Ultra-precision control of dual-axis scanning mechanism based on PSO-RBF neural network.

ABSTRACT: Broadband ultra-high resolution spectrometers require arc-second level grating positioning accuracy to achieve picometer to femtometer spectral resolution. Traditional control approaches exhibit significant limitations when confronting system nonlinearities, parameter uncertainties, and external disturbances. This work presents an adaptive PID control methodology integrating Particle Swarm Optimization (PSO) with Radial Basis Function (RBF) neural networks. The approach establishes a pitch axis dynamic model incorporating friction, gravitational effects, and transmission backlash, then implements an RBF network architecture enabling real-time parameter adaptation. An enhanced PSO algorithm performs global network parameter optimization, creating a dual-stage control framework combining "of

SUBMITTER: Fan J 

PROVIDER: S-EPMC12638319 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature