0
Research Papers

A Bio-Inspired Adaptive Control Compensation System for an Aircraft Outside Bounds of Nominal Design

[+] Author and Article Information
Andres E. Perez

Aerospace Engineering Department,
Embry-Riddle Aeronautical University,
Daytona Beach, FL 32119
e-mail: perezroa@my.erau.edu

Hever Moncayo

Assistant Professor
Aerospace Engineering Department,
Embry-Riddle Aeronautical University,
Daytona Beach, FL 32119
e-mail: moncayoh@erau.edu

Mario Perhinschi

Associate Professor
Mechanical and Aerospace Engineering Department,
West Virginia University,
Morgantown, WV 26506
e-mail: Mario.Perhinschi@mail.wvu.edu

Dia Al Azzawi

Adjunct Professor
Mechanical and Aerospace Engineering Department,
West Virginia University,
Morgantown, WV 26506
e-mail: diaazzawi@mix.wvu.edu

Adil Togayev

Mechanical and Aerospace Engineering Department,
West Virginia University,
Morgantown, WV 26506
e-mail: astogayev@mix.wvu.edu

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received December 11, 2014; final manuscript received May 5, 2015; published online June 24, 2015. Assoc. Editor: M. Porfiri.

J. Dyn. Sys., Meas., Control 137(9), 091012 (Sep 01, 2015) (13 pages) Paper No: DS-14-1526; doi: 10.1115/1.4030613 History: Received December 11, 2014; Revised May 05, 2015; Online June 24, 2015

This paper presents a novel bio-inspired adaptive control technique that has been designed to maintain the performance of an aircraft under upset conditions. The proposed control approach is inspired by biological principles that govern the humoral response of the immune system of living organisms and is intended to reduce pilot effort while maintaining adequate aircraft operation outside bounds of nominal design. The immunity-based control parameters are optimized offline for multiple sets of failures using a genetic algorithm approach. The performance of the immunity-based augmentation is compared with a neural network (NN)-based augmentation. Different piloted tests were performed on a six degrees-of-freedom (6DOF) motion-based simulator for different types of maneuvers under several flight conditions. The results show that the artificial immune system (AIS) proposed scheme improves the aircraft handling qualities by reducing the tracking errors (TEs) and improving the pilot response required to maintain control of the aircraft under upset conditions.

Copyright © 2015 by ASME
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Fig. 1

Humoral immune system representation

Grahic Jump Location
Fig. 2

General scheme of the baseline controller

Grahic Jump Location
Fig. 3

General scheme of the baseline controller (PID+NLDI) augmented with NNs

Grahic Jump Location
Fig. 4

Block diagram of the PID-AIS-based mechanism

Grahic Jump Location
Fig. 5

Baseline controller augmented with AIS-based mechanism

Grahic Jump Location
Fig. 6

simulink aircraft model

Grahic Jump Location
Fig. 7

The WVU 6DOF motion-based flight simulator

Grahic Jump Location
Fig. 8

Motion-based simulator maneuvers chronological history

Grahic Jump Location
Fig. 9

Multifailure evolutionary search diagram

Grahic Jump Location
Fig. 10

Desktop simulation results showing average PI for nine failures

Grahic Jump Location
Fig. 11

Roll response of system under 8 deg stabilator failure

Grahic Jump Location
Fig. 12

Pitch response of system under high magnitude stabilator failure

Grahic Jump Location
Fig. 13

Total pilot activity history for a high magnitude stabilator failure

Grahic Jump Location
Fig. 14

Nominal condition motion-based simulator results

Grahic Jump Location
Fig. 15

Aileron failure motion-based simulator results

Grahic Jump Location
Fig. 16

Rudder failure motion-based simulator results

Grahic Jump Location
Fig. 17

Stabilator failure motion-based simulator results

Grahic Jump Location
Fig. 18

Structural failure motion-based simulator results

Grahic Jump Location
Fig. 19

Average PI for all failures motion-based simulator results

Grahic Jump Location
Fig. 20

Average performance index of all failures for TE

Grahic Jump Location
Fig. 21

Average performance index of all failures for pilot activity

Grahic Jump Location
Fig. 22

Average performance index of all failures for control surface activity

Grahic Jump Location
Fig. 23

Algorithm to test task execution time of the different codes required to run each controller

Grahic Jump Location
Fig. 24

Task execution time histogram

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In