This paper investigated resilient containment control ( $\mathcal{C}\mathcal{C}$)-based consensus in fractional-order multi-agent systems ( $\mathcal{F}\mathcal{O}\mathcal{M}\mathcal{A}\mathcal{S}s$) subject to parametric uncertainties, communication time delays, and external disturbances. A non-fragile ( $\mathcal{N}\mathcal{F}$) distributed control protocol was proposed to accommodate controller perturbations and delay variations simultaneously. By integrating fractional calculus, algebraic graph theory, and an improved Razumikhin technique, we derived concise algebraic conditions ensuring all followers asymptotically converge to the convex hull that the leaders form under worst-case uncertainties. The results cover non-delayed and delayed cases and are expressed as simple, verifiable matrix inequalities. At the end, we provide examples to demonstrate the feasibility of the proposed method, including a numerical case study, and we illustrate the applicability of the developed theoretical results through designing a controller for electronic circuits.

This paper proposes an adaptive torque control and state estimation method that effectively addresses the trade-off between fatigue load reduction and power generation performance in a nonlinear wind energy conversion system subject to strong turbulence, measurement noise, and model uncertainties. The proposed fatigue load-aware adaptive torque controller dynamically adjusts control weights based on the variability, mismatch, and standard deviation of the aerodynamic torque, thereby reducing drivetrain fatigue loads while minimizing power loss. In addition, to accurately estimate the highly nonlinear and unknown aerodynamic torque, a combined estimator based on SMO and UKF was designed. SMO provides robustness against disturbances and noise, while the UKF enhances estimation accuracy in nonlinear systems, enabling reliable aerodynamic torque estimation. To validate the performance, simulations were conducted under three different turbulence intensities and wind speed profiles. Simulation results demonstrated that the proposed SMO-UKF estimator achieves up to a 2.77% improvement in estimation performance compared to conventional methods. Furthermore, the proposed control strategy reduces fatigue loads by up to 15.52% with negligible reduction in power output.