Uniting Iteration Limits for Mixed-Integer Quadratic MPC

TL;DR

This paper develops a hybrid MPC approach that combines low and high iteration limit solvers for mixed-integer quadratic programs, ensuring stability and robustness in control systems, and demonstrates its effectiveness through simulations.

Contribution

It introduces a novel hybrid MPC controller uniting two MIQP solvers with different iteration limits, with theoretical stability guarantees and practical algorithms.

Findings

The hybrid controller maintains stability under varying iteration limits.

The algorithms are effective in spacecraft rendezvous simulations.

The approach improves control performance with limited solver iterations.

Abstract

Iteration limited model predictive control (MPC) can stabilize a feedback control system under sufficient conditions; this work explores combining a low iteration limit MPC with a high iteration limit MPC for mixed-integer quadratic programs (MIQPs) where the suboptimality is due to solver iteration limits. To combine the two MPCs a hybrid systems controller is developed that ``unites'' two MIQP-MPC solvers where the iteration limits of interest are the branch-and-bound and quadratic programming iteration limits. Asymptotic stability and robustness of the hybrid feedback control system are theoretically derived. Then an interpretable branch-and-bound algorithm and implementable uniting controller algorithm are developed. Finally, the developed algorithms and varying iteration limits are empirically evaluated in simulation for the switching thruster and minimum thrust spacecraft rendezvous problems.