Premium
A novel AQM algorithm based on feedforward model predictive control
Author(s) -
Wang Ping,
Zhu ChaoJie,
Yang XiaoPing
Publication year - 2018
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.3711
Subject(s) - feed forward , active queue management , control theory (sociology) , computer science , model predictive control , queue , controller (irrigation) , observer (physics) , feedforward neural network , noise (video) , control (management) , control engineering , network congestion , network packet , artificial intelligence , artificial neural network , engineering , computer network , biology , programming language , image (mathematics) , agronomy , physics , quantum mechanics
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.