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Robust Model Predictive Control with Waysets

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Waypoint guidance is a well-known technique for reducing the computational complexity of large control problems, especially those pertaining to vehicle trajectory planning. However, it suffers from the inability to provide robustness guarantees in the presence of persistent disturbances. This talk will introduce the notion of waysets as a generalisation of waypoints, allowing robustness issues to be addressed. It will be shown how model predictive control, combined with wayset guidance and constraint tightening, can robustly steer the state of a system in the presence of bounded disturbances, whilst significantly reducing computational complexity when compared to existing approaches. A simple UAV model with non-convex minimum speed and collision avoidance constraints will be used to demonstrate the technique, as well as motivate other potential applications of the developed theory.

This talk is part of the CUED Control Group Seminars series.

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