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Feedforward information fusion-based data-driven fault estimation for fuzzy systems: Periodic-sampling event-triggered transmission

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  • Wang, Hong-Jun
  • Huang, Sheng-Juan

Abstract

This work is devoted to the data-driven remote fault estimation (FE) problem for Takagi–Sugeno (T–S) fuzzy systems with faults and disturbances. Compared with the existing data-driven FE methods, the proposed one embeds a periodic-sampling event-triggered data transmission strategy that can alleviate network congestion and save communication resources. Furthermore, distinct from conventional iterative FE techniques, a feedforward information fusion-based iterative data-driven FE approach is proposed, which can effectively suppress the phenomenon of potential iterative mutation. The designed feedforward information fusion-based iterative estimators, embedded with a developed dual-state projection fault transformation, are driven by a continuous and smooth fitting signal, so as to realize the data-driven remote FE of the fuzzy systems. Here, the fitting signal is derived from the transmitted data through a piecewise cubic Hermite interpolating polynomial fitting method. The stability conditions with multiple relaxation matrix factors for the estimation error system are formulated in the form of linear matrix inequalities (LMIs). Two examples are provided to support the proposed method.

Suggested Citation

  • Wang, Hong-Jun & Huang, Sheng-Juan, 2026. "Feedforward information fusion-based data-driven fault estimation for fuzzy systems: Periodic-sampling event-triggered transmission," Chaos, Solitons & Fractals, Elsevier, vol. 208(P3).
    • Handle: RePEc:eee:chsofr:v:208:y:2026:i:p3:s0960077926004388
    DOI: 10.1016/j.chaos.2026.118297
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