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<p>This paper proposes a distributed approach to the optimal consensus control problem for discrete-time multi-agent systems. The objective is to steer the states of all agents toward a common agreement while explicitly accounting for the individual linear dynamics of each agent. Following a model predictive control (MPC) framework, both the consensus state and the associated control input sequence over a finite prediction horizon are jointly optimized at each sampling instant. The resulting optimization problem is solved using a proximal Alternating Direction Method of Multipliers (ADMM), which guarantees exponential convergence under local communication constraints among agents. To address bandwidth limitations inherent in multi-agent networks, the ADMM-based iterative updates are further combined with a quantization scheme. Numerical comparisons are presented to demonstrate the effectiveness and performance of the proposed distributed control algorithm.</p>

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Quantized Distributed Linear Quadratic Optimal Consensus of Discrete-Time Multi-agent Systems

  • 期刊论文

  • 发表日期：

    2026年7月14日

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  • Viet Hoang Pham,
  • Quoc Van Tran

摘要

This paper proposes a distributed approach to the optimal consensus control problem for discrete-time multi-agent systems. The objective is to steer the states of all agents toward a common agreement while explicitly accounting for the individual linear dynamics of each agent. Following a model predictive control (MPC) framework, both the consensus state and the associated control input sequence over a finite prediction horizon are jointly optimized at each sampling instant. The resulting optimization problem is solved using a proximal Alternating Direction Method of Multipliers (ADMM), which guarantees exponential convergence under local communication constraints among agents. To address bandwidth limitations inherent in multi-agent networks, the ADMM-based iterative updates are further combined with a quantization scheme. Numerical comparisons are presented to demonstrate the effectiveness and performance of the proposed distributed control algorithm.