Cooperative Adaptive Cruise Control System Based on Interacting Multiple Model Cubature Kalman Filter and Robust Model Predictive Control

With the advancement of Internet of Vehicles technology, V2V-based cooperative adaptive cruise control (CACC) is poised to address variable traffic conditions. Considering the inevitability of communication failures, this paper introduces a robust CACC framework leveraging an on-board estimator, which integrates an interacting multiple model cubature Kalman filter (IMM-CKF) and a robust model predictive control (R-MPC) strategy with terminal constraints, designed to satisfy multiple driving requirements (safety, stability and driving experience). Due to the ill-conditioning calculations during estimation, the IMM method is introduced to tackle this issue. Meanwhile, the basic vehicle following model is enhanced to incorporate control signal increments and the safety concerns are managed through state constraints. Combined with H_∞ control theory and linear matrix inequality (LMI) theory, a terminal invariant set is developed to ensure the stability of the MPC under constraints, and the stability of this strategy is demonstrated using a Lyapunov function. Moreover, building on existing research, we analyze the string stability of a CACC system, a general condition ensuring string stability is identified through preliminary experiments. Subsequent numerical experiment and simulations validate the outstanding performance of the R-MPC strategy against the tube-MPC strategy.