Parallel solution of model predictive control using the alternating direction multiplier method

In this work a parallel solution method for model predictive control is presented based on the alternating direction multiplier method. Our approach solves the overall problem by decomposing it along the time-axis into smaller subproblem. The resulting subproblems can be solved efficiently in parallel and are coupled by the multiplier update. We specifically considers box constraints, since this allows to use tailored methods to solve the subproblems. We outline the applicability of the approach and the performance using a multi-core systems.