Model predictive neural control of a high-fidelity helicopter model

Alexander A. Bogdanov, Eric A. Wan, Magnus Carlsson, Yinglong Zhang, Richard Kieburtz, Antonio Baptista

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

20 Scopus citations

Abstract

In this paper we present a method for optimal control of a nonlinear highly realistic helicopter model based on a combination of a neural network (NN) feedback controller and a state-dependent Riccati equation (SDRE) controller. Optimization of the NN is performed within a receding horizon model predictive control (MPC) framework, subject to dynamic and kinematic constraints. The SDRE controller utilizes a simplified 6DOF rigid body dynamic model, and augments the NN controller by providing an initial feasible solution and improving stability. While the SDRE control provides robustness based on a pseudo-linear formulation of the dynamics, the MPNC utilizes the highly accurate numerical helicopter model.

Original languageEnglish (US)
Title of host publicationAIAA Guidance, Navigation, and Control Conference and Exhibit
StatePublished - 2001
EventAIAA Guidance, Navigation, and Control Conference and Exhibit 2001 - Montreal, QC, Canada
Duration: Aug 6 2001Aug 9 2001

Publication series

NameAIAA Guidance, Navigation, and Control Conference and Exhibit

Other

OtherAIAA Guidance, Navigation, and Control Conference and Exhibit 2001
Country/TerritoryCanada
CityMontreal, QC
Period8/6/018/9/01

ASJC Scopus subject areas

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

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