Event-Triggered Control of N-Player Nonlinear Systems using Nonzero-Sum Games


This paper presents an approximate optimal event-triggered control scheme for an N-player multi-input multi-output nonlinear system. The distributed minimizing control policy of each player is obtained co-operatively by introducing a novel performance index such that the Nash equilibrium is attained. The aperiodic control execution instants are optimized for each player by limiting the control policy error, i.e., the error between the continuous and sampled polices, by the worst case threshold, computed by solving the corresponding Hamilton-Jacobi (HJ) equation. The HJ equation is approximately solved using approximate dynamic programming (ADP). A critic neural network is employed at each player to approximate the solution, i.e., optimal value function, with aperiodically available feedback information. Impulsive weight update scheme with event-based Bellman error is proposed to guarantee convergence to near optimal solution and closed-loop stability of the event-triggered system. Finally, analysis of Zeno free behavior of the system is included along with numerical simulation results.

Meeting Name

2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018 (2018: Nov. 18-21, Bangalore, India)


Electrical and Computer Engineering


This research is funded in part by the intelligent systems center, Rolla, NSF ECCS #1406533, CMMI #1547042, and start-up fund Oklahoma State University, Stillwater, OK.

Keywords and Phrases

Artificial intelligence; Errors; MIMO systems; Nonlinear systems; Optimal systems; Approximate dynamic programming; Closed loop stability; Event-triggered controls; Event-triggered system; Hamilton Jacobi equations; Multi-input multi-output nonlinear systems; Near-optimal solutions; Optimal value functions; Dynamic programming

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Document Type

Article - Conference proceedings

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© 2018 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Nov 2018