Bipartite Consensus-Based Heterogeneous Vehicle Platoon Control Considering Differentially Private with Aperiodic Sampled-Data Interactions

This paper investigates the problem of achieving differential privacy protection while implementing aperiodic sampled-data averaging output bipartite consensus control in continuous-time heterogeneous connected vehicle platoons. The platoon incorporates both collaboration and competition among vehicles. First, a feedback linearization tool is applied to transform the nonlinear vehicle dynamics into a linear heterogeneous state space model. Then, a two-tier distributed control algorithm is proposed to design the hybrid distributed bipartite consensus controller, in which vehicles traveling in the same or opposite directions interact at discrete time instants. To ensure differential privacy, time-varying variance Laplace noise is introduced to protect the sensitive information of each vehicle. Next, the time-varying step size and noise parameters are determined such that the platoon reach bipartite consensus on an infinite time domain that satisfies the desired convergence accuracy and predefined upper bound on privacy. Finally, the controller is solved using the Riccati equation, thus enabling the preservation of individual vehicle data privacy while preserving the platoon bipartite consensus. Two simulation examples demonstrate the validity of the theoretical results.