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An Intelligent and Secure Control Approach for Nonlinear Systems under Attacks

This paper designs an intelligent and secure control approach based on adaptive dynamic programming for a class of nonlinear systems under the actuator attacks. The designed method can monitor and identify the attacks by an established state estimator based detector. When an attack is triggered, the control process automatically switches to a game-theoretical architecture for attack mitigation. The intelligent learning process is developed for both nominal and attack conditions without the requirement of system dynamics and attack information. Neural network techniques are applied to implement the proposed method with two critic networks and the control signals are calculated accordingly. Therefore, the designed intelligent control method can reduce the computation complexity. Simulation studies and results demonstrate the necessary of attack detection and mitigation during the learning process, and also verify the effectiveness of the developed method.