Nonlinear Model Predictive Congestion Control for Networks

Improved Optimization Process for Nonlinear Model Predictive Control of PMSM

Model-based predictive control (MPC) is one of the most efficient techniques that is widely used in industrial applications. In such controllers, increasing the prediction horizon results in better selection of the optimal control signal sequence. On the other hand, increasing the prediction horizon increase the computational time of the optimization process which make it impossible to be imple...

A Self-adaptive Predictive Congestion Control Model for Extreme Networks

To combine the design strategies of both preventive control and reactive control, a self-adaptive predictive congestion control model for extreme networks is proposed. First, a model is employed to predict the data traffic. Then the reference trajectory of the traffic is updated, and thereafter transmission flow is adjusted according to the prediction results by optimization of a performance in...

Congestion Control for TCP/AQM Networks using State Predictive Control

The purpose of this paper is to design congestion controllers for TCP/AQM networks using state predictive control and illustrate effectiveness of designed congestion controllers via SIMULINK and the ns-2 simulator. Linearized models of TCP/AQM networks can be described as linear systems with an information delay simply. Using state predictive control, these linear systems with an information de...

Nonlinear Model Predictive Control

Robust Model Predictive Control for a Class of Discrete Nonlinear systems

This paper presents a robust model predictive control scheme for a class of discrete-time nonlinear systems subject to state and input constraints. Each subsystem is composed of a nominal LTI part and an additive uncertain non-linear time-varying function which satisfies a quadratic constraint. Using the dual-mode MPC stability theory, a sufficient condition is constructed for synthesizing the ...