Learning Agent-Based Model Predictive Control for Holistic Vehicle Performance

Agent-based model predictive control (AMPC) has recently been proposed as a distributed scheme that collaborates with all agents to achieve optimal holistic performance. However, its optimality highly depends on the prediction accuracy that requires all agents or their contributions to be known, whi...

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Veröffentlicht in:	IEEE transactions on intelligent transportation systems 2024-11, Vol.25 (11), p.17482-17492
Hauptverfasser:	Zhong, Jiaming, Mehrizi, Reza Valiollahi, Pirani, Mohammad, Yu, Chao, Kasaiezadeh, Alireza, Pant, Yash Vardhan, Khajepour, Amir
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Actuators Computational modeling Gaussian process regression (GPR) Holistic vehicle control learning-based control Predictive control Real-time systems stochastic chance constraint Uncertainty Vehicle dynamics
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