Learning-Based Adaptive Fuzzy Output Feedback Control for MIMO Nonlinear Systems With Deception Attacks and Input Saturation
Ning Zhao, Yongjie Tian, Huiyan Zhang, Enrique Herrera‐Viedma
Abstract
This article proposes an adaptive fuzzy dual-channel event-triggered output feedback control approach for a class of multi-input-multi-output (MIMO) systems with deception attacks and input saturation. Due to the consideration of two pivotal factors simultaneously, including deception attacks and input saturation, the existing methods are difficult to be directly applied. To this end, a novel fuzzy state observer and an auxiliary system are constructed to address unavailable impaired system states and input saturation, respectively. Furthermore, by constructing a new transformation of coordinate and employing adaptive fuzzy technique and single parameter learning approach, the sensor deception attacks, fuzzy weight and external disturbance are reconstructed online into linear composite uncertain terms with single parameter under the framework of backstepping and dynamic surface design. Additionally, the communication and computation burden is significantly reduced by using fewer single-parameter adaptive laws and dual-channel event-triggered strategy (DCETS). The proposed control method guarantees that all signals within the closed-loop system are bounded. Meanwhile, the Zeno behavior is avoided. Finally, a simulation example is provided to verify the availability of the presented approach.