Towards real-time ADMM for linear MPC

We present a novel predictive control scheme for linear constrained systems that uses the alternating direction method of multipliers (ADMM) for online optimization. In contrast to existing works on ADMM-based model predictive control (MPC), we only consider a single ADMM-iteration in every time step. The resulting real-time ADMM scheme is tailored for embedded and fast MPC implementations. The main difference to existing MPC schemes based on real-time iterations is that the proposed controller allows to include state and input constraints of the system. The paper derives the dynamics of the resulting closed-loop system, identifies important parameters of the real-time ADMM, and compares its performance to classical MPC.