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| Main Authors: | , |
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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2509.24598 |
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| _version_ | 1866911221861580800 |
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| author | Babazadeh, Maryam Bajcinca, Naim |
| author_facet | Babazadeh, Maryam Bajcinca, Naim |
| contents | This paper presents a new method for achieving dynamic consensus in linear discrete-time homogeneous multi-agent systems (MAS) with marginally stable or unstable dynamics. The guarantee of consensus in this setting involves a set of constraints based on the graph's spectral properties, complicating the design of the coupling gains. This challenge intensifies for large-scale systems with diverse graph Laplacian spectra. The proposed approach reformulates the dynamic consensus problem with a prescribed convergence rate using a state-action value function framework inspired by optimal control theory. Specifically, a synthetic linear quadratic regulation (LQR) formulation is introduced to encode the consensus objective, enabling its translation into a convex semidefinite programming (SDP) problem. The resulting SDP is applicable in both model-based and model-free settings for jointly designing the local feedback and coupling gains. To handle the inherent non-convex feasibility conditions, a convex-concave decomposition strategy is employed. Adaptation of the method in a completely model-free set-up eliminates the need for system identification or knowledge of the agents' dynamics. Instead, it relies on input-state data collection and offers an entirely data-driven equivalent SDP formulation. Finally, a new algorithm balancing feasibility, convergence rate, robustness, and energy efficiency, is established to provide design flexibility. Numerical simulations validate the method's effectiveness in various scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_24598 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Model-Free Dynamic Consensus in Multi-Agent Systems: A Q-Function Perspective Babazadeh, Maryam Bajcinca, Naim Systems and Control This paper presents a new method for achieving dynamic consensus in linear discrete-time homogeneous multi-agent systems (MAS) with marginally stable or unstable dynamics. The guarantee of consensus in this setting involves a set of constraints based on the graph's spectral properties, complicating the design of the coupling gains. This challenge intensifies for large-scale systems with diverse graph Laplacian spectra. The proposed approach reformulates the dynamic consensus problem with a prescribed convergence rate using a state-action value function framework inspired by optimal control theory. Specifically, a synthetic linear quadratic regulation (LQR) formulation is introduced to encode the consensus objective, enabling its translation into a convex semidefinite programming (SDP) problem. The resulting SDP is applicable in both model-based and model-free settings for jointly designing the local feedback and coupling gains. To handle the inherent non-convex feasibility conditions, a convex-concave decomposition strategy is employed. Adaptation of the method in a completely model-free set-up eliminates the need for system identification or knowledge of the agents' dynamics. Instead, it relies on input-state data collection and offers an entirely data-driven equivalent SDP formulation. Finally, a new algorithm balancing feasibility, convergence rate, robustness, and energy efficiency, is established to provide design flexibility. Numerical simulations validate the method's effectiveness in various scenarios. |
| title | Model-Free Dynamic Consensus in Multi-Agent Systems: A Q-Function Perspective |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2509.24598 |