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.

Manipulators have a wide range of applications in industrial automation. However, their nonlinear characteristics, time-varying properties, and external disturbances present significant challenges in accurately tracking their trajectories. This paper proposes an integrated control strategy based on prescribed-time convergence control, output constraint control, prescribed performance control (PPC), and an extended state observer (ESO)-based anti-disturbance control method. The prescribed-time convergence control guarantees that the system will reach a steady state at a specified time, while the output constraint control ensures that the Vm will move within a predefined physical range. The PPC meets the rigorous requirements of error convergence during trajectory tracking by regulating the error dynamics, while the ESO is employed to estimate unknown disturbances and enhance the system’s resilience to interference. The simulation outcomes demonstrate that the proposed control methodology exhibits notable advantages in terms of a rapid response, precision tracking, and anti-disturbance capabilities.