The present invention relates to a novel coincidence-based magnetic hysteresis modeling of a piezo actuator using an adaptive neurofuzzy inference system and its compensator, and proposes a new approach for modeling and compensating strain independent magnetic hysteresis of a piezo actuator. A model named self-history based on consensus was developed based on two very important characteristics of self history. This is match and wipe-out. The proposed approach consists of two parts for monotonic increase and decrease of the input excitation. To recognize this model, the data set of the primary minor loop values will be predetermined. This can be solved using the Adaptive Neurofuzzy Inference System (ANFIS) technique and experimental data. With this technique, the input-output relationship of the primary minor loop was evaluated efficiently. In addition, the ANFIS technique is also used to construct a data set of inverse primary minor loop values, which is an integral part of a coincidence-based magnetic hysteresis compensator. Several experiments in modeling and open-loop control are constructed to show the effectiveness of the proposed approach. In addition, a comparative study between the proposed approach and one of the previous studies is undertaken to express the usefulness of the proposed method.
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