Safety Control for Order Preserving Systems with Applications to Intelligent Transportation
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In this thesis we develop computational tools for the safety control of multi-agent systems, with applications towards intersection collision avoidance within traffic networks. For multi-agent systems, the classical approach to the safety problem, or that of computing the maximal safe controlled invariant set, is computationally prohibitive in practice, where state information is imperfect (due to sensor and plant uncertainty) and controllers need be implemented in a distributed manner. We exploit the order preserving properties of the system under study to compute the capture set in an efficient manner, which is used to construct the set-valued feedback map that renders the system safe.
The algorithms developed in this thesis have been successfully tested on an industry development platform, consisting of Lexus IS250 test vehicles with on-board Dedicated-Short-Range-Communication (DSRC) and active longitudinal control.