Distributed Secure Learning Control for Large-scale Multirobots under Stealthy Actuator Attacks

Xinglong Zhang

doi:10.1109/tro.2026.3737565

Distributed Secure Learning Control for Large-scale Multirobots under Stealthy Actuator Attacks

2026-09-24

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DOI：10.1109/tro.2026.3737565

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Distributed learning control for multirobot systems (MRS) offers significant flexibility in the presence of uncertainties but lacks provable performance guarantees. A promising direction involves integrating reinforcement learning (RL) into distributed model predictive control (DMPC), leveraging the strengths of RL in nonlinear policy design and the receding-horizon replanning capabilities of DMPC. However, ensuring secure control within such a learning framework under malicious cyber attacks, particularly stealthy ones, remains a critical challenge, because the distributed policies generation depends on information exchange among neighbors, where compromised agents can rapidly influence the behavior of others through the communication network. This article proposes a distributed secure learning control (DSLC) framework for large-scale MRS under malicious, stealthy actuator attacks. Our framework offers two key features: (i) a unified approach that enables secure learning control across various coordination scenarios, including general formation, affine formation, and containment; and (ii) a game-theoretic distributed learning-based predictive control strategy that learns how to balance the attacker and defender through a differential-game based DMPC framework. Specifically, DSLC employs a distributed attacker-actor-critic architecture to learn the optimal defense and attack policies online within each prediction interval. Unlike numerical optimization-based controllers that calculate open-loop control sequences, our method simultaneously generates adversarial attack policies and corresponding defense policies in analytical closed-loop form. The defense policies could be generalized to MRS with varying scales and diverse actuator attack probabilities, enabling rapid and secure control deployment in large-scale MRS. The effectiveness and scalability of DSLC are validated experimentally on multiple wheeled robots via various control tasks.

Keywords:

Multirobots

malicious attacks

differential game

distributed model predictive control

learning control

Distributed Secure Learning Control for Large-scale Multirobots under Stealthy Actuator Attacks

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