Neurocontrol of nonlinear systems via local memory neurons

  • Amin S
  • Rodin E
  • Wu A
  • 3

    Readers

    Mendeley users who have this article in their library.
  • 0

    Citations

    Citations of this article.

Abstract

In this report, we consider the part of our work which concerns the approximation of nonlinear dynamic systems using neural networks. Based on a new paradigm of neurons with local memory (NNLM), we discuss the representation of control systems by neural networks. Using this formulation, the basic issues of controllability and observability for the dynamic system are addressed. A separation principle of learning and control is presented for NNLM, showing that the weights of the network do not affect its dynamics. Theoretical issues concerning local linearization via a coordinate transformation and nonlinear feedback are discussed. For illustration of the approach simulation results for no nonlinear control of an aircraft encountering wind shear on take-off is presented.

Author-supplied keywords

  • Aircraft
  • Control
  • Dynamic neural networks
  • Neurocontrol
  • Nonlinear control

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Authors

  • S. M. Amin

  • E. Y. Rodin

  • A. Y. Wu

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free