IEEE Transactions on Industrial Informatics · 2021 · 21 citations · 36 references
ComputingComputer ScienceSystems EngineeringComputer SecurityCyber-physical SystemsNetwork SecurityAdaptive ControlControl System DesignControl TheoryCyber-physical SecurityPower ControlAdaptive Control Of Nonlinear SystemsAdaptive Neural ControlControl System SecurityNetworked Control SystemsNetworked Nonlinear ControlNonlinear State EstimationRemote Control SystemsSecure Control Systems
In this article, we concentrate on a class of networked control systems (NCSs) under the unknown nonlinear attacks, which target the sensors and actuators with remote communication channels. To attenuate the attacks remotely, the remote secure control scheme based on the adaptive dynamic programming (ADP) is proposed. First, the dynamic process of NCSs with nonlinear sensors and actuators attacks is analyzed and an adaptive remote estimator is designed to obtain the state information of the NCSs from a long distance, which is the key to achieve the purpose of remote control. Moreover, to compensate for the unknown nonlinear attacks, the adaptive law of the remote estimator is presented based on recursive least square. Furthermore, we consider the influence of the estimation errors, the minimax control theory is applied to improve the design of the policy iteration algorithm, and the ADP-based remote secure control strategy is proposed to rely on the remote estimation information. Then, the neural networks are built to implement the online algorithm. Finally, results’ analysis illustrates on two networked power systems the effectiveness of the proposed method.
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