Event-triggered reinforcement learning approach for unknown nonlinear continuous-time system
Authors:
- 1 July 2014
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 21614393,p. 3677-3684
Abstract
This paper provides an adaptive event-triggered method using adaptive dynamic programming (ADP) for the nonlinear continuous-time system. Comparing to the traditional method with fixed sampling period, the event-triggered method samples the state only when an event is triggered and therefore the computational cost is reduced. We demonstrate the theoretical analysis on the stability of the event-triggered method, and integrate it with the ADP approach. The system dynamics are assumed unknown. The corresponding ADP algorithm is given and the neural network techniques are applied to implement this method. The simulation results verify the theoretical analysis and justify the efficiency of the proposed event-triggered technique using the ADP approach.Keywords
This publication has 33 references indexed in Scilit:
- Reactive power control of grid-connected wind farm based on adaptive dynamic programmingNeurocomputing, 2014
- Heuristic dynamic programming with internal goal representationSoft Computing, 2013
- Robust controller design of continuous-time nonlinear system using neural network2013
- Goal Representation Heuristic Dynamic Programming on Maze NavigationIEEE Transactions on Neural Networks and Learning Systems, 2013
- Adaptive Learning in Tracking Control Based on the Dual Critic Network DesignIEEE Transactions on Neural Networks and Learning Systems, 2013
- Model-based periodic event-triggered control for linear systemsAutomatica, 2013
- Learning and Optimization in Hierarchical Adaptive Critic Design2012
- A three-network architecture for on-line learning and optimization based on adaptive dynamic programmingNeurocomputing, 2012
- Event-Triggered Feedback in Control, Estimation, and Optimization2010
- A Self-Learning Call Admission Control Scheme for CDMA Cellular NetworksIEEE Transactions on Neural Networks, 2005