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Adaptive event controller for fuzzy system under rudder failure: A cooperative machine learning based low cost model
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DOI：10.1016/j.fss.2026.110021.png)
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Abstract
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This paper inspects the problem of adaptive event triggered based fault tolerant control draught for fuzzy networked control systems (NCSs) under rudder failure and deception attack. Primarily, an adaptive event triggered mechanism (AETM) is infused to downscale the data packets loss and holds down the system performance. Besides, the occurrence of rudder failure depicts the distribution characteristic of partial failure and a suitable fault tolerant controller is proposed to rectify the dilemmas. The deception attacks governed by stochastic Bernoulli distribution are considered for incorporating network security. By bringing about relevant Lyapunov-Krasovskii functional (LKF) in the network of linear matrix inequalities (LMIs), the sufficient conditions are derived to ensure that the closed-loop fuzzy system guarantees the asymptotic stability. Finally, two real-time simulation models delegated tunnel diode circuit and gas turbine system substantiate the validity of the projected controller setup. The simulations also emphasize the significant improvements provided by the anticipated technique compared to previous related results in the literature. Furthermore, a cost-effective adaptive event-triggered machine learning (AETML) model is proposed to predict trigger instants using a validated machine learning (ML) model by using the data from the simulated examples.
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