Nonlinear time-frequency iterative learning control for micro-robotic deposition system using adaptive Fourier decomposition approach

This study presents a cutting-edge approach to design iterative learning control (ILC) in micro-robotic deposition systems, utilizing nonlinear time-frequency analysis through adaptive Fourier decomposition (AFD). While ILC has demonstrated its effectiveness in achieving precise trajectory tracking, achieving a balance between robustness and convergence can be challenging. To address this challenge, we introduce a novel nonlinear time-frequency ILC design from a signal processing perspective, which exploits an advanced version of Fourier decomposition called AFD. By employing adaptive basis functions, AFD enables fast energy convergence during the control process. To reduce noise amplification and system delay, we propose a phase-lead ILC algorithm with zero amplitude attenuation. Additionally, we introduce a tunable bandwidth L-Q filter to achieve an optimal trade-off between robustness and convergence. The filter's bandwidth is adaptively adjusted based on the frequency content of the system, with a narrower bandwidth for low-frequency signals to accelerate convergence and a wider bandwidth for high-frequency signals to enhance robustness. Simulation results demonstrate the exceptional performance of the proposed ILC design in a micro-robotic deposition system.