Multi-objective Predictive Control for non steady-state operation

The concept of dynamic-mean Pareto optimality is introduced for multi-objective Model Predictive Control. Dynamic-mean Pareto optimal solutions are obtained by solving a free initial state and final time optimal control problem. Subsequently, we propose a receding horizon tracking formulation with dynamic-mean Utopia set-points. A Dynamic-mean Utopia point is defined as the intersection of average minima, of underlying performance indices, over a dynamic horizon. The latter is compared with recently proposed steady-state Utopia tracking and Pareto optimally weighted Economic MPC. Incorporating dynamic-mean Utopia set-points in a tracking formulation, one attains economic performance at least equal to that of steady-state Utopia tracking, and, performance close to that of Pareto optimal, weighted Economic MPC. The latter is illustrated for a CSTR numerical case example.