A dual decomposition-based optimization method with guaranteed primal feasibility for hierarchical MPC problems

We present a gradient-based dual decomposition method that is suitable for hierarchical MPC of large-scale systems. The algorithm generates a primal feasible solution within a finite number of iterations and solves the problem by applying a hierarchical conjugate gradient method in each dual iterative ascent step. The proposed scheme uses constraint tightening and a suboptimality bound to ensure stability and feasibility in a hierarchical MPC problem.