Paper Info
Title
Optimal Event-Triggered Control Of Nonlinear Systems: A Min-Max Approach
Abstract
This paper presents a co-optimization scheme for an event-triggered control system to simultaneously optimize both the sampling instants and the control policy. A continuous time nonlinear affine system is considered and a novel performance index is defined to regulate the system states with minimum energy and optimal feedback frequency. To achieve this, a min-max optimization problem is formulated with the control policy and error due to event-triggered feedback as two non-cooperative policies. Using the two-player non-cooperative zero-sum game theory, solution to the min-max optimization problem is determined. The sampling instants are optimized by designing an event-triggering mechanism with worst-case sampling error policy as threshold while the control policy is designed to minimize the performance index. Solution to this min-max problem is obtained by approximating the solution of the Hamilton-Jacobi-Issac (HJI) equation. Artificial neural networks (NNs) are employed for the approximation in a forward-in-time and on-line manner. To neutralize the effect of the aperiodic availability of the state information on learning accuracy, a hybrid learning scheme is proposed.The local ultimate boundedness of the closed-loop event-triggered system is demonstrated using Lyapunov direct method and Zeno free behavior of the system is also guaranteed. Finally, simulation results are included to validate the proposed design.
Year
Venue
Field
2018
2018 ANNUAL AMERICAN CONTROL CONFERENCE (ACC)
Affine transformation,Nonlinear system,Optimal control,Control theory,Computer science,Sampling (statistics),Control system,Aperiodic graph,Artificial neural network,Optimization problem
DocType
ISSN
Citations 
Conference
0743-1619
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Vignesh Narayanan1105.19
Avimanyu Sahoo215510.66
Sarangapani Jagannathan3113694.89