A Novel Distributed Adaptive Controller for Parameters Estimation in Heterogeneous Vehicle Platooning With V2V Communication

The application of cooperative adaptive cruise control (CACC) in vehicle platoons with vehicle-to-vehicle (V2V) communication has received significant interest. CACC systems ensure string stability, enabling vehicles to maintain close inter-vehicle distances, thereby mitigating traffic jams and reducing fuel consumption. However, in realistic scenarios, vehicle platoons are typically heterogeneous, featuring diverse powertrain dynamics and uncertain parameters. To extend linear CACC systems that assume prior knowledge of vehicle parameters, novel adaptive controllers are developed. These controllers accommodate nonlinear and heterogeneous vehicle platoons with parametric uncertainties. To guarantee uniformly global asymptotic stability in nonlinear CACC systems, two innovative parameter updating laws are proposed and validated using Lyapunov's method. Additionally, a data selection and updating algorithm is implemented to construct a positive definite matrix and enhance the convergence rate of CACC controllers. Subsequently, examining transient behavior provides insights into tuning the adaptive controller gains. Finally, simulation and experimental results illustrate that both the baseline adaptive and novel adaptive controller exhibit superior state tracking compared to the feedback linearizing PD controller with nominal parameters. In addition, the novel adaptive controller presents enhanced estimation accuracy for unknown parameters, ensuring improved robustness in practical vehicle platoon applications. String stability evaluation in the frequency domain for a nonlinear CACC system is realized through the innovative adaptive control scheme.