Discretized Distributed Optimization Control for Second-Order Nonlinear Multiagent Systems With Uncertainty

doi:10.1109/TAC.2025.3629507

Discretized Distributed Optimization Control for Second-Order Nonlinear Multiagent Systems With Uncertainty

2025-11-05

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DOI：10.1109/TAC.2025.3629507

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This article addresses discretized distributed optimization control algorithm for continuous-time multiagent systems with unmodeled nonlinear dynamics and external disturbances. A novel sampled-data-based uncertainty compensation distributed feedback control strategy is proposed to drive the outputs of all agents toward the optimizer of the global cost function. The proposed controller is capable of compensating for uncertainties by accurately estimating their values at specific moments within each sampling interval. Theoretical analysis confirms that the errors between the system outputs and the optimizer can ultimately be reduced to an arbitrarily small value when the sampling period is sufficiently small. Simulation results validate the effectiveness of the proposed algorithms.

Keywords:

Distributed optimization

disturbance

multiagent systems (MAS)

nonlinearity

sampled-data control

Discretized Distributed Optimization Control for Second-Order Nonlinear Multiagent Systems With Uncertainty

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