# Backstepping Controller for Nanopositioning in Piezoelectric Actuators with ANN Hysteresis Compensation

## TL;DR

This paper introduces a new controller combining backstepping and neural networks to improve precision in piezoelectric actuators by reducing hysteresis effects.

## Contribution

A novel backstepping controller with ANN-based hysteresis compensation for enhanced nanopositioning accuracy.

## Key findings

- The proposed controller achieves significantly lower tracking errors compared to a conventional PID controller.
- Improved accuracy and robustness are confirmed through real-time experiments and error metrics.
- The method shows effectiveness across various reference signals and frequencies.

## Abstract

Piezoelectric actuators (PEAs) are widely used in high-precision applications but suffer from nonlinear hysteresis effects that degrade positioning accuracy. To address this challenge, this study presents a backstepping controller with an Artificial Neural Network (ANN)-based feedforward compensation scheme to enhance trajectory tracking performance. The ANN compensates for the hysteresis effects, while the backstepping strategy ensures robust reference tracking. The proposed controller is validated through real-time experiments using a piezoelectric actuator system. Comparative analysis with a conventional PID controller demonstrates the superiority of the backstepping approach, achieving significantly lower tracking errors across different reference signals and frequencies. Error metrics have been employed to confirm the improved accuracy and robustness of the proposed method. These findings highlight the effectiveness of the proposed ANN-enhanced backstepping control in overcoming hysteresis-related challenges in precision positioning applications.