A novel AQM algorithm based on feedforward model predictive control

Abstract

Summary Developing feedforward model predictive controller as an active queue management (AQM) scheme is studied in this paper. MPC is an advanced control strategy for AQM. However, the conventional MPC is usually an implementable form of feedback MPC. In this paper, a feedforward and feedback optimal control law is presented. It is a clean, easily implementable, version of model predictive control that incorporates feedforward. Firstly, we use the nominal fluid model to design the feedforward control input so that the output tracks the given queue length with small error. Furthermore, in order to achieve robust performance and to reject the (unmeasured) disturbance, the feedback component is designed. In particular, a disturbance observer is incorporated into the prediction output in standard feedback MPC. This framework can significantly improve performance in the presence of measurement noise and certain types of model uncertainty. Finally, the simulation results show the effectiveness of FF‐AQM algorithm.

Performance Analysis of Active Queue Management Algorithm Based on Reinforcement Learning

“…Many AQM control algorithms have been developed under different control strategies, namely, proportional-integral (PI) controller [15], proportional-derivative (PD) controller [16], feedforward AQM (FF-AQM) [17], self-tuning compensated proportional-integral-derivative (ST-CPID) controller [18], and stable AQM (SAQM) [19]. Hollot et al [15] designed a stabilizing PI controller that uses instantaneous samples of the queue size, and successfully overcame the instability and lowfrequency oscillations of the low-pass filter design of RED in the regulated output.…”

“…Hollot et al [15] designed a stabilizing PI controller that uses instantaneous samples of the queue size, and successfully overcame the instability and lowfrequency oscillations of the low-pass filter design of RED in the regulated output. Wang et al [17] proposed the FFAQM under feedforward model predictive control (MPC), which stabilizes the queue length at a target value as quickly as possible and smooths out the burst traffic. Kahe and Jahangir [18] put forward the ST-CPID to address the time-variation of network conditions induced by parameter changes and unresponsive connections.…”