Data-Driven Output-Feedback LQ Secure Control for Unknown Cyber-Physical Systems Against Sparse Actuator Attacks

This article investigates the secure control problem of the unknown cyber-physical systems (CPSs) with the sparse actuator attacks. First, a data-driven residual generator design method via a full-rank transformation matrix is given to detect the actuator attacks. Based on the detection mechanism, an approximate dynamic programming (ADP) approach within the output-feedback framework is then developed to solve the optimal secure control problem, where a model-dependent value function is presented by combining adaptive self-organizing map neural network and clustering technology. By using the iterative learning algorithm and designing an appropriate neural network weight update law, it is shown that the secure control strategy can guarantee the stability of the closed-loop system regardless of whether the attack occurs or not and mitigate the performance loss. Finally, a numerical simulation example and a DC motor system are used to verify the effectiveness of the proposed secure control method.