Enhancing tilt range of electrostatic torsional micromirrors using robust adaptive critic-based neurofuzzy control

Abstract

Electrostatic torsional micromirrors, as instances of Micro Electro Mechanical Systems (MEMS), have many optical network applications; such as optical wavelength-selective switches, optical cross-connects, etc. For all these applications, the micromirror needs to have minimal overshoot and settling time in order to minimize the time between two successive switching operations. Moreover, the controllability and stability of a torsional micromirror are major challenges due to high nonlinearities in its dynamic characteristics. In this paper, a robust adaptive critic-based neurofuzzy controller is proposed for electrostatic torsional micromirrors, which can improve the performance of the mirror tilting and enhance the robustness of the system to any stochastic perturbations. Furthermore, utilizing this adaptive neurofuzzy controller, which is based on a proportional and derivative (PD) critic, the micromirror "pull-in" phenomenon is crucially eliminated. Thus, the mirror tilting range is significantly expanded. Moreover, the stability of the closed-loop system is guaranteed via the Lyapunov theorem. The robust adaptive critic-based neurofuzzy controller is simulated for a 1-DOF electrostatic torsional micromirror and the results show the effectiveness of this approach for various tilt ranges and conditions. In addition, the robustness of this controller is examined in the presence of input noises and parameter uncertainties.

Keywords: Electrostatic torsional micromirror; MEMS; Pull-in angle; Robust adaptive critic-based neurofuzzy control.