2019/02/07 by Danylo Malyuta, Malyuta, Danylo, Michael Szmuk +3 · 1 citation
Computer Science · Engineering · #Optimization and Variational Analysis #Aerospace Engineering and Control Systems #Stability and Control of Uncertain Systems
paper · pdf · doi:10.48550/arxiv.1902.02726
This paper presents a convex optimization-based method for finding the globally optimal solutions of a class of mixed-integer non-convex optimal control problems. We consider problems that are non-convex in the input norm, which is a semi-continuous variable that can be zero or lower- and upper-bounded. Using lossless convexification, the non-convex problem is relaxed to a convex problem whose optimal solution is proved to be optimal almost everywhere for the original problem. The relaxed problem can be solved using second-order cone programming, which is a subclass of convex optimization for which there exist numerically reliable solvers with convergence guarantees and polynomial time complexity. This is the first lossless convexification result for mixed-integer optimization problems. An example of spacecraft docking with a rotating space station corroborates the effectiveness of the approach and features a computation time almost three orders of magnitude shorter than a mixed-integer programming formulation.