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.
Decentralized Control for CACC Systems Accounting for Uncertainties / Seifoddini, A.; Azad, A.; Musa, A.; Misul, D.. - In: SAE TECHNICAL PAPER. - ISSN 0148-7191. - ELETTRONICO. - 1:(2024). (Intervento presentato al convegno CO2 Reduction for Transportation Systems Conference tenutosi a Torino, Italy nel June 12-13).
Decentralized Control for CACC Systems Accounting for Uncertainties
Musa A.;Misul D.
2024
Abstract
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.Pubblicazioni consigliate
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https://hdl.handle.net/11583/2989522
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