Transfer Learning-Based Multi-Objective Predictive Control for Vehicular Platoon

Le résumé fourni par la source

This paper proposes a novel transfer learning-based multi-objective predictive control (TLMPC) strategy to reduce the computational burden for the vehicular platoon. A data-driven model is established with subspace identification to alleviate the adverse effects of complex powertrain structure of electric vehicles. Then, a grey wolf optimizer is devised to navigate the search process, striving for globally optimal trade-offs among the conflicting multi-objectives. To improve the calculation efficiency, a transfer learning method is suggested to the platoon. Eventually, several experimental results demonstrate the effectiveness of the proposed TLMPC strategy.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.