2019/10/17 by Tobia Marcucci, Russ Tedrake, Marcucci, Tobia +1 · 4 citations
Engineering · #Advanced Control Systems Optimization #FOS: Electrical engineering #FOS: Mathematics #Fault Detection and Control Systems #Optimization and Control (math.OC) #Process Optimization and Integration #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1910.08251
openalex publication_date 2019/10/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In hybrid Model Predictive Control (MPC), a Mixed-Integer Quadratic Program\n(MIQP) is solved at each sampling time to compute the optimal control action.\nAlthough these optimizations are generally very demanding, in MPC we expect\nconsecutive problem instances to be nearly identical. This paper addresses the\nquestion of how computations performed at one time step can be reused to\naccelerate (warm start) the solution of subsequent MIQPs.\n Reoptimization is not a rare practice in integer programming: for small\nvariations of certain problem data, the branch-and-bound algorithm allows an\nefficient reuse of its search tree and the dual bounds of its leaf nodes. In\nthis paper we extend these ideas to the receding-horizon settings of MPC. The\nwarm-start algorithm we propose copes naturally with arbitrary model errors,\nhas a negligible computational cost, and frequently enables an a-priori pruning\nof most of the search space. Theoretical considerations and experimental\nevidence show that the proposed method tends to reduce the combinatorial\ncomplexity of the hybrid MPC problem to that of a one-step look-ahead\noptimization, greatly easing the online computation burden.\n