  Tube-based model predictive control (MPC) methods bound deviations from a
nominal trajectory due to uncertainties in order to ensure constraint
satisfaction. While techniques that compute the tubes online reduce
conservativeness and increase performance, they suffer from high and
potentially prohibitive computational complexity. This paper presents an
asynchronous computation mechanism for system level tube-MPC (SLTMPC), a
recently proposed tube-based MPC method which optimizes over both the nominal
trajectory and the tubes. Computations are split into a primary and a secondary
process, computing the nominal trajectory and the tubes, respectively. This
enables running the primary process at a high frequency and moving the
computationally complex tube computations to the secondary process. We show
that the secondary process can continuously update the tubes, while retaining
recursive feasibility of the primary process.
