Multiple Model Adaptive Estimation Applied to the ViSTA F-16 with Actuator and Sensor Failures

Abstract

A Multiple Model Adaptive Estimation (MMAE) algorithm is applied to the Variable Stability In-flight Simulator Test Aircraft (VISTA) F-16 at a low dynamic pressure flight condition (0.4 Mach at 20000 ft). A complete F-16 flight control system is modeled containing the longitudinal and lateral-directional axes. Single and dual actuator and sensor failures are simulated including: complete actuator failures, partial actuator failures, complete sensor failures, increased sensor noise, sensor biases, dual complete actuator failures, dual complete sensor failures, and combinations of actuator and sensor failures. Failure scenarios are examined in both maneuvering and straight and level flight conditions. Single scalar residual monitoring techniques are evaluated with suggestions for improved performance. A hierarchical moving bank structure is utilized for multiple failure scenarios. Simultaneous dual failures are included within the study. White Gaussian noise is included to simulate the effects of atmospheric disturbances, and white Gaussian noise is added to the measurements to simulate the effects of sensor noise. Kalman Filtering, Multiple Model Adaptive Estimation, MMAE, Failure detection and isolation, Bayesian, MMAC, multiple Model Adaptive Control.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1992
Accession Number
ADA256444

Entities

People

  • Timothy E. Menke

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Air Force
  • Aircrafts
  • Algorithms
  • Computational Science
  • Control Surfaces
  • Control Systems
  • Damage Detection
  • Databases
  • Detectors
  • Dynamic Pressure
  • Estimators
  • Failure Mode And Effect Analysis
  • Flight Control Systems
  • Gaussian Noise
  • Measurement
  • Simulations
  • Standards

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Aerodynamics/Aeronautics.
  • Inertial Navigation Systems.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms