2019/06/26 by Dominic Liao-McPherson, Liao-McPherson, Dominic, Marco M. Nicotra +9
Engineering · Mathematics · #Advanced Control Systems Optimization #Control Systems and Identification #FOS: Mathematics #Fault Detection and Control Systems #Optimization and Control (math.OC) #math.OC
paper · pdf · doi:10.48550/arxiv.1906.11363
openalex publication_date 2019/06/26 · arxiv created 2019/09/27 · arxiv updated 2019/10/01 · openalex created_date 2022/07/28 · openalex updated_date 2026/07/28
Model predictive control (MPC) is of increasing interest in applications for constrained control of multivariable systems. However, one of the major obstacles to its broader use is the computation time and effort required to solve a possibly non-convex optimal control problem (OCP) online. This paper introduces a sensitivity-based warmstarting strategy for systems with nonlinear dynamics and polyhedral constraints with the goal of reducing the computational footprint of MPC controllers. It predicts changes in the solution of the parameterized OCP as the parameter varies, by calculating the semiderivative of the solution mapping. The main novelty of the paper is that the polyhedrality of the constraints allows us to avoid imposing any constraint qualification conditions or strict complementarity assumptions. A numerical study featuring MPC applied to unmanned aerial vehicles illustrates the proposed approach.