Title
Neural Network-Based Event-Triggered State Feedback Control of Nonlinear Continuous-Time Systems.
Abstract
This paper presents a novel approximation-based event-triggered control of multi-input multi-output uncertain nonlinear continuous-time systems in affine form. The controller is approximated using a linearly parameterized neural network (NN) in the context of event-based sampling. After revisiting the NN approximation property in the context of event-based sampling, an event-triggered condition is proposed using the Lyapunov technique to reduce the network resource utilization and to generate the required number of events for the NN approximation. In addition, a novel weight update law for aperiodic tuning of the NN weights at triggered instants is proposed to relax the knowledge of complete system dynamics and to reduce the computation when compared with the traditional NN-based control. Nonetheless, a nonzero positive lower bound for the inter-event times is guaranteed to avoid the accumulation of events or Zeno behavior. For analyzing the stability, the event-triggered system is modeled as a nonlinear impulsive dynamical system and the Lyapunov technique is used to show local ultimate boundedness of all signals. Furthermore, in order to overcome the unnecessary triggered events when the system states are inside the ultimate bound, a dead-zone operator is used to reset the event-trigger errors to zero. Finally, the analytical design is substantiated with numerical results.
Year
DOI
Venue
2016
10.1109/TNNLS.2015.2416259
IEEE transactions on neural networks and learning systems
Keywords
Field
DocType
adaptive control,approximation,event-triggered control (etc),neural network (nn) control.
Lyapunov function,Control theory,Nonlinear system,Function approximation,Upper and lower bounds,Control theory,Computer science,System dynamics,Dynamical system,Numerical stability
Journal
Volume
Issue
ISSN
PP
99
2162-2388
Citations 
PageRank 
References 
51
1.23
14
Authors
3
Name
Order
Citations
PageRank
Avimanyu Sahoo115510.66
Hao Xu221414.63
Sarangapani Jagannathan3113694.89