Duhem Model-Based Hysteresis Identification in Piezo-Actuated Nano-Stage Using Modified Particle Swarm Optimization

Area of Science:

• * Control Systems Engineering
• * Materials Science
• * Nanotechnology

Background:

• * Piezoelectric actuators are crucial for high-precision nano-positioning systems.
• * Hysteresis in piezoelectric materials complicates accurate modeling and control.
• * The Duhem model is a common approach to describe hysteresis phenomena.

Purpose of the Study:

• * To develop an advanced optimization technique for identifying Duhem model parameters.
• * To improve the accuracy of hysteresis modeling in piezoelectric nano-stages.
• * To overcome limitations of traditional optimization algorithms in parameter identification.

Main Methods:

• * Modeling the parameter identification of the Duhem model as an optimization problem.
• * Proposing a Modified Particle Swarm Optimization (MPSO) technique with a randomness operator.
• * Validating the MPSO method using benchmark functions and experimental data.

Main Results:

• * The proposed MPSO method demonstrated superior performance in escaping local optima.
• * MPSO achieved accurate identification of Duhem model parameters for piezoelectric nano-stages.
• * Experimental validation confirmed the effectiveness of the MPSO-based identification scheme.

Conclusions:

• * The MPSO technique offers a robust and effective solution for Duhem model parameter identification.
• * This approach enhances the modeling accuracy of hysteresis in piezoelectric actuated systems.
• * MPSO shows significant advantages over conventional Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).