Dynamic-Gain-Based Tunable Transient Performance for Unknown Multi-Agent Systems Under DoS Attacks and Disturbances

Abstract

A resilient control scheme with tunable transient performances is proposed for multi-agent systems subjected to DoS attacks and external disturbances. A novel sufficiently differentiable distributed filter is introduced to eliminate control input singularities caused by differentiating the reference output signal, with relaxed parameter limitations compared to existing filters. Then, a dynamic control gain strategy, consisting of a smooth gain elevation phase and an online gain adaptation phase, is developed to achieve tunable transient performance. The smooth gain elevation phase coordinates the settling time by initially assigning a low convergence rate and increasing it as the agents approach the desired states. Additionally, this property also suppresses the initial control input amplitude, thereby preventing agents from going out of control due to excessively high control inputs. The online gain adaptation phase enhances robustness against external disturbances by rapidly and smoothly increasing the control gain for affected agents. The dynamic gain is designed to be sufficiently differentiable to avoid singularities in control inputs resulting from differentiating virtual controllers. Finally, simulation results on both numerical and tower crane multi-agent systems, compared with three existing methods, validate the effectiveness of the proposed control scheme. Note to Practitioners—Multi-agent systems, such as autonomous vehicles, robotics, and smart grids, are vulnerable to cyberattacks and disturbances, which can destabilize conventional controllers. Traditional controllers usually select fixed parameters by trial and error and produce large initial control signals. This work proposes a resilient control scheme that applies a smooth and dynamic parameter coordination strategy to suppress excessively high control signals, adjust settling time, and adapt to cyberattacks and external disturbances. Simulation studies, including a numerical multi-agent system and a tower crane multi-agent system, show that this approach improves both stability and resilience compared with existing methods, offering a practical method for safer and more robust control in multi-agent systems.