Συντάχθηκε 10-09-2025 10:38
Τόπος:
Σύνδεσμος τηλεδιάσκεψης
Έναρξη: 29/09/2025 12:00
Λήξη: 29/09/2025 14:00
TECHNICAL UNIVERSITY OF CRETE
School of Electrical and Computer Engineering
Doctoral Program of Studies
PhD Thesis Defense of Amirhossein Samii titled:
Predictor-Based Cooperative Adaptive Cruise Control of Vehicular Platoons with Actuation and Communication Delays
Doctoral Thesis Committee
Associate Professor Nikolaos Bekiaris-Liberis, TUC, School of ECE (supervisor)
Professor Eftychios Koutroulis, TUC, School of ECE
Professor Thrasyvoulos Spyropoulos, TUC, School of ECE
Professor Michail Lagoudakis, TUC, School of ECE
Professor Stefania Santini, University of Napoli Federico II
Professor Themistoklis Charalambous, University of Cyprus
Professor Meng Wang, Technische Universitat Dresden
Abstract
Traffic flow efficiency and safety can be significantly improved via Cooperative Adaptive Cruise Control (CACC) of vehicular platoons. One critical property in vehicle platooning is string stability, which is essential for ensuring both safety and efficiency. String stability serves as a key indicator of how efficiently disturbances are attenuated as they propagate upstream in a platoon. However, the benefits of string (and vehicle) stability may be compromised in the presense of delays in actuation, sensing, or communication.
This research aims to develop predictor-based CACC designs to compensate the negative effects of long actuation and communication delays in vehicular platoons. The proposed control design framework is applied to heterogeneous vehicular platoons, where each vehicle’s dynamics are modeled by a third-order linear system with input delay. For each design we develop, we establish vehicle stability, string stability, and tracking of the desired speed/spacing. The proofs of individual vehicle stability, string stability, and regulation rely mainly on employment of an input-output approach on the frequency/time domains. We present consistent simulation results, including validations with real traffic data. We further provide experimental validation results, in a pair of vehicles, of one of our predictor-based CACC designs.
Meeting ID: 920 0670 6930
Password: 662698
