Model predictive control based direct neural controllers represent another class of computer application in the field of non-linear controls. These controllers can also be made adaptive such that the adaptation mechanism attempts to adjust a parameterized nonlinear controller to approximate an ideal controller. Various approximators such as linear mappings, polynomials, fuzzy systems, or neural networks can be used as parameterized nonlinear controller. In this paper, we proposed a model predictive control based neural network controller to control the liquid level in a surge tank, with respect to the reference input. The neural controller works on the normalized gradient-based approximator parameter update law used for a class of nonlinear discrete-time systems in direct cases. In our proposed design, the reduction in error is reached upon between the ideal and the actual controller and the direct adaptive control scheme is tested for performance via a simple surge tank example. The proposed controller algorithm performs well and can be physically implemented. KeywordsModel predictive control, Direct neural control, Non linear systems.

Design of a neural network based model predictive controller for UDP(User Datagram Protocol) flow caused congestion, in IP( Internet protocol) networks is proposed in this paper. The objectives of congestion control are prevention of congestion collapse, maximum network bandwidth utilization, TCP-friendliness and smoothness for streaming media applications. Various approaches for controlling congestion in networks are present in the literature. Many of these are make use of network models, which are already identified. In this paper a neural network utilizing Levenberg-Marquardt learning algorithm for on-line identification of non-linear plant(network) model is implemented and combined with a model predictive optimization technique using back tracking line search routine over a specified time horizon. Simulations were carried out to prove the effectiveness of the designed controller. Significant increase in the network bandwidth utilization is also established.

Human body can be used as a communication channel for electrical signal transmission and thus offers a novel data communication means in biomedical monitoring systems. Human Body communication channel (on-body) may be proven as promising solution for Wireless Body Area networks (WBANs) in terms of simplicity, reliability, powerefficiency and security. This study proposes the design of an adaptive filter equivalent for human body communication channel. The simulations are based on Electronics and Telecommunication Research Institute (ETRI's) measurement results obtained on human body within a frequency range of 5-50MHz. The measured frequency response is processed to obtain FIR filter matrix coefficients and further identified as RLS adaptive filter. The designing is done using system identification tool in MATLAB. Also a comparison is made between RLS and normalized LMS algorithm for adaptive filter design, which established the RLS adaptive filter as the promising solution for modeling Human Body Communication Channel.

In the present study a congestion avoidance algorithm is proposed for multi-users, transmitting simultaneously on a single link. The present algorithm is based on the additive increase and multiplicative decrease feedback law. Equilibrium is reached at which all users shared equally at steady state and utilizes the whole link capacity. The resource allocation involves constraint on shared capacity and generates desired output within limited time period. The capacity of link is fully utilized and shared among the users in the set ratio, using the resource allocation algorithm. It represents a very simple, effective and more robust method for distribution of link capacity.