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An improved sliding mode control (SMC) approach for enhancement of communication delay in vehicle platoon system

Handong Li, Haimeng Wu, Ishita Gulati, Saleh A. Ali, Volker Pickert, Satnam Dlay

2022IET Intelligent Transport Systems23 citationsDOIOpen Access PDF

Abstract

Abstract Vehicle platoon systems are widely recognized as key enablers to address mass‐transport. Vehicle‐to‐vehicle (V2V) and vehicle‐to‐infrastructure (V2I) are two technologies that drive platooning. The inter‐vehicle spacing and collaboration velocity in the platoon are important parameters that must be controlled. A new mass‐transport system called the Tracked Electric Vehicles (TEV) has been proposed which has reduced the inter‐vehicular spacing to only a quarter of the regular car length. This enables mass transport at uniform speed for cars with speed of 200km/h. However, conventional radar based adaptive cruise control (ACC) system fail to control each vehicle in these scenarios. Lately, sliding mode control (SMC) has been applied to control platoons with communication technology but with low speed and without delay. This paper proposes a novel SMC design for TEV using global dynamic information with the communication delay. Also, graph theory has been employed to investigate different V2V communication topology structures. To address issues of node vehicle stability and string stability, Lyapunov candidate function is chosen and developed. Additionally, this paper uses first‐order vehicle models with different acceleration/deceleration parameters for simulation validations under communication delay. The results show that this SMC has a significant tolerance ability and meets the design requirements of TEV.

Topics & Concepts

PlatoonSliding mode controlMode (computer interface)Computer scienceControl (management)Control theory (sociology)Automotive engineeringEngineeringControl engineeringArtificial intelligencePhysicsHuman–computer interactionQuantum mechanicsNonlinear systemTraffic control and managementVehicular Ad Hoc Networks (VANETs)Traffic Prediction and Management Techniques