Narasimhan SUNDARARAJAN 

Senior Research Fellow of School of Electrical & Electronic Engineering

Description

Until the later part of 1940's and early 1950s flight control research was aimed at providing pilot relief capabilities, mainly in the form of auto-pilots. With the advent of high performance aircrafts, it become evident that controllers were required to bring the aircrafts within certain specified operating envelop that would increase the pilots capabilities for controlling the aircrafts. Due to stringent requirements and complexities of the modern flight control system, it is difficult to estimate the nonlinearities accurately.  The performance of conventional controller will be poor under severe nonlinearities. The research deals with the new design development of adaptive and fault tolerant control systems using neural network methodologies. Conventional aircraft, helicopters and unmanned aerial vehicles are considered. The aim of the research area is the design of adaptive flight controllers such that the aircraft can even under severe fault and disturbances. The application of controllers to linear and nonlinear models was investigated.

A sequential learning algorithm for realizing a minimal radial basis function network (RBFN), referred to as Minimal Resource Allocating Network (MRAN). With the growing and pruning strategy, the MRAN algorithm can implement a more compact network  structure, resulting in fast on-line learning. The MRAN algorithm is successfully applied in the areas of function approximation, time series prediction, nonlinear system identification and pattern recognition.

Neural Network Algorithms

Adaptive Flight Control

Minimal Resource Allocation Network:

Research Interest