Abstract: In this work, we provide a real-time algorithmic
optimal control framework for autonomous switched systems. Traditional
optimal control approaches for autonomous switched system are open-loop
in nature. Therefore, the switching times of the system can not be
adjusted or adapted when the system parameters or the operational
environments change. We aim to close this loop, and apply adaptations
to the optimal switching strategy based on new information that can
only be captured on-line. One important contribution of this work is to
provide the means to allow feedback (in a general sense) to the control
laws (i.e. the switching times) of the switched system so that the
control law can be updated to maintain optimality of the switching-time
control inputs. Furthermore, convergence analyses for the proposed
algorithms are presented. The second part of the talk involves a
receding horizon based approach to surveillance of a robot using
temporal logic specifications.
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