Design and Assessment of a Decentralized Control Strategy for CACC Systems accounting for Uncertainties

This thesis primarily investigates the optimization of traffic flow and vehicular coordination in Cooperative Adaptive Cruise Control (CACC) systems under real-world uncertainties. Traditional CACC systems utilize inter-vehicle wireless communication to maintain minimal yet safe inter-vehicle distances, thereby improving traffic efficiency. However, the introduction of communication delays generates system uncertainties that jeopardize string stability, a crucial requirement for robust CACC performance. Existing solutions often expand the time headway based on upper-bound delay estimates, which inadvertently diminishes the system's traffic throughput. 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. Our method significantly reduces the minimum time gap required for string stability compared to existing models. We validate our approach through MATLAB Simulink simulations, using stochastic and mathematical models to capture vehicular dynamics, Wi-Fi communication errors, and sensor noises. Our evaluation employs time-domain methods due to the complexities associated with frequency-based analyses in MPC controllers. In the final phase, 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. By improving the robustness and safety of CACC algorithms in the face of uncertainties, this research paves the way for their broader application in modern transportation systems.