Fuzzy Logic-Based Prescribed-Time Adaptive Intelligent Tracking Control for Uncertain Nonlinear Systems With Deception Attacks: A Novel Stability Criterion

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In this paper, the challenging problem of prescribed-time (PT) adaptive intelligent tracking control is investigated for the nonlinear uncertain systems with external disturbance under sensor deception attacks. Firstly, since the existence of sensor deception attacks, the states and output of the systems in the network channel are unavailable. To compensate for the attack gains and achieve PT tracking control under deception attacks, we substitute the gains generated by deception attacks and the PT adjustment function into the designed coordinate transformation, and apply the Nussbaum function technique to process the attack gains in the controller design process. Secondly, based on the cited definition of the PT adjustment function, a novel stability criterion is proposed and proven. Then, utilizing the available compromised states, the corresponding PT adaptive intelligent tracking controller is constructed using backstepping method, and the fuzzy logic system (FLS) is applied in the design process to approximate the unknown continuous function, avoiding the “complexity explosion” problem caused by repeated differentiation and thus reducing the computational burden to a certain extent. Further, according to the proposed stability lemma, it is demonstrated that the designed control strategy is capable of enabling the systems output to achieve zero-error tracking within the prescribed time considering external disturbances and deception attacks. At last, the effectiveness of the designed control scheme is illustrated by MATLAB simulation results.

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