Open Access

ARTICLE

Disturbed Dynamic Analysis and Robust Decision-Making Control of Complex Networks

Xiusen Wang^1,*, Zheng Fang², Jie Chen^2,*

1 The School of Mathematics and Statistics, Ningbo University, Ningbo, China
2 The College of Electronic Engineering, National University of Defense Technology, Hefei, China

* Corresponding Authors: Xiusen Wang. Email: ; Jie Chen. Email:

(This article belongs to the Special Issue: Dynamics, Control and Optimization in Complex Networks)

Computers, Materials & Continua 2026, 89(2), 86 https://doi.org/10.32604/cmc.2026.088952

Received 12 July 2026; Accepted 18 August 2026; Issue published 15 September 2026

Abstract

Complex networks in cyber–physical, transportation, and information infrastructures operate under topology variations, unmeasured disturbances, and limited actuation. This paper proposes robust disturbance-aware data-driven decision control (R-D3C), which couples a sliding-window graph-regularized estimator, disturbance-envelope adaptation, sparse intervention allocation, receding-horizon optimization, and a robust safety projection. The theory directly bounds the dynamic prediction regret of the implemented sliding-window estimator. A checkable sufficient condition for safety-filter feasibility is coupled with an explicit slack-and-backup fallback when the strict projection is infeasible. Practical input-to-state stability and sparse-allocation risk reduction are established. The nominal comparison uses 30 paired runs with standard deviations and significance tests, while the separate deployment-impairment benchmark uses 20 runs; evaluation also includes the IEEE 39-bus topology and timing tests up to 1000 nodes. R-D3C achieves the lowest constraint-violation rate; its residual error is statistically comparable with adaptive MPC while preserving an explicit safety layer.

---

Keywords

---