Control of Systems with MEMS Sensors and Actuators via Data Mining Techniques

University Of California-Los Angeles, Los Angeles CA

Investigators

Abstract

The goal of this interdisciplinary research is to analyze the vast amounts of MEMS sensor data using datamining techniques to discover relationships among actions at MEMS actuators and their impact on the system state. These relationships (captured in the form of rules) are then used to build a feedback loop for aircraft control. The input-output relationships for most systems (e.g., the delta wing aircraft) are highly non-linear. Traditional datamining approaches discard much important information from the datasets and cannot provide sufficient transfer function information, which makes them unsuitable for system control. This project develops a scalable multivariate datamining technique that discovers full sensor-actuator relationships and predictive models under a wide range of conditions (dynamic, temporal, spatial, etc.). The research includes collecting data for dynamic system behavior, extending the datamining algorithms for summarizing temporal rules, developing the rule selection strategy for actuation schema, and developing wind tunnel experiments to validate the approach. This work has the potential to advance the state-of-the-art in data mining substantially, as this problem has many features (real time feedback, spatio-temporal nature) that are not commonly found in other applications. The success of data mining techniques is expected to advance the MEMS sensor and actuation technology in system monitoring and control and in other engineering problems.