Frequency-response-data-based optimization of the controller for a compound dual-stage nano-positioning system.

Area of Science:

• Control Engineering
• Nanotechnology
• Mechatronics

Background:

• Traditional complementary-filter-based parallel control for dual-stage nano-positioning systems suffers from performance limitations due to model-based sequential design.
• Existing methods are susceptible to errors introduced during system identification.

Purpose of the Study:

• To propose a frequency-response-data-based optimization approach for simultaneous and systematic design of complementary-filter-based dual-feedback controllers.
• To enhance the control bandwidth and achieve a flat amplitude frequency response for nano-positioning systems.

Main Methods:

• A constrained optimization problem is formulated using frequency response data for controller design.
• Nyquist stability analysis is detailed for the proposed design procedure.
• Direct utilization of frequency response data minimizes the impact of system identification errors.

Main Results:

• Comparative experiments on a dual-stage nano-positioning system validate the proposed approach.
• The root-mean-square tracking error was reduced from 70.6 nm to 21.3 nm at 50 Hz sinusoidal tracking.
• The new method demonstrates clear superiority over baseline integral controllers.

Conclusions:

• The frequency-response-data-based optimization offers a systematic and effective method for designing dual-feedback controllers in nano-positioning systems.
• This approach enhances system performance by improving control bandwidth and reducing tracking errors.