Neural networks based adaptive tracking for nonlinear systems

Raheel Quraishi, Nisar Ahmed, Muhammad Shafiq

Research output: Contribution to journalArticle

Abstract

Adaptive Inverse Control (AIC) is a very significant approach for control of unknown linear and nonlinear plants; neural networks based Adaptive Inverse Control (AIC), for unknown dynamical systems has received much attention in recent years due to its generalized and acquiescent characteristics. In this paper a new Radial Basis Function Neural Networks (RBFNN) based technique for desired tracking performance of a nonlinear plant is presented. In this scheme the tracking error is passed through the estimated jacobian of the plant: and then used for updating the parameters of inverse controller. The presented scheme is authenticated through a simulation on a nonlinear plant model of a heat exchanger. The results demonstrate good quality tracking execution and error convergence is attained both in the presence of disturbance and without disturbance in the plant. Also the presented scheme is capable of minimizing the effect of disturbance in the plant.

Original languageEnglish
Pages (from-to)2457-2476
Number of pages20
JournalInformation (Japan)
Volume18
Issue number6
Publication statusPublished - Jun 1 2015

    Fingerprint

Keywords

  • Adaptive Inverse Control
  • Heat Exchanger
  • Neural Control
  • Radial Basis Function Neural Networks
  • Tracking in Nonlinear Systems

ASJC Scopus subject areas

  • General

Cite this