Input-output selection for planar tensegrity models

TL;DR: Input-output selection for planar tensegrity models efficiently selects actuators and sensors to achieve desired performance levels, based on a rigorous and systematic procedure.

Abstract: The input-output selection approach followed in this brief uses a rigorous and systematic procedure, efficiently selecting actuators and/or sensors that guarantee a desired level of performance, embedded in a heuristic. The procedure generates all so-called minimal dependent sets and uses a closed-loop criterion. The heuristic is a divide-and-conquer one. This approach is applied to controlled tensegrity structures, using as criterion efficiently computable conditions for the existence of a stabilizing H/sub /spl infin// controller achieving a desired level of performance. Structural systems, like controlled tensegrities, are a prime example for application of techniques that address system design issues, because they present opportunities in choosing actuators/sensors and in choosing their mechanical structure. Results for a three-unit planar tensegrity structure, where all 26 tendons can be used as actuator or sensor devices, making up 52 devices from which to choose, demonstrate the approach. Two design specifications were explored, one is related to the dynamical stiffness of the structure, the other to vibration isolation. The feasible sets of actuators and sensors depend on the specifications and really differ for both, but are mostly composed of much less than 52 devices.