Parallelized Algorithm for Persistent Feasibility in Linear Systems with Multiple, External Switching Signals
Ensuring feasibility in externally switched systems usually requires identifying time-varying, control invariant (CI) sets that ensure state and input constraints can be respected under any possible switching signal. As with traditional, time-invariant CI sets, these time-varying constraints can be...
Saved in:
Published in | 2022 IEEE 61st Conference on Decision and Control (CDC) pp. 485 - 690 |
---|---|
Main Authors | , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
06.12.2022
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | Ensuring feasibility in externally switched systems usually requires identifying time-varying, control invariant (CI) sets that ensure state and input constraints can be respected under any possible switching signal. As with traditional, time-invariant CI sets, these time-varying constraints can be very difficult to compute for higher dimensional systems. Furthermore, if multiple switching signals are present, the number of switching sequences that must be considered grows exponentially. Previous works would struggle to account for this growth. Inspired by distributed systems, this work examines a class of high dimensional systems with multiple switching signals. The switching signals are constrained using directed graphs that are significantly more flexible than dwell time based methods. An iterative algorithm is developed that computes the time-varying CI sets for this class of systems. Critically, this algorithm is parallelized over the number of agents, preventing the exponential growth in computation time as agents are added to the system. The scalability of the results is demonstrated on a randomized numerical example. |
---|---|
ISSN: | 2576-2370 |
DOI: | 10.1109/CDC51059.2022.9992861 |