Adaptive Neural Network-Based Event-Triggered Control of Single-Input Single-Output Nonlinear Discrete-Time Systems
Abstract
This paper presents a novel adaptive neural network (NN) control of single-input and single-output uncertain nonlinear discrete-time systems under event sampled NN inputs. In this control scheme, the feedback signals are transmitted, and the NN weights are tuned in an aperiodic manner at the event sampled instants. After reviewing the NN approximation property with event sampled inputs, an adaptive state estimator (SE), consisting of linearly parameterized NNs, is utilized to approximate the unknown system dynamics in an event sampled context. The SE is viewed as a model and its approximated dynamics and the state vector, during any two events, are utilized for the event-triggered controller design. An adaptive event-trigger condition is derived by using both the estimated NN weights and a dead-zone operator to determine the event sampling instants. This condition both facilitates the NN approximation and reduces the transmission of feedback signals. The ultimate boundedness of both the NN weight estimation error and the system state vector is demonstrated through the Lyapunov approach. As expected, during an initial online learning phase, events are observed more frequently. Over time with the convergence of the NN weights, the inter-event times increase, thereby lowering the number of triggered events. These claims are illustrated through the simulation results.
Recommended Citation
A. Sahoo et al., "Adaptive Neural Network-Based Event-Triggered Control of Single-Input Single-Output Nonlinear Discrete-Time Systems," IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 1, pp. 151 - 164, Institute of Electrical and Electronics Engineers (IEEE), Jan 2016.
The definitive version is available at https://doi.org/10.1109/TNNLS.2015.2472290
Department(s)
Electrical and Computer Engineering
Research Center/Lab(s)
Intelligent Systems Center
Keywords and Phrases
Adaptive control systems; Controllers; Digital control systems; Estimation; Uncertainty analysis; Adaptive neural network controls (ANNC); Adaptive neural networks; Approximation properties; Event-triggered controls (ETC); Nonlinear discrete-time systems; Single input and single outputs; Ultimate boundedness; Discrete time control systems; Function approximation; Neural network (NN) control
International Standard Serial Number (ISSN)
2162-237X; 2162-2388
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2016 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jan 2016
Comments
This work was supported in part by the National Science Foundation under Grant ECCS 1406533 and in part by the Intelligent Systems Center, Missouri University of Science and Technology, Rolla, MO, USA.