2021
DOI: 10.1109/tsmc.2019.2961421
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Distributed Continuous-Time Optimization of Second-Order Multiagent Systems With Nonconvex Input Constraints

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Cited by 18 publications
(18 citation statements)
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“…Hence, the analysis methods in [14–17] do not work here and we develop a new analysis method to prove the consensus of continuous‐time networks. In addition, compared with the continuous‐time occasions with no state constraint [18–21], the existence of state constraint leads to non‐smoothness of the closed‐loop system, which makes the methods based on the homogeneous dynamics of each agent in [18–21] be invalid. By discussing the dynamical equations of agents in different position regions and analysing the motions tendency of agents, we overcome these difficulties. Remark 6 Though state constraints were considered for continuous‐time networks in [24–28], their algorithms were just effective for the situation without delay and input constraint.…”
Section: Resultsmentioning
confidence: 99%
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“…Hence, the analysis methods in [14–17] do not work here and we develop a new analysis method to prove the consensus of continuous‐time networks. In addition, compared with the continuous‐time occasions with no state constraint [18–21], the existence of state constraint leads to non‐smoothness of the closed‐loop system, which makes the methods based on the homogeneous dynamics of each agent in [18–21] be invalid. By discussing the dynamical equations of agents in different position regions and analysing the motions tendency of agents, we overcome these difficulties. Remark 6 Though state constraints were considered for continuous‐time networks in [24–28], their algorithms were just effective for the situation without delay and input constraint.…”
Section: Resultsmentioning
confidence: 99%
“…Lin et al [14] introduced a constraint operator to deal with the consensus problem under the non‐convex velocity constraints. Then, this method was extended to the non‐convex input constraint occasions of discrete‐time networks [15–17], continuous‐time networks [18–21]. However, the position states in the above‐mentioned algorithms were assumed to be free.…”
Section: Introductionmentioning
confidence: 99%
“…Compared with [23][24][25][32][33][34], where only the nonconvex input constraints were taken into account, this paper investigates the situation of coexistence of nonconvex input and velocity constraints, which make the algorithms and analysis methods in [23][24][25][32][33][34] don't work on our problem. We improve the distributed algorithm and propose a new analysis method to prove the constrained consensus.…”
Section: Remark 36mentioning
confidence: 99%
“…In [21,22], a constraint operator was introduced to deal with the discrete-time nonconvex input and velocity constrained consensus problems. Then, this idea was extended to solve the continuous-time nonconvex input constrained consensus, optimization, and containment control problems [23][24][25][26] .…”
Section: Introductionmentioning
confidence: 99%
“…However, these methods used a simple robot model, in which kinematics are ignored. In recent decades, the consensus control [17], formation control [18] and flocking control [19] of multi-agent systems have received more and more attention by systems and control community. These problems can be extended to a multi-agent collision avoidance problem which can be formulated as a nonlinear differential game.…”
Section: Introductionmentioning
confidence: 99%