Feedback linearization with neural network augmentation applied to X-33 attitude control

Eric Johnson, Anthony J. Calise, Hesham A. El-Shirbiny, Rolf T. Rysdyk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

121 Scopus citations

Abstract

In the application of adaptive flight control, significant issues arise due to vehicle input characteristics such as actuator position limits, actuator position rate limits, and linear input dynamics. The concept of modifying a reference model to prevent an adaptation law from "seeing" and adapting-to these system characteristics is introduced. A specific adaptive control method based on this concept, termed Pseudo-Control Hedging, is introduced that accomplishes this for any Model Reference Adaptive Controller that includes approximate feedback linearization. This method enables continued adaptation while the plant input is saturated. Acceptance and flight certification of an online Neural Network adaptive control law for the X- 33 Reusable Launch Vehicle technology demonstrator is discussed as motivation for this work. Simulation results applying the method to the X-33 are described.

Original languageEnglish (US)
Title of host publicationAIAA Guidance, Navigation, and Control Conference and Exhibit
StatePublished - Dec 1 2000
EventAIAA Guidance, Navigation, and Control Conference and Exhibit 2000 - Dever, CO, United States
Duration: Aug 14 2000Aug 17 2000

Other

OtherAIAA Guidance, Navigation, and Control Conference and Exhibit 2000
CountryUnited States
CityDever, CO
Period8/14/008/17/00

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Feedback linearization with neural network augmentation applied to X-33 attitude control'. Together they form a unique fingerprint.

  • Cite this

    Johnson, E., Calise, A. J., El-Shirbiny, H. A., & Rysdyk, R. T. (2000). Feedback linearization with neural network augmentation applied to X-33 attitude control. In AIAA Guidance, Navigation, and Control Conference and Exhibit