Title

Event-Sampled Direct Adaptive NN State-Feedback Control of Uncertain Strict-Feedback System

Abstract

In this paper, 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 measurable. As part of the controller design, first, local 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 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 controller.

Meeting Name

2016 IEEE 55th Conference on Decision and Control, CDC (2016: Dec. 12-14, Las Vegas, NV)

Department(s)

Electrical and Computer Engineering

Comments

This research is supported, in part, by NSF ECCS #1128281 and #1406533 and Intelligent Systems Center.

Keywords and Phrases

Backstepping; Event sampling; Lyapunov method; Neural network (NN); State feedback

International Standard Book Number (ISBN)

978-1-5090-1837-6

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2016 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

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