Stable Neural Control of Uncertain Multivariable Systems

Abstract

Tracking control of a class of nonlinear, uncertain, multi-input, multiple-output systems is addressed in this paper. The control system architecture uses neural networks for function approximation, certainty equivalent control inputs to cancel plant dynamics and smoothed sliding mode control to insure that the trajectories remain bonded. Lyapunov analysis is used to derive equations for the sliding mode control, neural network training, and to show uniform ultimate boundedness of the closed loop systems. Stability analysis results are shown for single-input single-output and two-input two-output systems. Results are then extended to the more general multiple-input multiple-output case where the number of inputs is equal to the number of outputs. Simple simulation examples are used to illustrate control system performance.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2001
Accession Number
ADA411951

Entities

People

  • Marios M. Polycarpou
  • Mark J. Mears

Organizations

  • Air Force Research Laboratory

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Air Force
  • Air Force Facilities
  • Air Force Research Laboratories
  • Aircrafts
  • Closed Loop Systems
  • Control Systems
  • Dynamics
  • Equations
  • Intellectual Property
  • Law
  • Multiple Input Multiple Output
  • Neural Networks
  • Signal Processing
  • Simulations
  • Trajectories
  • United States
  • Unmanned Aerial Vehicles

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Calculus or Mathematical Analysis
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks