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<p>In this paper, we propose a novel finite-time optimization formation control framework for networked robots, which integrates a finite-time optimal estimator (FOE) with a finite-time local controller (FLC). Within this architecture, each robot is assigned a cost function to ensure optimal performance while minimizing energy consumption. The FOE employs a distributed optimization formation control protocol to compute the optimal positions in finite-time, effectively mitigating the influence of disturbances and uncertainty. The FLC enables the actual position for each robot to track the estimated signal generated by the FOE, thereby achieving coordinated formation control. A Lyapunov-based stability analysis is conducted to derive sufficient conditions ensuring the finite-time convergence and stability of the proposed scheme. Simulation results validate the effectiveness and robustness of the proposed control strategy.</p>

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Finite-time distributed optimization formation control of networked robots with time-varying reference signals under directed graphs

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月29日

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  • Weicheng Zhao,
  • Qian Chen,
  • Li Ding

摘要

In this paper, we propose a novel finite-time optimization formation control framework for networked robots, which integrates a finite-time optimal estimator (FOE) with a finite-time local controller (FLC). Within this architecture, each robot is assigned a cost function to ensure optimal performance while minimizing energy consumption. The FOE employs a distributed optimization formation control protocol to compute the optimal positions in finite-time, effectively mitigating the influence of disturbances and uncertainty. The FLC enables the actual position for each robot to track the estimated signal generated by the FOE, thereby achieving coordinated formation control. A Lyapunov-based stability analysis is conducted to derive sufficient conditions ensuring the finite-time convergence and stability of the proposed scheme. Simulation results validate the effectiveness and robustness of the proposed control strategy.