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This paper addresses the design problem of false data injection (FDI) attacks against the output tracking control of networked systems, where the network-induced delays in the feedback and forward channels are considered. The main contributions of this paper are as follows. 1) To actively compensate for the two-channel network-induced delays, a Kalman filter-based networked predictive control scheme is designed for stochastic linear discrete-time systems. 2) From an attacker’s perspective, stealthy FDI attacks are proposed for both the feedback and forward channels so as to disrupt the stability of the resulting closed-loop system while avoiding the detection of a Kalman filter-based attack detector. 3) Both numerical simulations and practical experiments are carried out to show the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

This paper addresses the data-based networked control problem for a class of nonlinear systems. Network communication constraints, such as network-induced delay, packet disorder, and packet dropout in both the feedback and forward channels, are considered and further treated as the round-trip time (RTT) delay that is redefined. By using the packet-based transmission mechanism and the model-free adaptive control algorithm, a data-based networked predictive control method is proposed to actively compensate for the random RTT delay. The proposed method does not require any information on the plant model and depends only on the input and output data of the plant. A simple and explicit sufficient condition, which is related to the upper bound of the RTT delays, is derived for the stability of the closed-loop system. Additionally, a zero steady-state output tracking error can be achieved for a step reference input. The effectiveness of the proposed method is demonstrated via simulation and experimental results. [ABSTRACT FROM PUBLISHER]