Andrew A Berlin

Distributed MEMS: new challenges for computation

How do you program a cloud of dust? That is just one computational challenge posed by MEMS, a technology in which multitudes of interacting tiny machines can add computational behavior to materials and the environment in an embedded, massively distributed fashion. Microelectromechanical systems, or MEMS, are an emerging set of technologies that make it possible to miniaturize and mass produce large numbers of integrated sensors, actuators, and computers. By merging sensing and actuation with computation and communication, MEMS devices can be distributed throughout the environment, coated on surfaces, or embedded within everyday objects to create distributed systems for sensing, reasoning about, and responding to events in the physical world on a scale never before possible. Distributed MEMS applications go well beyond the scaling limits of todays computational paradigms, posing serious challenges and new opportunities for information technology. MEMS will draw on and drive computation in four key areas: (1) control of large numbers of distributed MEMS sensors and actuators; (2) distributed intelligence, raising the general intelligence and capability of machines and matter; (3) MEMS devices as computational elements; (4) multiple energy domain simulation, analysis, and design. We look briefly at only the first of these areas: the problems and opportunities created by the control of large numbers (thousands to millions) of MEMS sensors and actuators, including coupling to the physical world and environment driven event time demands on computation.

MEMS-Based Control of Structural Dynamic Instability

This paper describes the design, analysis and characterization of a prototype active column that applies distributed MEMS technology to the active stabilization of a buckling compressive member. The axial load bearing capacity of structural members can be increased by actively controlling the dynamic instability of buckling. Effective active stabilization is dependent on three primary factors: sensor precision, actuator authority, and control system bandwidth. A networked array of MEMS sensors, filamentary PZT actuators, and recently developed optimal control strategies are combined to demonstrate active control of an inherently unstable column. The active system, designed and simulated using finite element and optimization methods, stabilizes an experimental column for compressive axial loads up to 2.9 times the critical buckling load. Additionally, the system is stable for all loads in the range from tension to this maximum compressive axial load.

Two Approaches to Distributed Manipulation

Two radically different approaches to distributed manipulation are reviewed. They each address scalability and manufacturing issues while producing forces sufficient to move macro-scale objects in different ways. The airjet system achieves scalability and manufacturability through macro-scale planar batch fabrication technology while PolyBot is modular, enabling mass production. Where PolyBot is suited to couple to non-planar objects through variable out-of-plane motion of the cilia, airjets are optimized for manipulation of planar objects with delicate surface features. The designs of both systems are well suited to hierarchical computation and communication to enable scalability without an explosion in the resource requirements.

Integrated centering control of inertially actuated systems

Abstract Inertially actuated systems are often hindered by the requirement that the actuator or controller be explicitly designed so that the actuators proof mass never exceeds the available stroke length. When the actuators proof mass reaches the end of its stroke a destabilizing input is created, which reduces performance. An integrated centering controller design that balances control effort with proof mass centering is presented. This approach enables the creation of controllers with reduced potential for stroke saturation. The controller developed is verified numerically for a buckling stabilization problem and experimentally for a vibration control example.