Privacy-Preserving Platoon Control—Constrained Cooperative-Tracking Control via Time-Varying Heterogeneous Directed Networks

Distributed cooperative tracking control has emerged as a pivotal research focus in multi-agent systems, particularly for platoon control applications where its decentralized architecture offers significant advantages over centralized approaches. However, the direct exchange of sensitive data between agents raises critical privacy risks, hindering its broader adoption across safety-critical applications. This paper presents a privacy-preserving cooperative tracking framework that rigorously maintains bounded coupling errors, which is a crucial requirement for collision avoidance in vehicular platoons. Departing from conventional methods that compromise privacy through explicit state sharing for error mitigation, our proposed algorithm achieves dual objectives: maintaining prescribed error constraints while preserving agent state confidentiality in directed communication networks with time-varying interaction weights. We establish sufficient conditions for achieving cooperative-tracking consensus with predefined error constraints and characterize the quantitative relationship between the asymptotic convergence rate and control gain parameters. Furthermore, we analyse the privacy-preserving performance against internal and external adversaries, demonstrating that the probability of an adversary inferring states within a finite neighborhood of ground-truth values can be rendered arbitrarily small, even while adversaries retain access to identical communication data streams. This extends classical initial-state privacy to the entire operational timeline under time-varying directed topologies. Numerical examples including an application of cooperative adaptive cruise control demonstrate our proposed algorithm’s efficacy.