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

Wang, Xuefang; Deng, Zhenhua; Song, Mengxuan; Du, Xian

doi:10.14736/kyb-2017-1-0179

Cited by 20 publications

(16 citation statements)

References 25 publications

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“…However, the authors design the evolution speeds of the exponential functions using exact knowledge of agent dynamic models and the graph topology. The effects of adding state-dependent terms to the trigger functions were not well-addressed by the authors of [37], [38].…”

Section: B Discussion On the Chosen Of Trigger Functionsmentioning

confidence: 99%

Event-Triggered Algorithms for Leader–Follower Consensus of Networked Euler–Lagrange Agents

Liu

Qin

et al. 2019

IEEE Trans. Syst. Man Cybern, Syst.

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This paper proposes three different distributed event-triggered control algorithms to achieve leader-follower consensus for a network of Euler-Lagrange agents. We firstly propose two model-independent algorithms for a subclass of Euler-Lagrange agents without the vector of gravitational potential forces. By model-independent, we mean that each agent can execute its algorithm with no knowledge of the agent selfdynamics. A variable-gain algorithm is employed when the sensing graph is undirected; algorithm parameters are selected in a fully distributed manner with much greater flexibility compared to all previous work concerning event-triggered consensus problems. When the sensing graph is directed, a constant-gain algorithm is employed. The control gains must be centrally designed to exceed several lower bounding inequalities which require limited knowledge of bounds on the matrices describing the agent dynamics, bounds on network topology information and bounds on the initial conditions. When the Euler-Lagrange agents have dynamics which include the vector of gravitational potential forces, an adaptive algorithm is proposed which requires more information about the agent dynamics but can estimate uncertain agent parameters.For each algorithm, a trigger function is proposed to govern the event update times. At each event, the controller is updated, which ensures that the control input is piecewise constant and saves energy resources. We analyse each controllers and trigger function and exclude Zeno behaviour. Extensive simulations show 1) the advantages of our proposed trigger function as compared to those in existing literature, and 2) the effectiveness of our proposed controllers.

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Section: B Discussion On the Chosen Of Trigger Functionsmentioning

confidence: 99%

Event-Triggered Algorithms for Leader–Follower Consensus of Networked Euler–Lagrange Agents

Liu

Qin

et al. 2019

IEEE Trans. Syst. Man Cybern, Syst.

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“…Each generator has a local quadratic generation cost function as (referring to the work of Binetti et al 39 When t ∈ [0, 1000](s), d i is randomly chosen in [40, 50](MW), while t ∈ [1000, 2000](s), d i is randomly chosen in [30,40](MW). The initial value P i (0) is randomly chosen in [5,10](MW), and the initial values ofṖ i (0), i (0), z i (0) are all zeros. Moreover, k = 10 in algorithms (32) and (33).…”

Section: Simulationsmentioning

confidence: 99%

“…For example, Wang et al have studied the distributed optimization problem of disturbed multiagent systems and demonstrated that the average consensus problem is a special case of distributed optimization problems. Besides, Deng et al have discussed the distributed event‐triggered optimization of multiagent systems.…”

Section: Introductionmentioning

confidence: 99%

Distributed optimal resource allocation of second‐order multiagent systems

Deng

Liang

2018

Intl J Robust & Nonlinear

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Summary In this paper, the resource allocation problems of multiagent systems are investigated. Different from the well‐studied resource allocation problems, the dynamics of agents are taken into account in our problem, which results that the problem could not be solved by most of existing resource allocation algorithms. Here, the agents are in the form of second‐order dynamics, which causes the difficulties in designing and analyzing distributed resource allocation algorithms. Based on gradient descent and state feedback, two distributed resource allocation algorithms are proposed to achieve the optimal allocation, and their convergence are analyzed by constructing suitable Lyapunov functions. One of the two algorithms can ensure that the decisions of all agents asymptotically converge to the exact optimal solution, and the other algorithm achieves the exponential convergence. Finally, numerical examples about the economic dispatch problems of power grids are given to verify the effectiveness of the obtained results.

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“…Resource allocation is one of the important optimization problems (see [7]). In particular, various distributed algorithms for resource allocation optimization have been discussed in [1,4,11,20]. For example, [1] considered the network utility maximization problem and proposed a fast distributed dual-based gradient method for solving the problem.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, it is well known that event-triggered strategy, which has drawn much attention (such as [10,17,6,12,21]), provides an effective method in solving control problem with reducing communication cost and computation burden in multi-agent systems for leader-following or leaderless cases. Some results can be found in [2,19,3], where different distributed event-triggered optimization algorithms were presented for different situations. However, to the best of our knowledge, up to now, no result has been obtained for the reduction of communication and measurement in the study of the distributed resource allocation optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Distributed event-triggered algorithm for optimal resource allocation of multi-agent systems

Yu¹,

Deng²,

Zhou³

et al. 2017

Kybernetika

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

Cited by 20 publications

References 25 publications

Event-Triggered Algorithms for Leader–Follower Consensus of Networked Euler–Lagrange Agents

Event-Triggered Algorithms for Leader–Follower Consensus of Networked Euler–Lagrange Agents

Distributed optimal resource allocation of second‐order multiagent systems

Distributed event-triggered algorithm for optimal resource allocation of multi-agent systems

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