about the nature of the system. However, sometimes complex phenomena occur in the system while there is not sufficient expertise to explain them. Therefore, we adopt identification methods to achieve the modeling task. Since the microdisplacements of the piezo-system sometimes generate corrupted data named observation outliers leading to large estimation errors, we propose a parameterized robust estimation criterion based on a mixed L2 – L1 norm with an extended range of a scaling factor to tackle efficiently these outliers. This choice is motivated by the high sensitivity of least-squares methods to the large estimation errors. Therefore, the role of the L1-norm is to make the L2-estimator more robust. Experimental results are presented and discussed.

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