Implementation of Model Predictive Control for Tracking in Embedded Systems Using a Sparse Extended ADMM Algorithm

This article presents a sparse, low-memory footprint optimization algorithm for the implementation of model predictive control (MPC) tracking formulation in embedded systems. MPC has several advantages over standard formulations, such as an increased domain attraction and guaranteed recursive feasibility even event sudden reference change. However, this comes at expense addition small amount decision variables to MPC's problem that complicates structure its matrices. We propose sparse algorithm, based on extension alternating direction method multipliers, exploits particular formulation. describe controller detail how is exploited by means aforementioned algorithm. show closed-loop simulations comparing proposed solver against other solvers approaches from literature.