Distributed Input Mapping Control for Multiple Mixed Platoons With a Flexible Structure Model

Research on the longitudinal control of mixed platoons has garnered significant attention, particularly addressing the uncertainties of human-driven vehicles (HDVs) and the computational inefficiency of centralized control for large platoons. This paper presents a novel distributed control strategy for multiple mixed platoons, utilizing a flexible model structure to account for dynamic uncertainties and lateral disturbances. A distributed control framework is introduced, where each platoon’s states and constraints are coupled with neighboring platoons, improving scalability and resilience in large-scale mixed traffic environments. Each platoon’s dynamic uncertainties are characterized by convex hulls in the subsystem model. A distributed data-driven input mapping method is proposed, where subsystems dynamically map the process data linearly from both local and neighboring platoons to local control law, eliminating the need for pre-collected datasets or offline training. To reduce iterations and communication requirements, a distributed implementation algorithm is proposed by designing subsystem basic laws with robust model predict control, then the control problem is solved online using convex quadratic programming, enabling fast computation of control actions and enhancing adaptability to real-time traffic variations. Additionally, a theoretical analysis guarantees the stability and optimality of the system, providing conditions that assist in subsystem parameter design for practical implementation. Simulation results validate the effectiveness and efficiency of the proposed approach, particularly in comparison to traditional methods that rely on pre-collected data.