Event Triggered Neuroadaptive Controller (ETNAC) Design for Uncertain Affine Nonlinear Systems


In this paper, a novel event triggered neural network (NN) adaptive controller is presented for uncertain affine nonlinear systems. Controller design is based on an observer, called as Modified State Observer (MSO), which is used to approximate uncertainties online. State is sensed continuously yet sent on feedback network only when required, in aperiodic fashion. Lyapunov analysis is used to derive this condition which is dynamic in nature since it is based on tracking error. In this way ETNAC helps to not only saves communication cost but also computational efforts. MSO formulations have two tunable gains which let you do fast estimation without inducing high frequency oscillations in the system. A benchmark example of 2-link robotic manipulator is used to show the efficacy of the proposed controller.

Meeting Name

ASME 2018 Dynamic Systems and Control Conference, DSCC 2018 (2018: Sep. 30-Oct. 3, Atlanta, GA)


Mechanical and Aerospace Engineering

Second Department

Engineering Management and Systems Engineering

Keywords and Phrases

Controllers; Frequency estimation; Human robot interaction; Machine design; Manipulators; Nonlinear systems; Robotics; Uncertainty analysis; Adaptive controllers; Affine Nonlinear systems; Communication cost; Computational effort; High frequency oscillations; Neural network (nn); Neuro-adaptive controllers; Robotic manipulators; Adaptive control systems

International Standard Book Number (ISBN)


Document Type

Article - Conference proceedings

Document Version


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© 2018 American Institute of Aeronautics and Astronautics (AIAA), All rights reserved.

Publication Date

01 Sep 2018