Masters Theses


"This thesis addresses optimal control of a helicopter unmanned aerial vehicle (UAV). Helicopter UAVs may be widely used for both military and civilian operations. Because these helicopters are underactuated nonlinear mechanical systems, high-performance controller design for them presents a challenge. This thesis presents an optimal controller design via both state and output feedback for trajectory tracking of a helicopter UAV using a neural network (NN). The state and output-feedback control system utilizes the backstepping methodology, employing kinematic and dynamic controllers while the output feedback approach uses an observer in addition to these controllers. The online approximator-based dynamic controller learns the Hamilton-Jacobi-Bellman (HJB) equation in continuous time and calculates the corresponding optimal control input to minimize the HJB equation forward-in-time. Optimal tracking is accomplished with a single NN utilized for cost function approximation. The overall closed-loop system stability is demonstrated using Lyapunov analysis. Simulation results are provided to demonstrate the effectiveness of the proposed control design for trajectory tracking. A description of the hardware for confirming the theoretical approach, and a discussion of material pertaining to the algorithms used and methods employed specific to the hardware implementation is also included. Additional attention is devoted to challenges in implementation as well as to opportunities for further research in this field. This thesis is presented in the form of two papers"--Abstract, page iv.


Sarangapani, Jagannathan, 1965-

Committee Member(s)

Erickson, Kelvin T.
Zawodniok, Maciej Jan, 1975-


Electrical and Computer Engineering

Degree Name

M.S. in Electrical Engineering


U.S. Army Research Laboratory


Missouri University of Science and Technology

Publication Date


Journal article titles appearing in thesis/dissertation

  • Neural network-based optimal output feedback control for trajectory tracking of a helicopter UAV.
  • Neural-network-based optimal control of a helicopter unmanned aerial vehicle (UAV) with hardware implementation


ix, 94 pages

Note about bibliography

Includes bibliographical references (pages 73-78).


© 2011 David John Nodland, All rights reserved.

Document Type

Thesis - Open Access

File Type




Subject Headings

Helicopters -- Control systems
Vehicles, Remotely piloted
Neural networks (Computer science)
Control theory -- Mathematical models

Thesis Number

T 10204

Print OCLC #


Electronic OCLC #