A measure-based convex relaxation, built from graphs of convex sets, provides near-optimal lower bounds and scalable mode planning for hybrid optimal control.
Symmetry reduction and recovery of trajectories of optimal control problems via measure relaxations
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abstract
We address the problem of symmetry reduction of optimal control problems under the action of a finite group from a measure relaxation viewpoint. We propose a method based on the moment-SOS aka Lasserre hierarchy which allows one to significantly reduce the computation time and memory requirements compared to the case without symmetry reduction. We show that the recovery of optimal trajectories boils down to solving a symmetric parametric polynomial system. Then we illustrate our method on the symmetric integrator and the time-optimal inversion of qubits.
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Optimal Control of Hybrid Systems via Measure Relaxations
A measure-based convex relaxation, built from graphs of convex sets, provides near-optimal lower bounds and scalable mode planning for hybrid optimal control.