Data-driven prescribed performance platooning sliding mode control under DoS attacks

In this paper, a prescribed performance model-free adaptive platooning sliding mode control (PP-MFAP-SMC) problem for the nonlinear vehicular platooning systems (VPSs) under denial-of-service (DoS) attacks is studied. Firstly, the partial form dynamic linearization (PFDL) technique is employed to convert the nonlinear VPSs into an equivalent linear data model, in which the nonlinear features of the VPSs are compressed into an unknown time-varying pseudo gradient (PG) vector. Then, an observer is devised to acquire the estimation value of the unknown time-varying PG vector. To lower the complication of the design, the constrained tracking error is converted into the unconstrained one. Based on which, the sliding mode control (SMC) strategy is proposed to enhance the robustness of the VPSs. Further, a PP-MFAP-SMC algorithm with an attack compensation mechanism is developed to ensure that the vehicular tracking errors of the position and velocity can converge to the predefined regions, respectively. Eventually, the effectiveness of the developed algorithm is demonstrated by an actual VPS with the comparisons.