@inProceedings{
 title = {Optimal event-triggered control of uncertain linear networked control systems: A co-design approach},
 type = {inProceedings},
 year = {2018},
 identifiers = {[object Object]},
 volume = {2018-Janua},
 id = {6e2065ee-b9d6-36d5-a9de-b7522395eb17},
 created = {2018-05-11T07:50:14.420Z},
 file_attached = {false},
 profile_id = {94cd643a-5276-3417-96f0-5dae1d90e4ae},
 last_modified = {2018-05-11T07:50:14.420Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {© 2017 IEEE. In this paper, a co-design approach for event-based optimal state regulation of an uncertain linear networked control system is presented. Both the transmission intervals and the control policy are optimized by introducing a novel performance index such that the error in the control policy due to event-based transmission can be maximized. The event-triggering mechanism uses the worst case control input error as threshold to decide the optimal transmission instants. Stochastic Q-learning approach is used to design both the control policy and event-triggering condition without explicit knowledge of the system dynamics. The event-based Q-function parameters are updated using a hybrid scheme both at triggering instants and during inter-event times to accelerate the parameter convergence. The asymptotic stability in the mean square of the closed-loop system is demonstrated using Lyapunov analysis with the assumptions of persistence of excitation of regression vector. Finally, numerical results are included to substantiate the analytical design.},
 bibtype = {inProceedings},
 author = {Sahoo, A. and Narayanan, V. and Jagannathan, S.},
 booktitle = {2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings}
}

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