Velocity‐Based Gain‐Scheduling Path Following Mechanism for Autonomous Electric Vehicles With Stochastic Actuator Failures and Deception Attacks

ABSTRACT This article proposes a velocity‐based gain‐scheduling path following mechanism for autonomous electric vehicles (AEVs) that are susceptible to semi‐Markov jump actuator failures and stochastic deception attacks. First, to address the uncertainties associated with tire dynamics and the time‐varying longitudinal speed, a linear parameter varying (LPV) approach is adopted to approximate the nonlinear vehicle dynamics. Second, a semi‐Markov jump model is utilized to describe the stochastic occurrence of actuator failures, which is more general than some existing failure models. Third, in consideration of the constraints imposed by limited communication bandwidth and stochastic cyber‐attacks within the transmission network, a reliable adaptive event‐triggered mechanism with two adjustable parameters is constructed and a Bernoulli distribution is used to describe whether AEVs are under deception attack signals. Furthermore, based on the Lyapunov functional and Bessel–Legendre integral inequalities, some sufficient conditions to ensure the AEVs is stochastically stable with performance are derived. Finally, the correlative simulation is carried out to demonstrate the effectiveness of the developed method.