Fault-tolerant switched fuzzy sliding mode control for path tracking of connected and automated vehicles under edge-cloud architecture

This paper proposes a fault-tolerant switched fuzzy sliding mode control strategy to solve the path-tracking problem of connected and automated vehicles under an edge–cloud architecture. The edge–cloud platform collects real-time environmental and traffic data to overcome the limitations of conventional controllers that rely solely on local vehicle states. To improve adaptability to longitudinal speed variations, a Takagi–Sugeno fuzzy model is established with speed as the premise variable. An observer with mismatching premise variables is developed to compensate for estimation errors caused by cloud communication delays. In addition, an adaptive event-triggered mechanism is introduced to reduce communication load while maintaining real-time control performance. Based on a piecewise Lyapunov–Krasovskii functional, a fault-tolerant switched fuzzy sliding mode controller is constructed to ensure exponential stability and guaranteed <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>H</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>∞</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> performance under both denial-of-service attacks and actuator faults. Simulation results validate the effectiveness and robustness of the proposed approach.