Buffer-Aided Model Predictive Controller to Mitigate Model Mismatches and Localization Errors
Artikel i vetenskaplig tidskrift, 2019
Any vehicle needs to be aware of its localization, destination, and neighboring vehicles' state information for collision free navigation. A centralized controller computes controls for cooperative adaptive cruise control (CACC) vehicles based on the assumed behavior of manually driven vehicles (MDVs) in a mixed vehicle scenario. The assumed behavior of the MDVs may be different from the actual behavior, which gives rise to a model mismatch. The use of erroneous localization information can generate erroneous controls. The presence of a model mismatch and the use of erroneous controls could potentially result into collisions. A controller robust to issues such as localization errors and model mismatches is thus required. This paper proposes a robust model predictive controller, which accounts for localization errors and mitigates model mismatches. Future control values computed by the centralized controller are shared with CACC vehicles and are stored in a buffer. Due to large localization errors or model mismatches when control computations are infeasible, control values from the buffer are used. Simulation results show that the proposed robust controller with buffer can avoid almost the same number of collisions in a scenario impacted by localization errors as that in a scenario with no localization errors despite model mismatch.
Robust model predictive control