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For a set M of vertices and an edge e of a graph G, let $ P_G(M, e) $ P G (M , e) be the set of the pair $ (x, y) $ (x , y) with a vertex x of M and a vertex y of $ V(G) $ V (G) such that $ d_G(x, y)\neq d_{G-e}(x, y) $ d G (x , y) ≠ d G − e (x , y). For a vertex x, let $ EM(x) $ EM (x) be the edge set e such that there exists a vertex v in G with $ (x, v) \in P(\{x\}, e) $ (x , v) ∈ P ({ x } , e). A set M of vertices of a graph G is distance-edge-monitoring set if every edge e of G is monitored by some vertex $ v\in M $ v ∈ M , that is, for any $ e \in E(G) $ e ∈ E (G) , we have $ P_G(M, e)\neq \emptyset $ P G (M , e) ≠ ∅. The distance-edge-monitoring number of a graph G, denoted by $ \operatorname {dem}(G) $ dem ⁡ (G) , is defined as the smallest size of distance-edge-monitoring sets of G. In this paper, we study the distance edge monitoring number of pseudo wheel graphs, that is, some variants of wheel graph. [ABSTRACT FROM AUTHOR]

In this paper, we investigate an architecture for autonomous distributed control of braking and driving force on each wheel of a vehicle equipped with in-wheel motors on four wheels. Specifically, in a scenario where the driving force of some wheels is reduced due to low-friction road or motor failure during going straight ahead, the remaining wheels that can generate driving force autonomously control the yaw rate caused by the unbalanced driving force and driving force reduction to maintain the vehicle behavior. To realize autonomous distributed control, we focus on broadcast control from the control theory of multi-agent systems, which deals with the control of systems composed of many elements. We verify by simulation that the proposed architecture performs as desired for the assumed scenario. [ABSTRACT FROM AUTHOR]

The identification of Wheel/Rail Force (WRF) based on acceleration is a cheaper and convenient way to ensure safe operation. For this purpose, this paper describes an approach for the estimation of Vertical Wheel/Rail Force (VWRF) using dynamic signals from a track inspection train (TIT). Based on the vehicle dynamics theory, a VWRF estimation model is established, by which VWRF can be estimated with the measured vehicle acceleration. Rolling angle is a non-constant and hard-measured value which will affect estimation results. By deducing its relation with the difference between left and right track profiles, it can be got through the Vertical Axle Box Acceleration (VABA) and the length of the axle box. VWRF cannot be estimated only by filtering the Axle Box Acceleration (ABA). The mass participating in vibration (PVM) also has a great influence on it. With many tests and calculations, the relationship between the PVM and the wheelset mass is concluded, which made the estimation results much more accurate. The proposed method provides the basis for online high-precision estimate WRF and ensures safe operation. By comparing with a commercial WRF Inspection System, the railway application results identified that the proposed method can produce a satisfactory result. [ABSTRACT FROM AUTHOR]

Wheel profile optimisation for variable gauge high-speed trains is required to ensure good vehicle operation performance with two rail gauges, therefore, there is an urgent need to further optimise the wheel profile of variable gauge high-speed trains. This paper proposed the IRSFT-KSM-NSGA-II method to optimise a new wheel profile. Firstly, a new three-parameter improved rotary-scaling fine-tuning (IRSFT) wheel profile generation method was proposed. Then a dynamic model of a variable gauge high-speed train is established, and the clearance between the bushings is considered. The dynamic responses of the vehicle with the generated profiles are calculated and analysed. The wheel profile is optimised using the Kriging surrogate model-Non dominated sorting genetic algorithm-II (KSM-NSGA-II) algorithm. Finally, the dynamic and wear performance of the vehicle before and after the wheel optimisation is verified. The critical speed of the variable gauge high-speed trains is effectively increased, and the ride indexes and safety indexes are improved. When the optimised wheel profile LMB10opt is matched with the rail profile CHN60, the maximum wear depth of the wheel profile is reduced by 11.6%. When the LMB10opt is matched with the P65, the maximum wear depth is reduced by 9.7% and the wear is more uniform. [ABSTRACT FROM AUTHOR]

This paper presents an investigation into high-performance tire characteristics at handling limits. Analysis of the tire characteristics has been conducted using the data obtained from indoor test rigs and vehicle tests. The test rig utilised a flat track test platform to obtain tire forces with precise controllability and measurability. As for the vehicle tests, driving data of a professional driver was obtained for repeatability through a high-performance test vehicle equipped with a wheel force transducer (WFT) at each wheel and a Differential Global Positioning System (DGPS). This study is primarily focused on obtaining a deeper insight into vehicle control through an analysis of tire force characteristics at handling limits. The analysis of indoor test data utilised the Magic Formula to obtain an appropriate model capable of describing real-world tire characteristics. An optimisation-based friction ellipse has been proposed to describe the tire force limits in particular. The solution of the proposed optimisation problem allows for an envelope to be generated that accurately reflects the tire force limits. Additionally, a normalised friction circle model has been proposed to quantify tire friction usage based on the proposed friction ellipse, providing a methodology to monitor the friction use of each wheel. [ABSTRACT FROM AUTHOR]