A methodology for designing congestion controllers, based on active queue management (AQM), is presented here. The congestion control law is derived using sampled-data H ∞ systems theory. More precisely, a sampled-data state feedback that guarantees the stability of the closed-loop system and satisfies a H ∞ disturbance attenuation level is derived here, based on sufficient conditions expressed in terms of linear matrix inequalities. The effectiveness of the developed technique is validated on two examples. Keywords AQM • Network congestion control • Sampled-data controller • Saturating input 1 Introduction Network congestion in communication networks is being paid significant attention because of their wide range of applications, from Web servers to industrial control systems. In order to manage network congestion, active queue management (AQM) (Braden et al. 1998) techniques are frequently used, which aim to reduce packet drops and improve the overall network utilization. One of the most well-known AQM policies are random early detection (RED) (Floyd and Jacobson 1993), adaptive RED (ARED) (La et al. 2003) and nonlinear RED (NLRED) (Rastogi and Zaheer 2016). Moreover, the fundamentals of control theory have been used to analyze new AQM schemes, such as proportional integral (PI) (Hollot et al. 2002), proportional integral derivative (PID) (Zhang and Papachristodoulou 2014), adaptive PI (Zhang et al. 2003) and PI enhanced (PIE) (Pan et al. 2013). Also, in Bender (2013) it has proposed the use of a dynamic anti-windup gain matrix to improve the performance of a designed controller used to AQM in congested TCP/IP routers, and it was developed in El Fezazi et al.