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.
A. Gaffoor et al., "Event Triggered Neuroadaptive Controller (ETNAC) Design for Uncertain Affine Nonlinear Systems," Proceedings of the American Society of Mechanical Engineers 2018, American Society of Mechanical Engineers (ASME), Oct 2018.
Dynamic Systems and Controls Conference, DSC (2018: Sep. 30-Oct. 3, )
Mechanical and Aerospace Engineering
Electrical and Computer Engineering
Intelligent Systems Center
Article - Conference proceedings
© 2018 American Society of Mechanical Engineers (ASME), All rights reserved.
03 Oct 2018