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This paper proposes a new modeling approach that is experimentally validated on piezoelectric systems in order to provide a black-box pseudolinear model for complex systems control. Most of the time, one uses physical based approaches. However, sometimes complex phenomena occur in the system due to atypical changes of the process behavior, output noise or some hard nonlinearities. Therefore, we adopt identification methods to achieve the modeling task. The microdisplacements of the piezoelectric systems generate atypical data named outliers, leading to large estimated prediction errors. Since these errors disturb the classical normal probability density function, we choose here, as corrupted distribution model, the gross error model (GEM). In order to deal more efficiently with the outliers, we use the Huber's function, as mixed L 2 =L 1 norms in which the tuning threshold named scaling factor is extended. From this function, a cost function also named PREC as parameterized robust estimation criterion is established. The identification is performed by choosing an Output Error model structure. In order to express the asymptotic covariance matrix of the robust estimator, we present a L finite Taylor's expansion to linearize the gradient and the hessian of the PREC. Experimental results are presented and discussed.