Neural Network-Based Event-Triggered State Feedback Control of Nonlinear Continuous-Time Systems
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.
A. Sahoo et al., "Neural Network-Based Event-Triggered State Feedback Control of Nonlinear Continuous-Time Systems," IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 3, pp. 497-509, Institute of Electrical and Electronics Engineers (IEEE), Mar 2016.
The definitive version is available at https://doi.org/10.1109/TNNLS.2015.2416259
Electrical and Computer Engineering
Intelligent Systems Center
Keywords and Phrases
Dynamical systems; Feedback; Feedback control; MIMO systems; Nonlinear feedback; Nonlinear systems; State feedback; Uncertainty analysis; Approximation properties; Event-triggered controls; Event-triggered system; Impulsive dynamical system; Linearly parameterized neural networks; Multi input multi output; Network resource utilization; Nonlinear continuous-time systems; Continuous time systems; Adaptive control; Approximation; Event-triggered control (ETC); Neural network (NN) control
International Standard Serial Number (ISSN)
Article - Journal
© 2016 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Mar 2016