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MEMS smart variable-geometry flexible flight control surfaces: distributed control and high-fidelity modeling

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1 Author(s)
Lyshevski, S.E. ; Dept. of Electr. Eng., Rochester Inst. of Technol., NY, USA

Distributed embedded control is a key methodology to design networked large-scale microelectromechanical systems (MEMS) that integrate micromachined transducers (actuators and sensors) and computing-processing- controlling and amplifying ICs. These MEMS allow one to design highly versatile robust, fault-tolerant, high-performance systems with the desired redundancy. Synthesis of distributed systems is a very complex problem due to complexity of networked MEMS to be controlled as well as rigid performance and functional requirements imposed on software (flexibility, robustness, autonomy, real-time capabilities, computing, interfacing, execution time, fault tolerance, etc.). Furthermore, control of networked MEMS must be accomplished within multi-objective goals, complex tasks, nonlinear dynamic behavior, constraints, delays, uncertainties, parameter variations, disturbances, etc. Systems design, explicit mathematical representations (accurate mathematical models), coherent control, adaptive networking and robust hardware-software co-design can dramatically improve performance. These problems are addressed and examined in this paper, except hardware- software co-design and software development. Enhanced functional and computing capabilities of ICs may ensure real-time implementation of complex controllers. This paper illustrates that it is possible to design and implement cognitive high-performance systems for networked large-scale MEMS to control smart variable-geometry flexible control surfaces. The reported results are directly applicable to deformable mirrors, smart lenses, membranes, vibro-acoustic cavities, smart skins, etc. This paper develops a modular platform formulating and solving a spectrum of complex problems for networked actuators, sensors and ICs. The application of MEMS guarantees exceptional flexibility, functionality, robustness, survivability as well as optimal adaptive (reconfigurable) dynamic behavior thereby ensuring superior overall performance in rapidly-changing dynamic environments under uncertainties.

Published in:

Decision and Control, 2003. Proceedings. 42nd IEEE Conference on  (Volume:5 )

Date of Conference:

9-12 Dec. 2003

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