Generalized predictive control—Part I. The basic algorithm
Automatica (Journal of IFAC)
Multilayer feedforward networks are universal approximators
Neural Networks
Random early detection gateways for congestion avoidance
IEEE/ACM Transactions on Networking (TON)
A receding-horizon regulator for nonlinear systems and a neural approximation
Automatica (Journal of IFAC)
Fluid-based analysis of a network of AQM routers supporting TCP flows with an application to RED
Proceedings of the conference on Applications, Technologies, Architectures, and Protocols for Computer Communication
A control theoretic approach to active queue management
Computer Networks: The International Journal of Computer and Telecommunications Networking
Aggregate traffic performance with active queue management and drop from tail
ACM SIGCOMM Computer Communication Review
Artificial Neural Networks for Modelling and Control of Non-Linear Systems
Artificial Neural Networks for Modelling and Control of Non-Linear Systems
Analysis and design of the virtual rate control algorithm for stabilizing queues in TCP networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
FPGA Implementations of Neural Networks
FPGA Implementations of Neural Networks
RaQ: A robust active queue management scheme based on rate and queue length
Computer Communications
SMPCS: sub-optimal model predictive control scheduler
NEW2AN'06 Proceedings of the 6th international conference on Next Generation Teletraffic and Wired/Wireless Advanced Networking
The explicit linear quadratic regulator for constrained systems
Automatica (Journal of IFAC)
Structured neural networks for constrained model predictive control
Automatica (Journal of IFAC)
A robust active queue management scheme for network congestion control
Computers and Electrical Engineering
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Utilizing model predictive controllers (MPC) as an active queue management scheme is investigated in this paper. Model based prediction of future output and determining optimized value of the control signal have made MPC as an advanced control strategy in various modern control systems. In this paper a new approach is proposed to alleviate the computational complexity of MPC in order to implement in fast dynamics systems like computer networks. Neural network approximation of MPC as an active queue management (AQM) method implemented here not only has less computational burden with respect to the common MPC approaches, but also results in better performance compare to the well-known AQM methods such as random early detection (RED) and proportional-integral (PI) control. The proposed AQM approach is implemented in a field-programmable gate array (FPGA) system and its feasibility is investigated by timing analysis.