Research On Data-Driven Control Methods For Heterogeneous Vehicle Platoons Under Various Communication Conditions

Intelligent Transportation Systems(ITS)play a pivotal role in expediting the establishment of a robust transportation infrastructure and fostering self-sufficiency in transportation technology.The research on platoon control within vehicular networks is essential for actualizing the potential of ITS and propelling the high-quality evolution of the autonomous driving industry.Heterogeneous platoons encounter challenges stemming from variations in inertial delays,driving/braking capabilities,and resistance characteristics,necessitating adaptive control strategies grounded in real-time data collection.Given the sensitivity and dynamic nature of Vehicular Ad Hoc Networks(VANET),which may result in communication interruptions and frequent restructuring of network topology,a robust control strategy becomes imperative.Additionally,the effective utilization of communication data resources underscores the demand for efficient control strategies.With the shift in control objectives from stability to efficiency,the complexity and difficulty of control escalates,introducing novel challenges to platoon control.These challenges manifest in several specific aspects:(1)In heterogeneous vehicle platooning systems,the variability in inertia delay and propulsion-braking dynamics,compounded by the difficulty in obtaining precise parameter values,can lead to significant fluctuations in safety distance control when relying on model-based methods.This inconsistency raises the risk of collisions.Leveraging real-time data through a data-driven approach to ensure the safety control of the platoon is of paramount importance.(2)Communication link interruptions prevent vehicles from exchanging information across the network.Singular cooperative modes fall short in meeting the requirements for stable platoon control.The stability of sampled systems under switching signal constraints and the queue stability of heterogeneous platoons under non-uniform parameters warrant further investigation.(3)Under the VANET communication framework,the multipath effect and limited communication range result in frequent topology restructuring among swiftly moving platoon vehicles.The rapid alternation of information transmission paths can induce transient system oscillations,posing challenges to system robustness and adversely affecting the uniform stability of heterogeneous platoons with non-fully connected topologies.(4)With the continuous growth of connected heterogeneous vehicle nodes and diversified communication demands,resources for control data transmission,storage,and computation become constrained.Traditional fixed-period sampling control strategies occupy significant resources.Hence,efficient data-enabled event-triggered platoon control strategies merit in-depth exploration.Centered on the aforementioned issues,this study delves into data-driven control for heterogeneous vehicle platooning under conditions of stable communication links,communication link interruptions,network topology restructuring,and limited communication resources.Specifically:Firstly,to address the challenge of maintaining safe inter-vehicle distances in heterogeneous mixed-traffic platoons due to dynamic differences,a distributed data-driven predictive control method is proposed,which relaxes the conditions for sufficient motivation.By integrating the Rouche-Capeli theorem,the study relaxes the criteria for data validity,establishes a predictive model mapping high-dimensional data to a low-dimensional subspace,determines the upper bound error between the continuous system state and the sampled state,and subsequently analyzes the input-state stability of the platoon system under TPLF topology.This demonstrates the string stability of autonomous vehicles,given the stability of human-driven vehicles.Secondly,to address the instability of heterogeneous vehicle platoons due to the inability to retrieve interactive information during communication link interruptions,a multi-modal adaptive data-driven switching predictive control method is introduced.This establishes a switching prior model for heterogeneous vehicle platooning during communication interruption.Utilizing Givens rotations,efficient recursive online updates for multi-modalities are achieved.With the inclusion of modal average dwell time,the study asserts system stability under finite switching instances,discusses the impact of state jumps under reset mapping on stability analysis complexity,and presents sufficient conditions for the string stability of a generalized heterogeneous vehicle platoon system.Next,to tackle the decline in adaptive performance of heterogeneous vehicle platoon systems due to frequent topology restructuring within limited communication ranges,a data-learning dynamic output regulation robust control approach is proposed.A platoon output regulation model,described by graph Laplacian,is established.A dynamic compensator is constructed to estimate the convergence of the lead vehicle state under varying directed topologies.Merging dynamic programming with the internal model principle,an iterative optimal control law is designed.This ensures system robustness under frequent topology restructuring,analyzing the convergence of the Riccati equation solution based on data learning,and the leader-follower uniform string stability of the interconnected platoon system.Lastly,to confront the challenge of balancing control performance and resource utilization due to restricted communication bandwidth and computational resources,an efficient control approach enabled by Gaussian process regression and event-triggering is put forward.This establishes a nonlinear heterogeneous vehicle feature representation under squared exponential kernel functions,designs an online learning feedback linearization control law targeting time-varying data,and analyzes the global ultimate boundedness of the platoon’s tracking error.A data-safe forgetting strategy and event-triggering mechanism are crafted to assure system asymptotic stability,determining the lower bound for the minimum event-triggering interval,and achieving efficient resource utilization for stable control of the heterogeneous vehicle platoon.