Distributed MPC for Tracking and Formation of Homogeneous Multi‐agent System with Time‐Varying Communication Topology

Ding, Baocang; Ge, Liang; Pan, Hongguang; Wang, Peng

doi:10.1002/asjc.1186

Cited by 19 publications

(16 citation statements)

References 34 publications

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“…, M ), two subsystems ( 10) and ( 11) is obtained based on its kinematic model by transformed and dividing processes. 3) Two consensus error systems (19) and (20) are obtained based on the directed graphḠ and the subsystems in 2). 4) For each robot ith i = 1, 2, .…”

Section: General Projection Network Optimizationmentioning

confidence: 99%

“…However, in [15]- [18] and [19], the formation systems are constructed by calculating robots' relative relationships but not in a consensus form. In [20], a homogeneous multi-agent consensus system is controlled through the distributed MPC method with input and state constraints. In [21], MPC is used to control the second-order multi-agent flocking system, the input constraints can be handled.…”

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confidence: 99%

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Leader-Follower Consensus Multi-Robot Formation Control Using Neurodynamic-Optimization-Based Nonlinear Model Predictive Control

Xiao

Chen

2019

IEEE Access

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This paper investigates a nonlinear-model-predictive-control (NMPC)-strategy-based distributed leader-follower consensus multi-robot formation system. The control objective of this system is to design a group of nonholonomic robots to converge into the desired geometric pattern and to track a designed path. A directed graph that specifies communication topology for the formation is given. A leader-follower consensus formation problem based on the mobile robot kinematic model is obtained, which is further reformulated into a constrained nonlinear minimization problem through the NMPC strategy. A general projection neural network (GPNN) is implemented to efficiently derive the optimal control inputs for the robots. The simulation results verify the effectiveness of the proposed formation algorithm.INDEX TERMS Nonholonomic Multi-robot formation, leader-follower consensus system, nonlinear model predictive control (NMPC), graph theory, general projection neural network (GPNN). I. INTRODUCTIONIn recent years, robot formation, which is one of the most important research areas in multi-robot coordination, has become more and more attractive. Many researchers are interested in its application prospects such as surveillance, transportation, mine sweeping, rescue operations, and geographical exploration. Compared to single robot, a team of robots can offer many superiorities on working. The consensus formation, whose objective is to control a group of robots to reach and maintain a designed geometric pattern during moving, is a typical formation scheme. Meanwhile, owing to Brockett's theorem [1], it is hard to directly implement the differentiable, or even continuous, pure state feedback algorithm on the nonholonomic-robot-based distributed consensus formation problem.Generally, there are two control paradigms for robot formation: centralized and distributed. In centralized formation,The associate editor coordinating the review of this manuscript and approving it for publication was Jinpeng Yu.

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Section: General Projection Network Optimizationmentioning

confidence: 99%

mentioning

confidence: 99%

Leader-Follower Consensus Multi-Robot Formation Control Using Neurodynamic-Optimization-Based Nonlinear Model Predictive Control

Xiao

Chen

2019

IEEE Access

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“…In accordance to the agent dynamics, MASs are classified into homogenous and heterogenous types [6,31]. A majority of rich references concerns homogeneous MASs [2,4,19,24,28], where all agents are assumed to have the same time-domain dynamics. However, MASs are heterogeneous among numerous practical applications.…”

Section: Introduction and Problem Formulationmentioning

confidence: 99%

Consensus tracking problem for linear fractional multi-agent systems with initial state error

Luo

Wang

Shen

2020

NAMC

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In this paper, we discuss the consensus tracking problem by introducing two iterative learning control (ILC) protocols (namely, Dα-type and PDα-type) with initial state error for fractional-order homogenous and heterogenous multi-agent systems (MASs), respectively. The initial state of each agent is fixed at the same position away from the desired one for iterations. For both homogenous and heterogenous MASs, the Dα-type ILC rule is first designed and analyzed, and the asymptotical convergence property is carefully derived. Then, an additional P-type component is added to formulate a PDα-type ILC rule, which also guarantees the asymptotical consensus performance. Moreover, it turns out that the PDα-type ILC rule can further adjust the final performance. Two numerical examples are provided to verify the theoretical results.

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“…Model predictive control (MPC) refers to a class of computer control algorithms that utilize an explicit process model to predict the future response of a plant . Due to the advantage of controlling the constrained multivariable processes, MPC has been widely and successfully implemented in the process industries over recent years .…”

Section: Introductionmentioning

confidence: 99%

Economic Optimization and Control Based on Multi Priority Rank RTO and Double Layered MPC

Pan

Zhong

Wang

2018

Asian Journal of Control

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A prevailing hierarchical structure in industrial process optimization and control includes three levels, i.e., a real time optimization (RTO) level, a double layered model predictive control (MPC) level (which is composed of a steady-state target calculation (SSTC) layer and a dynamic control layer), and a distributed control level. In this paper, a multi priority rank RTO algorithm, in which a new variable is introduced to uniformly express the set points, is presented to get optimal set points according to their importance levels. In order to guarantee the feasibility of the dynamic control layer during tracking the steady-state targets calculated in SSTC layer, the region of attraction is added into the SSTC layer as additional constraints, hence, the steady-state targets can be calculated online and transmitted to the dynamic control layer at each instant to guide the state to achieve the steady-state state gradually. The effect of the above methods are illustrated through an example.

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Distributed MPC for Tracking and Formation of Homogeneous Multi‐agent System with Time‐Varying Communication Topology

Cited by 19 publications

References 34 publications

Leader-Follower Consensus Multi-Robot Formation Control Using Neurodynamic-Optimization-Based Nonlinear Model Predictive Control

Leader-Follower Consensus Multi-Robot Formation Control Using Neurodynamic-Optimization-Based Nonlinear Model Predictive Control

Consensus tracking problem for linear fractional multi-agent systems with initial state error

Economic Optimization and Control Based on Multi Priority Rank RTO and Double Layered MPC

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