Sampled-Data Leader-Following Exponential Practical Consensus for Multiple Uncertain Euler-Lagrange Systems

doi:10.1109/tase.2026.3736709

Sampled-Data Leader-Following Exponential Practical Consensus for Multiple Uncertain Euler-Lagrange Systems

2026-09-22

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DOI：10.1109/tase.2026.3736709

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In a sampled-data framework, leader-following exponential practical consensus is considered for a collection of Euler-Lagrange systems subject to uncertainties. First, a distributed sampled-data dynamic compensator is developed based on an internal model design to generate the steady-state information needed for consensus. Second, by incorporating adaptive control technology, an additional sampled-data dynamic compensator is developed to address the unknown parameters of the Euler-Lagrange dynamics. Third, exploiting the above two compensators, we formulate a distributed sampled-data controller that guarantees practical consensus under directed networks and is compatible with digital realization. In particular, appropriate tuning of the design parameters enables an arbitrarily small steady-state consensus error and an arbitrarily fast convergence rate. Moreover, the relationship between the sampling interval and the system’s initial conditions and parameters is explicitly established. Comparative numerical simulations using heterogeneous two-link robotic manipulators demonstrate competitive tracking accuracy and control effort under the stricter information condition. Moreover, MuJoCo rigid-body simulations incorporating joint friction, sampled measurement noise, and actuator saturation provide further evidence of implementation feasibility and empirical robustness.

Keywords:

Consensus control

sampled-data control

internal model

Euler-Lagrange systems

adaptive technology

Sampled-Data Leader-Following Exponential Practical Consensus for Multiple Uncertain Euler-Lagrange Systems

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