Chen Zhang

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 investigates connectivity-preserving formation control for high-order multi-agent systems under switching distance-dependent topologies, actuator faults and external disturbances. The admissible interaction edges are selected by a switching signal, while the actual edge weights vary continuously with inter-agent distances. To avoid unsafe jumps of algebraic connectivity at switching instants, a connectivity-certified switching condition is introduced. Between switching instants, an algebraic-connectivity-dependent barrier signal is incorporated into a recursive formation controller. A fixed-time extended state observer is designed to estimate the matched lumped uncertainty caused by actuator loss of effectiveness, additive faults and external disturbances. Explicit ultimate estimation-error bounds and a fixed convergence-time upper bound are derived. The effects of command filtering and smooth actuator saturation used in the numerical implementation are further represented by bounded implementation residuals in the stability analysis. Theoretical results establish boundedness of all closed-loop signals, uniform ultimate boundedness of formation errors and preservation of a prescribed connectivity margin under the stated hybrid safety conditions. Numerical simulations with five planar followers illustrate formation tracking, connectivity preservation, fault-tolerant disturbance rejection and observer estimation performance.

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 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.

This paper proposes a modular control framework for high-order nonlinear multi-agent systems (MASs) to achieve distributed finite-time formation tracking with a prescribed performance. The design integrates two modules to address uncertainties and safety constraints simultaneously. Module I—Prescribed Performance-Based Trajectory Generation: A virtual signal generator constructs collision/connectivity-aware reference trajectories by encoding time-varying performance bounds into formation errors. It ensures network rigidity and optimal formation convergence through dynamic error transformation. Module II—Anti-disturbance Tracking Control: A finite-time extended state observer (FTESO) estimates and compensates for uncertainties within a finite time, while a time-varying surface controller drives tracking errors into predefined performance funnels. This module guarantees rapid error convergence without violating the transient constraints from Module I. The simulations verified the accelerated formation reconfiguration under disturbances, and thus, demonstrated improved robustness and convergence over asymptotic approaches. The framework offers a systematic solution for safety-critical MAS coordination with heterogeneous high-order dynamics.