Distributed Continuous-Time Optimization of Second-Order Multiagent Systems With Nonconvex Input Constraints

Mo, Lipo; Yu, Yongguang; Zhao, Lin; Cao, Xianbing

doi:10.1109/tsmc.2019.2961421

Cited by 19 publications

(18 citation statements)

References 35 publications

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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%

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Constrained consensus over continuous‐time multi‐agent networks with state constraints, non‐convex input constraints and time delays

2020

IET Control Theory & Appl

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This study considers the constrained consensus problem of continuous‐time multi‐agent networks with hypercube state constraints, non‐convex input constraints and non‐uniform delays. It is assumed that each agent can only perceive its own constraint sets, the communication graphs are switching over time and the joint communication graphs are strongly connected. By introducing the time‐varying gains and the constraint operators, a new distributed algorithm is proposed. Then, it is proved that the constraint consensus can be reached under the proposed algorithm by reduction, while the states and the inputs are constrained to stay at the corresponding constraint sets. Finally, simulation examples are given to examine the effectiveness of the proposed results.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Constrained consensus over continuous‐time multi‐agent networks with state constraints, non‐convex input constraints and time delays

2020

IET Control Theory & Appl

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“…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%

Constrained Consensus of Continuous-Time Heterogeneous Multi-Agent Networks with Nonconvex Constraints and Delays

Ren

et al. 2021

J Syst Sci Complex

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This paper studies the continuous-time constrained consensus of heterogeneous multi-agent networks with nonconvex input and velocity constraints, where each agent's dynamic is modeled by a first-order or second-order integrator, the communication delays are assumed to be time-varying, nonuniform, bounded and the communication graph is changing over time. An improved distributed control algorithm with time-varying gains is proposed and the heterogeneous consensus is changed into the homogeneous consensus by a coordination transformation. Then, it is proved that the constrained consensus can be reached by analyzing six connection cases between agents if the joint graph has a directed spanning tree over each bounded interval. Finally, simulations are done to show the correctness of the theorem.

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“…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%

Distributed Non-Convex Model Predictive Control for Non-Cooperative Collision Avoidance of Networked Differential Drive Mobile Robots

Mao

Dai

2022

IEEE Access

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This paper proposes a novel Non-Cooperative Distributed Model Predictive Control (NC-DMPC) strategy for collision avoidance of networked multiple Differential Drive Mobile Robots (multi-DDMRs). This strategy is based on Priority-Based NC-DMPC (PB-NC-DMPC). To avoid the loss of the prediction consistency caused by PB-NC-DMPC, a concept of urgency is introduced to DDMRs in the Networked Control Systems (NCS), and a corresponding priority queue is obtained by sorting these urgency values. Renumbering these priorities in descending order converts the partial order priorities into a topological order, which is a valid sequence for the DDMRs to solve their optimization problems. At last, the Sequential Convex Programming (SCP) is presented to solve the non-convex optimization problem resulting from the collision avoidance constraints. As a special application, the proposed strategy solves the problem of collision avoidance of Multi-DDMRs, compared to the original PB-NC-DMPC, the proposed strategy offer a more effective and safe performance in complex applications.

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Distributed Continuous-Time Optimization of Second-Order Multiagent Systems With Nonconvex Input Constraints

Cited by 19 publications

References 35 publications

Constrained consensus over continuous‐time multi‐agent networks with state constraints, non‐convex input constraints and time delays

Constrained consensus over continuous‐time multi‐agent networks with state constraints, non‐convex input constraints and time delays

Constrained Consensus of Continuous-Time Heterogeneous Multi-Agent Networks with Nonconvex Constraints and Delays

Distributed Non-Convex Model Predictive Control for Non-Cooperative Collision Avoidance of Networked Differential Drive Mobile Robots

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