2015
DOI: 10.1002/rnc.3388
|View full text |Cite
|
Sign up to set email alerts
|

Event-triggered control for discrete-time linear systems subject to bounded disturbance

Abstract: Summary This paper considers event‐triggering controller design for directly observable discrete‐time linear systems subject to bounded disturbances. The main control objective is diminishing the influence aroused by the disturbances despite a reduction of the communication. Criteria are given to design feedback controllers in order to guarantee that systems are uniformly ultimately bounded in an ellipsoidal‐positive invariant set, which is used as an estimate of control performance for disturbance rejection. … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
2

Citation Types

0
58
0

Year Published

2016
2016
2020
2020

Publication Types

Select...
8

Relationship

0
8

Authors

Journals

citations
Cited by 70 publications
(58 citation statements)
references
References 29 publications
0
58
0
Order By: Relevance
“…However, Ref. [30] pointed out that the volume optimization can lead ellipsoids to be "flat" in some directions. On the contrary, the trace optimization yields the ellipsoids that tend to be homogeneous in all directions.…”
Section: Remarkmentioning
confidence: 99%
See 1 more Smart Citation
“…However, Ref. [30] pointed out that the volume optimization can lead ellipsoids to be "flat" in some directions. On the contrary, the trace optimization yields the ellipsoids that tend to be homogeneous in all directions.…”
Section: Remarkmentioning
confidence: 99%
“…On the contrary, the trace optimization yields the ellipsoids that tend to be homogeneous in all directions. Referring to [18,30], we consider the following optimization problem to find the largest ellipsoid for given K, L, ρ 0 and some ε 1 , ε 2 > 0: min −tr(P −1…”
Section: Remarkmentioning
confidence: 99%
“…The (strict) inequality of event triggering condition at event instants brings difficulty and inaccuracy in estimating the state under ETC and thus it may lead to conservative conditions. To the best of our knowledge, fewer results have been reported for solving these ETC issues for discrete‐time dynamical systems and networks despite the recent works in the literature …”
Section: Introductionmentioning
confidence: 99%
“…For instances, for continuous-time systems, these include centralized or decentralized ETC in a framework of input-to-state stability 1,4 ; output-based decentralized ETC for linear systems 3,5 ; distributed ETC for interconnected subsystems 6,7 ; event-triggered synchronization control 8 ; ETC via the method of delayed system 9 ; quantized and robust ETC for nonlinear systems via small-gain approach 10,11 ; ETC for stochastic systems 12,13 ; and stabilization via event-triggered impulsive control (ETIC). 14 For discrete-time systems, similarly, there are stabilization via ETC for discrete-time systems, [15][16][17][18] event-triggered strategies for control with an extension to self-triggered formulation, 15 event-triggered model predictive control, 19 ETC subject to disturbances or parameter-varyings, 20,21 consensus via event-triggered and self-triggered control, 22 and ETC via dynamic thresholds. 16 Notwithstanding the merits of ETC for dynamical systems and networks, it should be noted that there are several key issues in ETC design.…”
Section: Introductionmentioning
confidence: 99%
“…However, it is sometimes less preferable, especially during the peak of communications, because the sampling takes place periodically regardless of whether the current behaviors of the system states needs or not. In order to make up for the shortcomings of periodic data sampling, another control method, namely, event‐based control strategy, emerges as the times require . More specifically, an introduction of event‐triggered control and self‐triggered control for linear systems was provided in the work of Heemels et al Subsequently, Wu et al investigated the problem of event‐triggered control for directly observable discrete‐time linear systems subject to exogenous disturbances.…”
Section: Introductionmentioning
confidence: 99%