Speaker
Description
Model Predictive Control (MPC) provides a powerful optimization-based framework for feedback design, but its real-time deployment remains computationally demanding. Suboptimal MPC schemes address this issue by terminating the underlying optimal control solver prematurely, thereby trading optimality for computational tractability.
In this talk, we develop a suboptimal MPC approach tailored to port-Hamiltonian systems that leverages dissipativity at multiple levels. Specifically, we exploit the dissipative structure of the optimality system induced by its primal–dual formulation, in conjunction with the intrinsic dissipativity of the port-Hamiltonian dynamics. This perspective enables a reformulation of the closed-loop scheme as a control-by-interconnection of two port-Hamiltonian systems.
Building on this structural viewpoint, we establish well-posedness and convergence of the resulting algorithm in an infinite-dimensional (function space) setting. In particular, we demonstrate mesh-independent convergence, highlighting the robustness of the approach with respect to discretization.