Low-order control-oriented modeling of piezoelectric actuator using Huberian function with low threshold: pseudolinear and neural network models

Abstract

International audienceThis paper presents a new methodology of nonlinear system identification using Huberian function. Pseudolinear and Neural network black box model families are applied to identify a piezoelectric actuator for suspensions and vibration assisted drilling. Huberian function, which is a mixture of L2L2L\₂ and L1L1L\₁ norms with a threshold, is used to estimate a parameters vector in these model families. Pseudolinear black box model with reduced model order and balanced simplicity-accuracy neural network model families are proposed. Moreover, we show the interest to decrease the threshold in the Huberian function by providing efficient models for vibration drilling control. Experimental results are presented and discusse

Neural network modeling and single-neuron proportional–integral–derivative control for hysteresis in piezoelectric actuators

“…[12][13][14] NN techniques have developed rapidly; [15][16][17][18] research works involving the use of NNs for the modeling/correction of hysteresis have been published. [19][20][21][22] Deng and Tan 19 created an expanded input field of hysteresis for one-to-one mapping conversion by using a hysteretic operator and then used a NARMAX model to describe the hysteresis in PZT actuators. One new NARMAX model based on a back-propagation (BP) NN was proposed to model the nonlinear hysteresis in PZT actuators, and a linear controller based on adaptive inverse control was designed for the correction of hysteresis in PZT actuators.…”