This paper investigates distributed optimization-assisted output consensus for nonlinear multi-agent systems with mutually incommensurate Caputo orders, unavailable velocity-like states, bounded disturbances, measurement noise, and actuator saturation. A mixed-power exact penalty flow generates practical optimal references from local costs and intermittent neighbor broadcasts. The penalty gain and a smoothing bias bound are determined from a public interval, topology information, and certified local gradient data without prior knowledge of the aggregate optimizer. An autonomous decaying threshold provides event-triggered communication, an initial condition-independent fixed-time practical certificate for the integer-order optimizer, and exclusion of finite-time event accumulation. The physical layer is analyzed with established Caputo quadratic inequalities and agentwise Mittag–Leffler comparison. Fractional reference and command filters, a composite observer, and two-gain anti-saturation compensation form the output feedback controller, while the physical result is formulated as a finite-horizon regional verification certificate. Numerical studies include same-model and communication budget comparisons, a recent method-inspired optimizer benchmark, certificate tightening, and robustness tests for initialization, the fractional order, measurement noise, and the integration step size.

This paper addresses the tracking control problem of robotic manipulators under a universal symmetry constraint framework in the presence of lumped uncertainties and external disturbances. Unlike conventional constrained control schemes that treat tracking error bounds and state bounds separately, the proposed method explicitly exploits the symmetric structure of the prescribed constraints and formulates both tracking error constraints and full-state constraints in a unified manner. Based on the Euler–Lagrange dynamics of robotic manipulators, a universal symmetry constraint transformation is introduced to convert the original constrained system into an equivalent unconstrained form while preserving the intrinsic symmetry of the admissible sets. To enhance robustness against uncertainties and disturbances, a sliding-mode extended state observer is designed to estimate the total disturbance online. Meanwhile, a tracking differentiator is incorporated into the recursive design to avoid repeated differentiation of virtual control signals. On this basis, a disturbance-compensated backstepping controller is developed for the transformed manipulator system. It is shown that all closed-loop signals remain bounded, the prescribed symmetric tracking error and state constraints are never violated, and the tracking error converges asymptotically when the observer and differentiator errors vanish asymptotically. Simulation results obtained from a robotic manipulator verify the effectiveness of the proposed control strategy.

Construction sites are complex environments, and unsafe behaviors by workers, such as not wearing safety helmets or reflective vests, can easily lead to accidents. When using target detection technology to detect unsafe behaviors, the results are often unsatisfactory due to the complexity of the background and the small size of the targets. This paper proposes an unsafe behavior detection algorithm based on dual adaptive feature fusion. The algorithm is based on YOLOv5, introducing a front-end adaptive feature fusion module (FE-AFFM) at the head of the backbone network for deep data processing, improving the model’s feature extraction capability in complex backgrounds. Simultaneously, a back-end adaptive feature fusion module (BE-AFFM) is introduced at the tail of the network to strengthen feature fusion. In the experimental verification phase, this paper selects a self-made laboratory dataset and verifies the effectiveness of the improved algorithm through ablation experiments, algorithm comparisons, and heatmap analysis. The average accuracy of the improved algorithm is 3.6% higher than the baseline model, and the detection effect on small targets is significantly improved, meeting the actual needs of construction sites. This paper also selects the publicly available dataset SHWD for algorithm comparison experiments. The results show that the improved algorithm still has a significant advantage over mainstream algorithms, verifying the generalization ability of the improved model.

This paper studies resilient safe distributed optimization with practical prescribed-time performance for fractional-order multi-agent systems under false data injection attacks. Caputo dynamics are used to represent memory-dependent agent behavior. A local trimming mechanism is combined with gradient feedback, auxiliary coordination, a bounded time-varying gain, and a barrier-type safety correction. In contrast to an ideal singular prescribed-time design, the proposed bounded gain drives the normal-agent errors into an explicitly characterized neighborhood of the global optimizer at the assigned time, provided that a verifiable gain matrix is positive definite. Explicit residual bounds are derived in terms of the attack amplitudes, the trimming level, and the retained-neighbor redundancy. A fractional extremum argument is further used to establish forward invariance of the safe interval. The analysis proves boundedness of the physically relevant closed-loop signals, safety preservation, practical prescribed-time convergence, and robustness against bounded disturbances. Simulations on a sparse six-agent network demonstrate the roles of resilient information processing and barrier correction without requiring all-to-all communication.

This paper investigates robust output-feedback consensus of linear fractional-order multi-agent systems under external disturbances and communication constraints. A fully distributed framework is developed by integrating local dynamic observers, fractional adaptive edge couplings, and dynamic event-triggered communication. The resulting protocol requires neither the network size nor algebraic connectivity. An explicit locally verifiable triggering condition is derived directly from the weighted broadcast-error term, while positivity of the fractional internal variable and Hölder continuity of Caputo trajectories are used to exclude Zeno behavior. A three-channel fractional bounded-real analysis characterizes the consensus, observer-to-coupling, and observer-to-output mappings and yields a generalized $H_{\infty }$ attenuation bound for the actual plant disagreement. In a six-agent benchmark, the proposed trigger requires 366 transmissions, compared with 3164 for a static trigger and 6000 for periodic communication, corresponding to reductions of $88.43\%$ and $93.90\%$, respectively, with comparable consensus accuracy. Finite-energy disturbance tests, attenuation-certificate analysis, numerical sensitivity studies, and a fractional servo synchronization example further demonstrate the effectiveness and applicability of the proposed method.