Distributed Optimization for a Class of Nonlinear Multiagent Systems With Disturbance Rejection

Wang, Xinghu; Ye, Hong; Ji, Haibo

doi:10.1109/tcyb.2015.2453167

Cited by 197 publications

(137 citation statements)

References 38 publications

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“…Consider the heterogeneous EL multi-agent system (3), and suppose that Assumption 1 holds. Then, for any initial conditions with n i=1 v i (0) = 0 m , the distributed optimal consensus of system (3) can be achieved exponentially under the controller (6) with event-triggered condition (8). Furthermore, the Zeno behavior of the event-triggered strategies can be excluded.…”

Section: Resultsmentioning

confidence: 99%

“…So far, there have been many meaningful results in continuous-time cases. For example, [5,6] developed gradient-based or subgradient-based algorithms to solve the constrained distributed optimization problem, while [7,8] studied the distributed optimization problem with external disturbances. Moreover, [16] designed an algorithm for distributed optimization problem with discrete-time communication.…”

Section: Introductionmentioning

confidence: 99%

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Event-triggered design for multi-agent optimal consensus of Euler--Lagrangian systems

Wang¹,

Deng²,

Song³

et al. 2017

Kybernetika

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Event-triggered design for multi-agent optimal consensus of Euler--Lagrangian systems

Wang¹,

Deng²,

Song³

et al. 2017

Kybernetika

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“…With Remark , an internal model can be designed for every player as follows (see the works of Deng et al and Wang et al):

{\overset{η}{true}}_{i} = false(I_{n} \otimes H false) η_{i} + false(I_{n} \otimes ξ false) u_{i}, i \in scriptV .

…”

Section: Resultsmentioning

confidence: 99%

“…On the other hand, when the mechanical and electromagnetic losses are neglected, ie, the output electrical powers of generators are equal to the input mechanical powers of turbines, the dynamics of generation systems are first‐order linear systems after feedback linearization (see the work of Guo et al for details). Besides, the following disturbance is added to the generation system

i \in scriptV

, which was widely used (see other works):

d_{i} false(t false) = a_{i} \sin ((), w_{i} t + c_{i}),

where a i ,w i , c i are the parameters of the disturbance. Furthermore, we can rewrite as the form of with

C = false[1 0 false], S = ([], \begin{matrix} centerarray \\ array 0 & array {normalw}_{i} \\ array - {normalw}_{i} & array 0 \end{matrix})

, and

w_{i} false(0 false) = ([], \begin{matrix} centerarray \\ array a_{i} \sin c_{i} \\ array a_{i} \cos c_{i} \end{matrix}) .

…”

Section: Simulationsmentioning

confidence: 99%

“…Besides, it has been proved that the internal model approach is one of the effective ways to deal with various types of disturbances, such as the finite superposition of the step, sinusoidal, and ramp signals (see the works of Wang et al, Davison, and Huang). For instance, Deng et al and Wang et al) adopted the internal model approach to solve distributed optimization problems of distributed multiagent systems. Note that there are few results about aggregative games of disturbed multiagent systems.…”

Section: Introductionmentioning

confidence: 99%

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Distributed algorithm design for aggregative games of disturbed multiagent systems over weight‐balanced digraphs

Deng

Nian

2018

Intl J Robust & Nonlinear

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Summary In this paper, an aggregative game of multiagent systems over weight‐balanced digraphs is studied, where the decisions of all players are coupled by linear constraints. Different from the well‐known aggregative games, the dynamics of players are disturbed first‐order linear systems in our problem. In order to seek the variational generalized Nash equilibrium (GNE) of the game, a distributed algorithm is developed via gradient descent, internal model, and dynamic average consensus, where the gradient is for seeking the variational GNE, the internal model is for rejecting the exogenous disturbances, and the dynamic average consensus is for estimating the aggregate of the decisions of all players. The exponential convergence of the algorithm to the variational GNE is analyzed by constructing suitable Lyapunov functions. Finally, the effectiveness of our method is illustrated via two examples.

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Fully distributed optimization of second‐order systems with disturbances based on event‐triggered control

Hao

2023

Asian Journal of Control

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This paper studies the distributed optimization problem of second‐order multiagent systems containing external disturbances. To reject the external disturbances and lead agents' states to converge to the optimal consensus point, an adaptive event‐triggered controller is proposed based on the internal model principle. With the adaptive mechanism, both the controller and the event‐triggering condition do not contain the parameters related to global information, such as the maximum Lipschitz constant and the minimum strongly convex constant of local cost functions, and hence the event‐triggered controller is fully distributed. By utilizing the event‐triggered scheme, the consumption of communication among neighbors and the computing resources are saved. Furthermore, with the Lyapunov analysis framework, the optimal consensus can be proved to achieve and Zeno behavior is excluded from the event‐triggering condition. Finally, the effectiveness of the proposed protocol is verified by numerical simulations.

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Distributed Optimization for a Class of Nonlinear Multiagent Systems With Disturbance Rejection

Cited by 197 publications

References 38 publications

Event-triggered design for multi-agent optimal consensus of Euler--Lagrangian systems

Event-triggered design for multi-agent optimal consensus of Euler--Lagrangian systems

Distributed algorithm design for aggregative games of disturbed multiagent systems over weight‐balanced digraphs

Fully distributed optimization of second‐order systems with disturbances based on event‐triggered control

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