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Decentralized Control for CACC Systems Accounting for Uncertainties Politecnico di Torino
- Format:
- Book
- Conference/Event
- Author/Creator:
- Seifoddini, Arash, author.
- Conference Name:
- CO2 Reduction for Transportation Systems Conference (2024-06-12 : Turin, Italy)
- Language:
- English
- Physical Description:
- 1 online resource cm
- Place of Publication:
- Warrendale, PA SAE International 2024
- Summary:
- Traditional CACC systems utilize inter-vehicle wireless communication to maintain minimal yet safe inter-vehicle distances, thereby improving traffic efficiency. However, introducing communication delays generates system uncertainties that jeopardize string stability, a crucial requirement for robust CACC performance. To address these issues, we introduce a decentralized model predictive control (MPC) approach that incorporates Kalman filters and state predictors to counteract the uncertainties posed by noise and communication delays. We validate our approach through MATLAB/Simulink simulations, using stochastic and mathematical models to capture vehicular dynamics, Wi-Fi communication errors, and sensor noises. In addition, we explore the application of a reinforcement learning (RL)-based algorithm to compare its merits and limitations against our decentralized MPC controller, considering factors like feasibility and reliability
- Notes:
- Vendor supplied data
- Publisher Number:
- 2024-37-0010
- Access Restriction:
- Restricted for use by site license
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