Software-based neural network assisted movement compensation for nanoresolution piezo actuators

Kauppinen, Marko; Röning, Juha

doi:10.1117/12.905749

Cited by 2 publications

(1 citation statement)

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“…Furthermore, the robustness and the tracking performance were greatly improved. In [ 42 ], a control strategy of utilizing the neural network as the system feedback controller was proposed, and by conducting the simulations with the collected data, the effectiveness of the developed approach was validated and the positioning precision of the PEA could be improved greatly.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Self-Tuning Control for a Piezoelectric Actuator

Zhang

Gao

et al. 2020

Sensors

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Piezoelectric actuators (PEA) have been widely used in the ultra-precision manufacturing fields. However, the hysteresis nonlinearity between the input voltage and the output displacement, which possesses the properties of rate dependency and multivalued mapping, seriously impedes the positioning accuracy of the PEA. This paper investigates a control methodology without the hysteresis model for PEA actuated nanopositioning systems, in which the inherent drawback generated by the hysteresis nonlinearity aggregates the control accuracy of the PEA. To address this problem, a neural network self-tuning control approach is proposed to realize the high accuracy tracking with respect to the system uncertainties and hysteresis nonlinearity of the PEA. First, the PEA is described as a nonlinear equation with two variables, which are unknown. Then, using the capabilities of super approximation and adaptive parameter adjustment, the neural network identifiers are used to approximate the two unknown variables automatically updated without any off-line identification, respectively. To verify the validity and effectiveness of the proposed control methodology, a series of experiments is executed on a commercial PEA product. The experimental results illustrate that the established neural network self-tuning control method is efficient in damping the hysteresis nonlinearity and enhancing the trajectory tracking property.

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Section: Introductionmentioning

confidence: 99%