Masters Theses
Keywords and Phrases
Backstepping; Event-Sampling; Lyapunov Method; Neural Network; Output-Feedback; Quadrotor UAV
Abstract
"Neural networks (NNs) are utilized in the backstepping approach to design a control input by approximating unknown dynamics of the strict-feedback nonlinear system with event-sampled inputs. The system state vector is assumed to be unknown and an observer is used to estimate the state vector. By using the estimated state vector and backstepping design approach, an event-sampled controller is introduced. As part of the controller design, first, input-to-state-like stability (ISS) for a continuously sampled controller that has been injected with bounded measurement errors is demonstrated and, subsequently, an event-execution control law is derived such that the measurement errors are guaranteed to remain bounded. Lyapunov theory is used to demonstrate that the tracking errors, the observer estimation errors, and the NN weight estimation errors for each NN are locally uniformly ultimately bounded (UUB) in the presence bounded disturbances, NN reconstruction errors, as well as errors introduced by event-sampling. Simulation results are provided to illustrate the effectiveness of the proposed controllers.
Subsequently, the output-feedback neural network (NN) controller that was presented above is considered for an underactuated quadrotor UAV application. The flexibility for the control of a quadrotor UAV is extended by incorporating notions of event-sampling and by designing an appropriate event-execution law. First, the continuously sampled controller is considered in the presence of bounded measurement errors and it is shown that the system generates a local ISS-like Lyapunov function. Next, by designing an appropriate event-execution law, the measurement errors that result from event-sampling are shown to be bounded for all time. Finally, the effectiveness of the proposed event-sampled controller is demonstrated with simulation results"--Abstract, page iv.
Advisor(s)
Sarangapani, Jagannathan, 1965-
Committee Member(s)
Acar, Levent
Stanley, R. Joe
Department(s)
Electrical and Computer Engineering
Degree Name
M.S. in Electrical Engineering
Sponsor(s)
National Science Foundation (U.S.)
Missouri University of Science and Technology. Intelligent Systems Center
Research Center/Lab(s)
Intelligent Systems Center
Publisher
Missouri University of Science and Technology
Publication Date
Fall 2016
Journal article titles appearing in thesis/dissertation
- Event-sampled direct adaptive nn output- and state-feedback control of uncertain strict-feedback system
- Event-sampled control of quadrotor unmanned aerial vehicle
Pagination
x, 114 pages
Note about bibliography
Includes bibliographical references.
Rights
© 2016 Nathan Szanto
Document Type
Thesis - Open Access
File Type
text
Language
English
Subject Headings
Drone aircraft -- Control systems -- Design and constructionDrone aircraft -- Control systems -- Mathematical modelsNeural networks (Computer science)Lyapunov stability
Thesis Number
T 11053
Electronic OCLC #
974715916
Recommended Citation
Szanto, Nathan, "Event-sampled direct adaptive neural network control of uncertain strict-feedback system with application to quadrotor unmanned aerial vehicle" (2016). Masters Theses. 7616.
https://scholarsmine.mst.edu/masters_theses/7616