Adaptive output-feedback resilient tracking control using virtual closed-loop reference model for cyber-physical systems with false data injection attacks

This work studies output-feedback resilient tracking control problems in cyber-physical systems (CPSs) with false data injection attacks via closed-loop model-reference adaptive control (MRAC) techniques. The control input signals in CPSs, directly and indirectly influenced by sensor and actuator false data injection attacks, could degrade the system's control performance seriously. Then a virtual closed-loop reference model is used as a mediator between the open-loop reference and actual systems to improve the ability to suppress attacks, and by the MRAC techniques, a novel adaptive output-feedback resilient tracking control scheme is proposed to ensure the reliability of the attacked systems. Different from the existing results, (a) the new one is with an adaptive attack compensator in a time-varying gain output-feedback form, which can automatically eliminate the impact of matched attacks by online adjusting the gain; and (b) the L-2-gain rejection property of mismatched attacks is achieved by the virtual closed-loop reference. Finally, an illustrative example validates the developed method.