2018
DOI: 10.1016/j.automatica.2018.05.032
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A fixed-time convergent algorithm for distributed convex optimization in multi-agent systems

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Cited by 114 publications
(65 citation statements)
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“…, N } that finds the minimizer x * ∈ R n of f (•), with each node i having access to its individual cost function f i (•) only, has been tackled in recent years [6]- [12]. However, most of them, e.g., [6]- [11], assume that the individual cost function f i (•) is convex. The reason is the need for stability, e.g., passivity, for each node, to achieve consensus.…”
Section: Introductionmentioning
confidence: 99%
“…, N } that finds the minimizer x * ∈ R n of f (•), with each node i having access to its individual cost function f i (•) only, has been tackled in recent years [6]- [12]. However, most of them, e.g., [6]- [11], assume that the individual cost function f i (•) is convex. The reason is the need for stability, e.g., passivity, for each node, to achieve consensus.…”
Section: Introductionmentioning
confidence: 99%
“…On distributed finite‐time optimisation, by using non‐smooth control techniques, a few of results were proposed for first‐order [32–35] and second‐order [36, 37] multi‐agent systems. However, neither matched nor mismatched disturbances were considered in [32, 33, 35, 36]. In [34], for first‐order multi‐agent systems subject to bounded disturbances, an approximate distributed finite‐time optimisation control algorithm was proposed by using integral sliding‐mode control.…”
Section: Introductionmentioning
confidence: 99%
“…The main contribution is threefold. First, different from the existing distributed finite‐time optimisation literature [32–36] where no disturbances or only matched disturbances were considered, in this study, the distributed finite‐time optimisation problem is solved for second‐order multi‐agent systems with mismatched disturbances. Specifically, the proposed distributed composite optimisation controllers directly and fast handle the disturbance effects, and retain the nominal control performance.…”
Section: Introductionmentioning
confidence: 99%
“…To date, although a wide spectrum of results have been reported for discrete-time networks with various scenarios in the literature, ranging from distributed optimization problems in the absence of constraints to those subject to constraints, [1][2][3][4][5] continuous-time algorithms have attracted an increasing interest in recent years mostly due to the fact that a lot of physical systems operate in a continuum domain, such as the current flow in smart grid. [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] For instance, distributed convex optimization problems have been studied in the work of Yang et al 17 subject to local feasible constraints, local inequality and equality constraints, where a proportional-integral continuous-time algorithm has been designed with output information exchange.…”
Section: Introductionmentioning
confidence: 99%