The objective of this paper is to develop a novel two-level supervised fuzzy controller to stabilize the response of electrostatically actuated microbeams beyond their pull-in range. To this end, Lagrange equations are utilized to derive the differential equations governing the dynamic behavior of the system. To investigate the possibility of using a passive control strategy, the static behavior of the system is studied in detail. Through some open loop simulations, the qualitative and quantitative dependence of the beam deflection to the applied voltage and system parameters are studied. Based on the understanding obtained from these studies, a single level fuzzy controller is designed to control the response of the microstructure. In order to enhance the performance of the closed-loop system, another higher level supervisory fuzzy controller is designed to tune the maximum allowable voltage the lower level controller can apply. Simulation results reveal that both single level and multi-level fuzzy controllers can extend the travel range of the microbeams beyond its pull-in range. However the rise time, overshoot and settling time in the multilevel controlled system is far better than that of a simple single level fuzzy controller. The novel controller presented in this paper can be applied in most intrinsically nonlinear nano/micro structures to help them to have more efficient regulations and command tracking maneuvers.
- Dynamic Systems and Control Division
Deflection Control of Electrostatically Actuated Micro Cantilevers via Fuzzy Controller
Khadembashi, M, Moeenfard, H, & Ghasemi, AH. "Deflection Control of Electrostatically Actuated Micro Cantilevers via Fuzzy Controller." Proceedings of the ASME 2016 Dynamic Systems and Control Conference. Volume 1: Advances in Control Design Methods, Nonlinear and Optimal Control, Robotics, and Wind Energy Systems; Aerospace Applications; Assistive and Rehabilitation Robotics; Assistive Robotics; Battery and Oil and Gas Systems; Bioengineering Applications; Biomedical and Neural Systems Modeling, Diagnostics and Healthcare; Control and Monitoring of Vibratory Systems; Diagnostics and Detection; Energy Harvesting; Estimation and Identification; Fuel Cells/Energy Storage; Intelligent Transportation. Minneapolis, Minnesota, USA. October 12–14, 2016. V001T01A010. ASME. https://doi.org/10.1115/DSCC2016-9838
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