Fuzzy Adaptive Data-Driven Security Control for Multiple-Unit High-Speed Train With Dynamic Sensor Attacks

This paper studies a multiple-input multiple-output fuzzy adaptive data-driven security control issue for multiple-unit high-speed train (HST) with actuator faults and dynamic sensor attacks. Firstly, the ideal controller of HST is modified to an equivalent linear data model by utilizing the dynamic linearization method. Then, a novel partial form dynamic linearization controller-based model-free adaptive control framework is designed. To alleviate the impact of actuator faults, the unknown fault-related uncertainties are approximated by employing a fuzzy logic system (FLS), and the fuzzy weight is estimated by introducing a parameter estimation criterion function. Furthermore, a fixed threshold sensor attack detection mechanism is introduced to detect and isolate compromised sensors by predicting the speed of the train in the next time interval. Based on the prediction algorithm, an improved data fusion strategy is developed to compensate for the impacts of sensor attacks, which can remove the requirement of the number of attacked sensors while allowing the system to maintain robust performance under various attack conditions. It is shown that the proposed algorithm is effective in the sense of the generic norm, thus ensuring reliable and safe operation for the multiple-unit HST. Eventually, simulation examples are provided to confirm the proposed protocol.