Synthesis and Implementation of Single and Multi-Vehicle Systems Guidance Based on Nonlinear Control and Optimization Techniques

Abstract

This report documents the work related to tasks #1, 2, 3 and 5 for the project entitled "Synthesis and Implementation of Single- and Multi-vehicle Systems Guidance Based on Nonlinear Control and Optimization Techniques". In this report the radial basis function (RBF) neural network control approach for active flow control extended to handle unmodelled dynamics and multiple equilibrial in hybrid (switching) system framework. Hybrid RBF adaptive controller applied to delta wing vortex-coupled-led roll dynamics using hysteresis switching logic. The combinatory control method also applied to the delta wing dynamics coupled with the SMA micro actuator dynamics which has been obtained form identification process in DRDC. In this report also the linear parameter-varying sliding mode control (LPVSMC) approach which has been developed for linear parameter-varying time-delayed systems (LPVTDS) has been applied to delta wing model coupled with SMA dynamics. This approach combines sliding mode control (SMC), linear parameter-varying (LPV) control theory, and time delay stability analysis to solve a LPVTDS control problem. It is anticipated that this method will lead to significant improvement over existing SMC approaches in aerospace applications with parameter variations and coupled with new SMA actuating devices.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2007
Accession Number
ADA475429

Entities

People

  • Ali Azimi
  • Brandon W. Gordon
  • Hojjat A. Izadi
  • Mehrdad Pakmehr
  • Yan Zhao

Organizations

  • Concordia University

Tags

Communities of Interest

  • Space

DTIC Thesaurus Topics

  • Abstracts
  • Control Theory
  • Delta Wings
  • Dynamics
  • Guidance
  • Hypervelocity Flow
  • Information Operations
  • Instructions
  • Neural Networks
  • Optimization
  • Switching

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Control Systems Engineering.
  • Marine Propulsion Engineering and Naval Architecture

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Space
  • Space - Spacecraft Maneuvers