Averaging Conflicting Objectives in Economic Nonlinear MPC for Adaptive Cruise Control

Optimizing the energy consumption of electric vehicles (EVs) during operation is a key factor in mitigating their overall environmental impact. Autonomous vehicle functions, such as Adaptive Cruise Control (ACC), typically disregard economic criteria such as energy optimization, being in general not trivial to conciliate tracking and economic control tasks. Within the domain of optimal control, Economic Nonlinear MPC (E-NMPC) is designed to deliver an economically optimal control action, optimizing the economic profit of the plant. However, E-NMPC does not allow to include additional adversarial tasks, such as tracking, and its closed-loop stability is not easy to guarantee. In this work, we propose a novel E-NMPC formulation for conflicting control objectives - such as tracking and economic tasks - that attains the optimal trade-off between them. Furthermore, we propose a constructive procedure to design stabilizing terms for E-NMPC, ensuring its closed-loop stability with minimal impact on the economic performance. We apply the proposed E-NMPC strategy to the ACC case study, proving its effectiveness in simulation: the E-NMPC-based ACC proficiently attains the conflicting tasks, delivering a higher economic profit than standard NMPC, while ensuring closed-loop stability.